[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Convert this manual workflow into a reproducible checklist for `proteomics_upload`: download files, check row counts, validate required columns, archive the raw input, run the analysis script, save logs, and upload results.

[Assistant]
A reproducible checklist for `proteomics_upload` could look like this:

1. Create a dated run folder.
2. Download or receive the input files.
3. Save raw files in a read-only `raw/` folder.
4. Record file names, sizes, and checksums.
5. Validate row counts and required columns.
6. Run the analysis script with explicit command-line arguments.
7. Save stdout and stderr logs.
8. Write outputs to a versioned `results/` folder.
9. Upload final outputs to the approved destination.
10. Record the software version, run date, and operator.

Example folder layout:

```text
proteomics_upload/
  raw/
  scripts/
  logs/
  results/
  manifests/
```

This structure makes the workflow easier to audit, rerun, and debug.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write an AWS CLI command to sync `./results` to `s3://open-bio-demo-results/raw/fastq`. Include a dry-run version first and a real sync version. Also explain what each important flag does.

[Assistant]
Use the dry-run command first:

```bash
aws s3 sync "./results" "s3://open-bio-demo-results/raw/fastq" \
  --dryrun \
  --only-show-errors
```

After reviewing the dry-run output, run the real sync:

```bash
aws s3 sync "./results" "s3://open-bio-demo-results/raw/fastq" \
  --only-show-errors
```

Explanation:

- `aws s3 sync` copies new or changed files between a local folder and S3.
- `./results` is the local source folder.
- `s3://open-bio-demo-results/raw/fastq` is the destination S3 path.
- `--dryrun` previews what would be copied without uploading anything.
- `--only-show-errors` keeps the output cleaner by hiding routine transfer messages.

aws s3 sync
--dryrun
--only-show-errors
S3


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a Python script that scans a folder for FASTQ files ending in `.fastq.gz` and creates a CSV manifest with sample_id, read_pair, and file_path. Assume filenames look like SAMPLE123_R1.fastq.gz or SAMPLE123_R2.fastq.gz.

[Assistant]
Here is a simple FASTQ manifest builder:

```python
from pathlib import Path
import csv
import re
import sys

FASTQ_PATTERN = re.compile(r"(?P<sample_id>.+)_(?P<read_pair>R[12])\.fastq\.gz$")

def build_manifest(input_dir: str, output_csv: str) -> None:
    input_path = Path(input_dir)

    if not input_path.exists():
        raise FileNotFoundError(f"Input directory does not exist: {input_dir}")

    rows = []

    for fastq in sorted(input_path.rglob("*.fastq.gz")):
        match = FASTQ_PATTERN.match(fastq.name)
        if not match:
            continue

        rows.append({
            "sample_id": match.group("sample_id"),
            "read_pair": match.group("read_pair"),
            "file_path": str(fastq.resolve()),
        })

    with open(output_csv, "w", newline="", encoding="utf-8") as handle:
        writer = csv.DictWriter(handle, fieldnames=["sample_id", "read_pair", "file_path"])
        writer.writeheader()
        writer.writerows(rows)

    print(f"Wrote {len(rows)} FASTQ records to {output_csv}")

if __name__ == "__main__":
    input_dir = sys.argv[1]
    output_csv = sys.argv[2]
    build_manifest(input_dir, output_csv)
```

This script recursively scans folders, extracts sample IDs and read-pair labels, and writes a reproducible manifest.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write an AWS CLI command to sync `./analysis_output` to `s3://open-bio-demo-results/reports/monthly`. Include a dry-run version first and a real sync version. Also explain what each important flag does.

[Assistant]
Use the dry-run command first:

```bash
aws s3 sync "./analysis_output" "s3://open-bio-demo-results/reports/monthly" \
  --dryrun \
  --only-show-errors
```

After reviewing the dry-run output, run the real sync:

```bash
aws s3 sync "./analysis_output" "s3://open-bio-demo-results/reports/monthly" \
  --only-show-errors
```

Explanation:

- `aws s3 sync` copies new or changed files between a local folder and S3.
- `./analysis_output` is the local source folder.
- `s3://open-bio-demo-results/reports/monthly` is the destination S3 path.
- `--dryrun` previews what would be copied without uploading anything.
- `--only-show-errors` keeps the output cleaner by hiding routine transfer messages.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Return a JSON object that summarizes a failed data transfer. The message is: `Transfer failed for sample S-3959 from site CHI04 because the destination S3 prefix was missing.` Use keys: sample_id, site_id, problem, recommended_action.

[Assistant]
```json
{
  "sample_id": "S-3959",
  "site_id": "CHI04",
  "problem": "The transfer failed because the destination S3 prefix was missing.",
  "recommended_action": "Create or correct the destination S3 prefix, then rerun the transfer after confirming the target path."
}
```


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Generate a bash script that checks whether `/mnt/analysis` is mounted. If it is not mounted, print an error and exit with a non-zero status code. If it is mounted, print available disk space.

[Assistant]
Here is a safe bash script:

```bash
#!/usr/bin/env bash
set -euo pipefail

MOUNT_POINT="/mnt/analysis"

if ! mountpoint -q "$MOUNT_POINT"; then
  echo "ERROR: $MOUNT_POINT is not mounted." >&2
  exit 1
fi

echo "Mount point is available: $MOUNT_POINT"
echo "Disk usage:"
df -h "$MOUNT_POINT"
```

Explanation:

- `set -euo pipefail` makes the script fail more safely.
- `mountpoint -q` checks whether the path is actually a mounted filesystem.
- `>&2` sends the error message to standard error.
- `exit 1` signals failure to a scheduler or pipeline.
- `df -h` prints human-readable disk usage.

mountpoint
df -h
exit 1
set -euo pipefail


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Return a JSON object that summarizes a failed data transfer. The message is: `Transfer failed for sample S-2179 from site SEA06 because the destination S3 prefix was missing.` Use keys: sample_id, site_id, problem, recommended_action.

[Assistant]
```json
{
  "sample_id": "S-2179",
  "site_id": "SEA06",
  "problem": "The transfer failed because the destination S3 prefix was missing.",
  "recommended_action": "Create or correct the destination S3 prefix, then rerun the transfer after confirming the target path."
}
```


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Convert this manual workflow into a reproducible checklist for `imaging_metadata`: download files, check row counts, validate required columns, archive the raw input, run the analysis script, save logs, and upload results.

[Assistant]
A reproducible checklist for `imaging_metadata` could look like this:

1. Create a dated run folder.
2. Download or receive the input files.
3. Save raw files in a read-only `raw/` folder.
4. Record file names, sizes, and checksums.
5. Validate row counts and required columns.
6. Run the analysis script with explicit command-line arguments.
7. Save stdout and stderr logs.
8. Write outputs to a versioned `results/` folder.
9. Upload final outputs to the approved destination.
10. Record the software version, run date, and operator.

Example folder layout:

```text
imaging_metadata/
  raw/
  scripts/
  logs/
  results/
  manifests/
```

This structure makes the workflow easier to audit, rerun, and debug.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a pandas function that groups a DataFrame by `run_id` and reports duplicate `file_path` values within each group. Return a DataFrame of problems.

[Assistant]
Here is a pandas function for that check:

```python
import pandas as pd

def find_duplicates_within_group(
    df: pd.DataFrame,
    group_col: str = "run_id",
    value_col: str = "file_path",
) -> pd.DataFrame:
    required = {group_col, value_col}
    missing = required - set(df.columns)
    if missing:
        raise ValueError(f"Missing required columns: {sorted(missing)}")

    duplicated_rows = df[df.duplicated(subset=[group_col, value_col], keep=False)].copy()

    if duplicated_rows.empty:
        return pd.DataFrame(columns=[group_col, value_col, "duplicate_count"])

    summary = (
        duplicated_rows
        .groupby([group_col, value_col])
        .size()
        .reset_index(name="duplicate_count")
        .sort_values(["duplicate_count", group_col], ascending=[False, True])
    )

    return summary
```

This returns only problematic group/value combinations, which makes it easier to review duplicate sample or file records.

pandas groupby duplicated duplicate_count


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a pandas function that groups a DataFrame by `run_id` and reports duplicate `sample_id` values within each group. Return a DataFrame of problems.

[Assistant]
Here is a pandas function for that check:

```python
import pandas as pd

def find_duplicates_within_group(
    df: pd.DataFrame,
    group_col: str = "run_id",
    value_col: str = "sample_id",
) -> pd.DataFrame:
    required = {group_col, value_col}
    missing = required - set(df.columns)
    if missing:
        raise ValueError(f"Missing required columns: {sorted(missing)}")

    duplicated_rows = df[df.duplicated(subset=[group_col, value_col], keep=False)].copy()

    if duplicated_rows.empty:
        return pd.DataFrame(columns=[group_col, value_col, "duplicate_count"])

    summary = (
        duplicated_rows
        .groupby([group_col, value_col])
        .size()
        .reset_index(name="duplicate_count")
        .sort_values(["duplicate_count", group_col], ascending=[False, True])
    )

    return summary
```

This returns only problematic group/value combinations, which makes it easier to review duplicate sample or file records.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Convert this manual workflow into a reproducible checklist for `imaging_metadata`: download files, check row counts, validate required columns, archive the raw input, run the analysis script, save logs, and upload results.

[Assistant]
A reproducible checklist for `imaging_metadata` could look like this:

1. Create a dated run folder.
2. Download or receive the input files.
3. Save raw files in a read-only `raw/` folder.
4. Record file names, sizes, and checksums.
5. Validate row counts and required columns.
6. Run the analysis script with explicit command-line arguments.
7. Save stdout and stderr logs.
8. Write outputs to a versioned `results/` folder.
9. Upload final outputs to the approved destination.
10. Record the software version, run date, and operator.

Example folder layout:

```text
imaging_metadata/
  raw/
  scripts/
  logs/
  results/
  manifests/
```

This structure makes the workflow easier to audit, rerun, and debug.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Generate a bash script that checks whether `/mnt/data` is mounted. If it is not mounted, print an error and exit with a non-zero status code. If it is mounted, print available disk space.

[Assistant]
Here is a safe bash script:

```bash
#!/usr/bin/env bash
set -euo pipefail

MOUNT_POINT="/mnt/data"

if ! mountpoint -q "$MOUNT_POINT"; then
  echo "ERROR: $MOUNT_POINT is not mounted." >&2
  exit 1
fi

echo "Mount point is available: $MOUNT_POINT"
echo "Disk usage:"
df -h "$MOUNT_POINT"
```

Explanation:

- `set -euo pipefail` makes the script fail more safely.
- `mountpoint -q` checks whether the path is actually a mounted filesystem.
- `>&2` sends the error message to standard error.
- `exit 1` signals failure to a scheduler or pipeline.
- `df -h` prints human-readable disk usage.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a Python script that scans a folder for FASTQ files ending in `.fastq.gz` and creates a CSV manifest with sample_id, read_pair, and file_path. Assume filenames look like SAMPLE123_R1.fastq.gz or SAMPLE123_R2.fastq.gz.

[Assistant]
Here is a simple FASTQ manifest builder:

```python
from pathlib import Path
import csv
import re
import sys

FASTQ_PATTERN = re.compile(r"(?P<sample_id>.+)_(?P<read_pair>R[12])\.fastq\.gz$")

def build_manifest(input_dir: str, output_csv: str) -> None:
    input_path = Path(input_dir)

    if not input_path.exists():
        raise FileNotFoundError(f"Input directory does not exist: {input_dir}")

    rows = []

    for fastq in sorted(input_path.rglob("*.fastq.gz")):
        match = FASTQ_PATTERN.match(fastq.name)
        if not match:
            continue

        rows.append({
            "sample_id": match.group("sample_id"),
            "read_pair": match.group("read_pair"),
            "file_path": str(fastq.resolve()),
        })

    with open(output_csv, "w", newline="", encoding="utf-8") as handle:
        writer = csv.DictWriter(handle, fieldnames=["sample_id", "read_pair", "file_path"])
        writer.writeheader()
        writer.writerows(rows)

    print(f"Wrote {len(rows)} FASTQ records to {output_csv}")

if __name__ == "__main__":
    input_dir = sys.argv[1]
    output_csv = sys.argv[2]
    build_manifest(input_dir, output_csv)
```

This script recursively scans folders, extracts sample IDs and read-pair labels, and writes a reproducible manifest.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Generate a bash script that checks whether `/mnt/results` is mounted. If it is not mounted, print an error and exit with a non-zero status code. If it is mounted, print available disk space.

[Assistant]
Here is a safe bash script:

```bash
#!/usr/bin/env bash
set -euo pipefail

MOUNT_POINT="/mnt/results"

if ! mountpoint -q "$MOUNT_POINT"; then
  echo "ERROR: $MOUNT_POINT is not mounted." >&2
  exit 1
fi

echo "Mount point is available: $MOUNT_POINT"
echo "Disk usage:"
df -h "$MOUNT_POINT"
```

Explanation:

- `set -euo pipefail` makes the script fail more safely.
- `mountpoint -q` checks whether the path is actually a mounted filesystem.
- `>&2` sends the error message to standard error.
- `exit 1` signals failure to a scheduler or pipeline.
- `df -h` prints human-readable disk usage.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a Python script that validates a CSV file named `manifest.csv`. The required columns are: subject_id, file_path, site_id. The script should report missing columns, empty values in required columns, duplicate sample IDs if sample_id exists, and print a short summary.

[Assistant]
Below is a practical validation script for `manifest.csv`.

```python
import sys
import pandas as pd

REQUIRED_COLUMNS = ['subject_id', 'file_path', 'site_id']

def validate_csv(path: str) -> int:
    errors = []

    try:
        df = pd.read_csv(path)
    except FileNotFoundError:
        print(f"ERROR: File not found: {path}")
        return 1
    except Exception as exc:
        print(f"ERROR: Could not read CSV: {exc}")
        return 1

    missing_columns = [col for col in REQUIRED_COLUMNS if col not in df.columns]
    if missing_columns:
        errors.append(f"Missing required columns: {missing_columns}")

    for col in REQUIRED_COLUMNS:
        if col in df.columns:
            empty_count = df[col].isna().sum() + (df[col].astype(str).str.strip() == "").sum()
            if empty_count > 0:
                errors.append(f"Column '{col}' has {empty_count} empty values")

    if "sample_id" in df.columns:
        duplicate_count = df["sample_id"].duplicated().sum()
        if duplicate_count > 0:
            errors.append(f"Found {duplicate_count} duplicate sample_id values")

    print("CSV validation summary")
    print(f"Rows: {len(df)}")
    print(f"Columns: {len(df.columns)}")

    if errors:
        print("\nValidation failed:")
        for error in errors:
            print(f"- {error}")
        return 1

    print("Validation passed.")
    return 0

if __name__ == "__main__":
    csv_path = sys.argv[1] if len(sys.argv) > 1 else "manifest.csv"
    raise SystemExit(validate_csv(csv_path))
```

This script exits with status code `0` when the file passes validation and `1` when it finds problems.

pandas
required columns
duplicate
sample_id
Validation


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a Python script that scans a folder for FASTQ files ending in `.fastq.gz` and creates a CSV manifest with sample_id, read_pair, and file_path. Assume filenames look like SAMPLE123_R1.fastq.gz or SAMPLE123_R2.fastq.gz.

[Assistant]
Here is a simple FASTQ manifest builder:

```python
from pathlib import Path
import csv
import re
import sys

FASTQ_PATTERN = re.compile(r"(?P<sample_id>.+)_(?P<read_pair>R[12])\.fastq\.gz$")

def build_manifest(input_dir: str, output_csv: str) -> None:
    input_path = Path(input_dir)

    if not input_path.exists():
        raise FileNotFoundError(f"Input directory does not exist: {input_dir}")

    rows = []

    for fastq in sorted(input_path.rglob("*.fastq.gz")):
        match = FASTQ_PATTERN.match(fastq.name)
        if not match:
            continue

        rows.append({
            "sample_id": match.group("sample_id"),
            "read_pair": match.group("read_pair"),
            "file_path": str(fastq.resolve()),
        })

    with open(output_csv, "w", newline="", encoding="utf-8") as handle:
        writer = csv.DictWriter(handle, fieldnames=["sample_id", "read_pair", "file_path"])
        writer.writeheader()
        writer.writerows(rows)

    print(f"Wrote {len(rows)} FASTQ records to {output_csv}")

if __name__ == "__main__":
    input_dir = sys.argv[1]
    output_csv = sys.argv[2]
    build_manifest(input_dir, output_csv)
```

This script recursively scans folders, extracts sample IDs and read-pair labels, and writes a reproducible manifest.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a Python script that validates a CSV file named `sequencing_manifest.csv`. The required columns are: sample_id, site_id, subject_id. The script should report missing columns, empty values in required columns, duplicate sample IDs if sample_id exists, and print a short summary.

[Assistant]
Below is a practical validation script for `sequencing_manifest.csv`.

```python
import sys
import pandas as pd

REQUIRED_COLUMNS = ['sample_id', 'site_id', 'subject_id']

def validate_csv(path: str) -> int:
    errors = []

    try:
        df = pd.read_csv(path)
    except FileNotFoundError:
        print(f"ERROR: File not found: {path}")
        return 1
    except Exception as exc:
        print(f"ERROR: Could not read CSV: {exc}")
        return 1

    missing_columns = [col for col in REQUIRED_COLUMNS if col not in df.columns]
    if missing_columns:
        errors.append(f"Missing required columns: {missing_columns}")

    for col in REQUIRED_COLUMNS:
        if col in df.columns:
            empty_count = df[col].isna().sum() + (df[col].astype(str).str.strip() == "").sum()
            if empty_count > 0:
                errors.append(f"Column '{col}' has {empty_count} empty values")

    if "sample_id" in df.columns:
        duplicate_count = df["sample_id"].duplicated().sum()
        if duplicate_count > 0:
            errors.append(f"Found {duplicate_count} duplicate sample_id values")

    print("CSV validation summary")
    print(f"Rows: {len(df)}")
    print(f"Columns: {len(df.columns)}")

    if errors:
        print("\nValidation failed:")
        for error in errors:
            print(f"- {error}")
        return 1

    print("Validation passed.")
    return 0

if __name__ == "__main__":
    csv_path = sys.argv[1] if len(sys.argv) > 1 else "sequencing_manifest.csv"
    raise SystemExit(validate_csv(csv_path))
```

This script exits with status code `0` when the file passes validation and `1` when it finds problems.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Convert this manual workflow into a reproducible checklist for `sample_manifest`: download files, check row counts, validate required columns, archive the raw input, run the analysis script, save logs, and upload results.

[Assistant]
A reproducible checklist for `sample_manifest` could look like this:

1. Create a dated run folder.
2. Download or receive the input files.
3. Save raw files in a read-only `raw/` folder.
4. Record file names, sizes, and checksums.
5. Validate row counts and required columns.
6. Run the analysis script with explicit command-line arguments.
7. Save stdout and stderr logs.
8. Write outputs to a versioned `results/` folder.
9. Upload final outputs to the approved destination.
10. Record the software version, run date, and operator.

Example folder layout:

```text
sample_manifest/
  raw/
  scripts/
  logs/
  results/
  manifests/
```

This structure makes the workflow easier to audit, rerun, and debug.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Generate a bash script that checks whether `/data/project` is mounted. If it is not mounted, print an error and exit with a non-zero status code. If it is mounted, print available disk space.

[Assistant]
Here is a safe bash script:

```bash
#!/usr/bin/env bash
set -euo pipefail

MOUNT_POINT="/data/project"

if ! mountpoint -q "$MOUNT_POINT"; then
  echo "ERROR: $MOUNT_POINT is not mounted." >&2
  exit 1
fi

echo "Mount point is available: $MOUNT_POINT"
echo "Disk usage:"
df -h "$MOUNT_POINT"
```

Explanation:

- `set -euo pipefail` makes the script fail more safely.
- `mountpoint -q` checks whether the path is actually a mounted filesystem.
- `>&2` sends the error message to standard error.
- `exit 1` signals failure to a scheduler or pipeline.
- `df -h` prints human-readable disk usage.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Return a JSON object that summarizes a failed data transfer. The message is: `Transfer failed for sample S-9679 from site BOS02 because the destination S3 prefix was missing.` Use keys: sample_id, site_id, problem, recommended_action.

[Assistant]
```json
{
  "sample_id": "S-9679",
  "site_id": "BOS02",
  "problem": "The transfer failed because the destination S3 prefix was missing.",
  "recommended_action": "Create or correct the destination S3 prefix, then rerun the transfer after confirming the target path."
}
```


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Convert this manual workflow into a reproducible checklist for `imaging_metadata`: download files, check row counts, validate required columns, archive the raw input, run the analysis script, save logs, and upload results.

[Assistant]
A reproducible checklist for `imaging_metadata` could look like this:

1. Create a dated run folder.
2. Download or receive the input files.
3. Save raw files in a read-only `raw/` folder.
4. Record file names, sizes, and checksums.
5. Validate row counts and required columns.
6. Run the analysis script with explicit command-line arguments.
7. Save stdout and stderr logs.
8. Write outputs to a versioned `results/` folder.
9. Upload final outputs to the approved destination.
10. Record the software version, run date, and operator.

Example folder layout:

```text
imaging_metadata/
  raw/
  scripts/
  logs/
  results/
  manifests/
```

This structure makes the workflow easier to audit, rerun, and debug.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a Python script that validates a CSV file named `sequencing_manifest.csv`. The required columns are: run_id, assay, fastq_1. The script should report missing columns, empty values in required columns, duplicate sample IDs if sample_id exists, and print a short summary.

[Assistant]
Below is a practical validation script for `sequencing_manifest.csv`.

```python
import sys
import pandas as pd

REQUIRED_COLUMNS = ['run_id', 'assay', 'fastq_1']

def validate_csv(path: str) -> int:
    errors = []

    try:
        df = pd.read_csv(path)
    except FileNotFoundError:
        print(f"ERROR: File not found: {path}")
        return 1
    except Exception as exc:
        print(f"ERROR: Could not read CSV: {exc}")
        return 1

    missing_columns = [col for col in REQUIRED_COLUMNS if col not in df.columns]
    if missing_columns:
        errors.append(f"Missing required columns: {missing_columns}")

    for col in REQUIRED_COLUMNS:
        if col in df.columns:
            empty_count = df[col].isna().sum() + (df[col].astype(str).str.strip() == "").sum()
            if empty_count > 0:
                errors.append(f"Column '{col}' has {empty_count} empty values")

    if "sample_id" in df.columns:
        duplicate_count = df["sample_id"].duplicated().sum()
        if duplicate_count > 0:
            errors.append(f"Found {duplicate_count} duplicate sample_id values")

    print("CSV validation summary")
    print(f"Rows: {len(df)}")
    print(f"Columns: {len(df.columns)}")

    if errors:
        print("\nValidation failed:")
        for error in errors:
            print(f"- {error}")
        return 1

    print("Validation passed.")
    return 0

if __name__ == "__main__":
    csv_path = sys.argv[1] if len(sys.argv) > 1 else "sequencing_manifest.csv"
    raise SystemExit(validate_csv(csv_path))
```

This script exits with status code `0` when the file passes validation and `1` when it finds problems.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a Python script that validates a CSV file named `samples.csv`. The required columns are: visit_id, collection_date, subject_id. The script should report missing columns, empty values in required columns, duplicate sample IDs if sample_id exists, and print a short summary.

[Assistant]
Below is a practical validation script for `samples.csv`.

```python
import sys
import pandas as pd

REQUIRED_COLUMNS = ['visit_id', 'collection_date', 'subject_id']

def validate_csv(path: str) -> int:
    errors = []

    try:
        df = pd.read_csv(path)
    except FileNotFoundError:
        print(f"ERROR: File not found: {path}")
        return 1
    except Exception as exc:
        print(f"ERROR: Could not read CSV: {exc}")
        return 1

    missing_columns = [col for col in REQUIRED_COLUMNS if col not in df.columns]
    if missing_columns:
        errors.append(f"Missing required columns: {missing_columns}")

    for col in REQUIRED_COLUMNS:
        if col in df.columns:
            empty_count = df[col].isna().sum() + (df[col].astype(str).str.strip() == "").sum()
            if empty_count > 0:
                errors.append(f"Column '{col}' has {empty_count} empty values")

    if "sample_id" in df.columns:
        duplicate_count = df["sample_id"].duplicated().sum()
        if duplicate_count > 0:
            errors.append(f"Found {duplicate_count} duplicate sample_id values")

    print("CSV validation summary")
    print(f"Rows: {len(df)}")
    print(f"Columns: {len(df.columns)}")

    if errors:
        print("\nValidation failed:")
        for error in errors:
            print(f"- {error}")
        return 1

    print("Validation passed.")
    return 0

if __name__ == "__main__":
    csv_path = sys.argv[1] if len(sys.argv) > 1 else "samples.csv"
    raise SystemExit(validate_csv(csv_path))
```

This script exits with status code `0` when the file passes validation and `1` when it finds problems.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a Python script that scans a folder for FASTQ files ending in `.fastq.gz` and creates a CSV manifest with sample_id, read_pair, and file_path. Assume filenames look like SAMPLE123_R1.fastq.gz or SAMPLE123_R2.fastq.gz.

[Assistant]
Here is a simple FASTQ manifest builder:

```python
from pathlib import Path
import csv
import re
import sys

FASTQ_PATTERN = re.compile(r"(?P<sample_id>.+)_(?P<read_pair>R[12])\.fastq\.gz$")

def build_manifest(input_dir: str, output_csv: str) -> None:
    input_path = Path(input_dir)

    if not input_path.exists():
        raise FileNotFoundError(f"Input directory does not exist: {input_dir}")

    rows = []

    for fastq in sorted(input_path.rglob("*.fastq.gz")):
        match = FASTQ_PATTERN.match(fastq.name)
        if not match:
            continue

        rows.append({
            "sample_id": match.group("sample_id"),
            "read_pair": match.group("read_pair"),
            "file_path": str(fastq.resolve()),
        })

    with open(output_csv, "w", newline="", encoding="utf-8") as handle:
        writer = csv.DictWriter(handle, fieldnames=["sample_id", "read_pair", "file_path"])
        writer.writeheader()
        writer.writerows(rows)

    print(f"Wrote {len(rows)} FASTQ records to {output_csv}")

if __name__ == "__main__":
    input_dir = sys.argv[1]
    output_csv = sys.argv[2]
    build_manifest(input_dir, output_csv)
```

This script recursively scans folders, extracts sample IDs and read-pair labels, and writes a reproducible manifest.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Generate a bash script that checks whether `/mnt/data` is mounted. If it is not mounted, print an error and exit with a non-zero status code. If it is mounted, print available disk space.

[Assistant]
Here is a safe bash script:

```bash
#!/usr/bin/env bash
set -euo pipefail

MOUNT_POINT="/mnt/data"

if ! mountpoint -q "$MOUNT_POINT"; then
  echo "ERROR: $MOUNT_POINT is not mounted." >&2
  exit 1
fi

echo "Mount point is available: $MOUNT_POINT"
echo "Disk usage:"
df -h "$MOUNT_POINT"
```

Explanation:

- `set -euo pipefail` makes the script fail more safely.
- `mountpoint -q` checks whether the path is actually a mounted filesystem.
- `>&2` sends the error message to standard error.
- `exit 1` signals failure to a scheduler or pipeline.
- `df -h` prints human-readable disk usage.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a Python script that validates a CSV file named `metadata.csv`. The required columns are: assay, fastq_1, sample_id. The script should report missing columns, empty values in required columns, duplicate sample IDs if sample_id exists, and print a short summary.

[Assistant]
Below is a practical validation script for `metadata.csv`.

```python
import sys
import pandas as pd

REQUIRED_COLUMNS = ['assay', 'fastq_1', 'sample_id']

def validate_csv(path: str) -> int:
    errors = []

    try:
        df = pd.read_csv(path)
    except FileNotFoundError:
        print(f"ERROR: File not found: {path}")
        return 1
    except Exception as exc:
        print(f"ERROR: Could not read CSV: {exc}")
        return 1

    missing_columns = [col for col in REQUIRED_COLUMNS if col not in df.columns]
    if missing_columns:
        errors.append(f"Missing required columns: {missing_columns}")

    for col in REQUIRED_COLUMNS:
        if col in df.columns:
            empty_count = df[col].isna().sum() + (df[col].astype(str).str.strip() == "").sum()
            if empty_count > 0:
                errors.append(f"Column '{col}' has {empty_count} empty values")

    if "sample_id" in df.columns:
        duplicate_count = df["sample_id"].duplicated().sum()
        if duplicate_count > 0:
            errors.append(f"Found {duplicate_count} duplicate sample_id values")

    print("CSV validation summary")
    print(f"Rows: {len(df)}")
    print(f"Columns: {len(df.columns)}")

    if errors:
        print("\nValidation failed:")
        for error in errors:
            print(f"- {error}")
        return 1

    print("Validation passed.")
    return 0

if __name__ == "__main__":
    csv_path = sys.argv[1] if len(sys.argv) > 1 else "metadata.csv"
    raise SystemExit(validate_csv(csv_path))
```

This script exits with status code `0` when the file passes validation and `1` when it finds problems.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Generate a bash script that checks whether `/mnt/data` is mounted. If it is not mounted, print an error and exit with a non-zero status code. If it is mounted, print available disk space.

[Assistant]
Here is a safe bash script:

```bash
#!/usr/bin/env bash
set -euo pipefail

MOUNT_POINT="/mnt/data"

if ! mountpoint -q "$MOUNT_POINT"; then
  echo "ERROR: $MOUNT_POINT is not mounted." >&2
  exit 1
fi

echo "Mount point is available: $MOUNT_POINT"
echo "Disk usage:"
df -h "$MOUNT_POINT"
```

Explanation:

- `set -euo pipefail` makes the script fail more safely.
- `mountpoint -q` checks whether the path is actually a mounted filesystem.
- `>&2` sends the error message to standard error.
- `exit 1` signals failure to a scheduler or pipeline.
- `df -h` prints human-readable disk usage.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a Python script that validates a CSV file named `metadata.csv`. The required columns are: fastq_1, run_id, sample_id. The script should report missing columns, empty values in required columns, duplicate sample IDs if sample_id exists, and print a short summary.

[Assistant]
Below is a practical validation script for `metadata.csv`.

```python
import sys
import pandas as pd

REQUIRED_COLUMNS = ['fastq_1', 'run_id', 'sample_id']

def validate_csv(path: str) -> int:
    errors = []

    try:
        df = pd.read_csv(path)
    except FileNotFoundError:
        print(f"ERROR: File not found: {path}")
        return 1
    except Exception as exc:
        print(f"ERROR: Could not read CSV: {exc}")
        return 1

    missing_columns = [col for col in REQUIRED_COLUMNS if col not in df.columns]
    if missing_columns:
        errors.append(f"Missing required columns: {missing_columns}")

    for col in REQUIRED_COLUMNS:
        if col in df.columns:
            empty_count = df[col].isna().sum() + (df[col].astype(str).str.strip() == "").sum()
            if empty_count > 0:
                errors.append(f"Column '{col}' has {empty_count} empty values")

    if "sample_id" in df.columns:
        duplicate_count = df["sample_id"].duplicated().sum()
        if duplicate_count > 0:
            errors.append(f"Found {duplicate_count} duplicate sample_id values")

    print("CSV validation summary")
    print(f"Rows: {len(df)}")
    print(f"Columns: {len(df.columns)}")

    if errors:
        print("\nValidation failed:")
        for error in errors:
            print(f"- {error}")
        return 1

    print("Validation passed.")
    return 0

if __name__ == "__main__":
    csv_path = sys.argv[1] if len(sys.argv) > 1 else "metadata.csv"
    raise SystemExit(validate_csv(csv_path))
```

This script exits with status code `0` when the file passes validation and `1` when it finds problems.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Convert this manual workflow into a reproducible checklist for `variant_calling`: download files, check row counts, validate required columns, archive the raw input, run the analysis script, save logs, and upload results.

[Assistant]
A reproducible checklist for `variant_calling` could look like this:

1. Create a dated run folder.
2. Download or receive the input files.
3. Save raw files in a read-only `raw/` folder.
4. Record file names, sizes, and checksums.
5. Validate row counts and required columns.
6. Run the analysis script with explicit command-line arguments.
7. Save stdout and stderr logs.
8. Write outputs to a versioned `results/` folder.
9. Upload final outputs to the approved destination.
10. Record the software version, run date, and operator.

Example folder layout:

```text
variant_calling/
  raw/
  scripts/
  logs/
  results/
  manifests/
```

This structure makes the workflow easier to audit, rerun, and debug.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a pandas function that groups a DataFrame by `site_id` and reports duplicate `file_path` values within each group. Return a DataFrame of problems.

[Assistant]
Here is a pandas function for that check:

```python
import pandas as pd

def find_duplicates_within_group(
    df: pd.DataFrame,
    group_col: str = "site_id",
    value_col: str = "file_path",
) -> pd.DataFrame:
    required = {group_col, value_col}
    missing = required - set(df.columns)
    if missing:
        raise ValueError(f"Missing required columns: {sorted(missing)}")

    duplicated_rows = df[df.duplicated(subset=[group_col, value_col], keep=False)].copy()

    if duplicated_rows.empty:
        return pd.DataFrame(columns=[group_col, value_col, "duplicate_count"])

    summary = (
        duplicated_rows
        .groupby([group_col, value_col])
        .size()
        .reset_index(name="duplicate_count")
        .sort_values(["duplicate_count", group_col], ascending=[False, True])
    )

    return summary
```

This returns only problematic group/value combinations, which makes it easier to review duplicate sample or file records.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Return a JSON object that summarizes a failed data transfer. The message is: `Transfer failed for sample S-2380 from site SEA06 because the destination S3 prefix was missing.` Use keys: sample_id, site_id, problem, recommended_action.

[Assistant]
```json
{
  "sample_id": "S-2380",
  "site_id": "SEA06",
  "problem": "The transfer failed because the destination S3 prefix was missing.",
  "recommended_action": "Create or correct the destination S3 prefix, then rerun the transfer after confirming the target path."
}
```


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a Python script that validates a CSV file named `samples.csv`. The required columns are: run_id, sample_id, fastq_2. The script should report missing columns, empty values in required columns, duplicate sample IDs if sample_id exists, and print a short summary.

[Assistant]
Below is a practical validation script for `samples.csv`.

```python
import sys
import pandas as pd

REQUIRED_COLUMNS = ['run_id', 'sample_id', 'fastq_2']

def validate_csv(path: str) -> int:
    errors = []

    try:
        df = pd.read_csv(path)
    except FileNotFoundError:
        print(f"ERROR: File not found: {path}")
        return 1
    except Exception as exc:
        print(f"ERROR: Could not read CSV: {exc}")
        return 1

    missing_columns = [col for col in REQUIRED_COLUMNS if col not in df.columns]
    if missing_columns:
        errors.append(f"Missing required columns: {missing_columns}")

    for col in REQUIRED_COLUMNS:
        if col in df.columns:
            empty_count = df[col].isna().sum() + (df[col].astype(str).str.strip() == "").sum()
            if empty_count > 0:
                errors.append(f"Column '{col}' has {empty_count} empty values")

    if "sample_id" in df.columns:
        duplicate_count = df["sample_id"].duplicated().sum()
        if duplicate_count > 0:
            errors.append(f"Found {duplicate_count} duplicate sample_id values")

    print("CSV validation summary")
    print(f"Rows: {len(df)}")
    print(f"Columns: {len(df.columns)}")

    if errors:
        print("\nValidation failed:")
        for error in errors:
            print(f"- {error}")
        return 1

    print("Validation passed.")
    return 0

if __name__ == "__main__":
    csv_path = sys.argv[1] if len(sys.argv) > 1 else "samples.csv"
    raise SystemExit(validate_csv(csv_path))
```

This script exits with status code `0` when the file passes validation and `1` when it finds problems.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write an AWS CLI command to sync `./results` to `s3://research-demo-landing-zone/metadata/manifests`. Include a dry-run version first and a real sync version. Also explain what each important flag does.

[Assistant]
Use the dry-run command first:

```bash
aws s3 sync "./results" "s3://research-demo-landing-zone/metadata/manifests" \
  --dryrun \
  --only-show-errors
```

After reviewing the dry-run output, run the real sync:

```bash
aws s3 sync "./results" "s3://research-demo-landing-zone/metadata/manifests" \
  --only-show-errors
```

Explanation:

- `aws s3 sync` copies new or changed files between a local folder and S3.
- `./results` is the local source folder.
- `s3://research-demo-landing-zone/metadata/manifests` is the destination S3 path.
- `--dryrun` previews what would be copied without uploading anything.
- `--only-show-errors` keeps the output cleaner by hiding routine transfer messages.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Generate a bash script that checks whether `/mnt/analysis` is mounted. If it is not mounted, print an error and exit with a non-zero status code. If it is mounted, print available disk space.

[Assistant]
Here is a safe bash script:

```bash
#!/usr/bin/env bash
set -euo pipefail

MOUNT_POINT="/mnt/analysis"

if ! mountpoint -q "$MOUNT_POINT"; then
  echo "ERROR: $MOUNT_POINT is not mounted." >&2
  exit 1
fi

echo "Mount point is available: $MOUNT_POINT"
echo "Disk usage:"
df -h "$MOUNT_POINT"
```

Explanation:

- `set -euo pipefail` makes the script fail more safely.
- `mountpoint -q` checks whether the path is actually a mounted filesystem.
- `>&2` sends the error message to standard error.
- `exit 1` signals failure to a scheduler or pipeline.
- `df -h` prints human-readable disk usage.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Convert this manual workflow into a reproducible checklist for `variant_calling`: download files, check row counts, validate required columns, archive the raw input, run the analysis script, save logs, and upload results.

[Assistant]
A reproducible checklist for `variant_calling` could look like this:

1. Create a dated run folder.
2. Download or receive the input files.
3. Save raw files in a read-only `raw/` folder.
4. Record file names, sizes, and checksums.
5. Validate row counts and required columns.
6. Run the analysis script with explicit command-line arguments.
7. Save stdout and stderr logs.
8. Write outputs to a versioned `results/` folder.
9. Upload final outputs to the approved destination.
10. Record the software version, run date, and operator.

Example folder layout:

```text
variant_calling/
  raw/
  scripts/
  logs/
  results/
  manifests/
```

This structure makes the workflow easier to audit, rerun, and debug.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a Python script that validates a CSV file named `samples.csv`. The required columns are: visit_id, subject_id, specimen_type. The script should report missing columns, empty values in required columns, duplicate sample IDs if sample_id exists, and print a short summary.

[Assistant]
Below is a practical validation script for `samples.csv`.

```python
import sys
import pandas as pd

REQUIRED_COLUMNS = ['visit_id', 'subject_id', 'specimen_type']

def validate_csv(path: str) -> int:
    errors = []

    try:
        df = pd.read_csv(path)
    except FileNotFoundError:
        print(f"ERROR: File not found: {path}")
        return 1
    except Exception as exc:
        print(f"ERROR: Could not read CSV: {exc}")
        return 1

    missing_columns = [col for col in REQUIRED_COLUMNS if col not in df.columns]
    if missing_columns:
        errors.append(f"Missing required columns: {missing_columns}")

    for col in REQUIRED_COLUMNS:
        if col in df.columns:
            empty_count = df[col].isna().sum() + (df[col].astype(str).str.strip() == "").sum()
            if empty_count > 0:
                errors.append(f"Column '{col}' has {empty_count} empty values")

    if "sample_id" in df.columns:
        duplicate_count = df["sample_id"].duplicated().sum()
        if duplicate_count > 0:
            errors.append(f"Found {duplicate_count} duplicate sample_id values")

    print("CSV validation summary")
    print(f"Rows: {len(df)}")
    print(f"Columns: {len(df.columns)}")

    if errors:
        print("\nValidation failed:")
        for error in errors:
            print(f"- {error}")
        return 1

    print("Validation passed.")
    return 0

if __name__ == "__main__":
    csv_path = sys.argv[1] if len(sys.argv) > 1 else "samples.csv"
    raise SystemExit(validate_csv(csv_path))
```

This script exits with status code `0` when the file passes validation and `1` when it finds problems.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Return a JSON object that summarizes a failed data transfer. The message is: `Transfer failed for sample S-9088 from site CHI04 because the destination S3 prefix was missing.` Use keys: sample_id, site_id, problem, recommended_action.

[Assistant]
```json
{
  "sample_id": "S-9088",
  "site_id": "CHI04",
  "problem": "The transfer failed because the destination S3 prefix was missing.",
  "recommended_action": "Create or correct the destination S3 prefix, then rerun the transfer after confirming the target path."
}
```


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a Python script that scans a folder for FASTQ files ending in `.fastq.gz` and creates a CSV manifest with sample_id, read_pair, and file_path. Assume filenames look like SAMPLE123_R1.fastq.gz or SAMPLE123_R2.fastq.gz.

[Assistant]
Here is a simple FASTQ manifest builder:

```python
from pathlib import Path
import csv
import re
import sys

FASTQ_PATTERN = re.compile(r"(?P<sample_id>.+)_(?P<read_pair>R[12])\.fastq\.gz$")

def build_manifest(input_dir: str, output_csv: str) -> None:
    input_path = Path(input_dir)

    if not input_path.exists():
        raise FileNotFoundError(f"Input directory does not exist: {input_dir}")

    rows = []

    for fastq in sorted(input_path.rglob("*.fastq.gz")):
        match = FASTQ_PATTERN.match(fastq.name)
        if not match:
            continue

        rows.append({
            "sample_id": match.group("sample_id"),
            "read_pair": match.group("read_pair"),
            "file_path": str(fastq.resolve()),
        })

    with open(output_csv, "w", newline="", encoding="utf-8") as handle:
        writer = csv.DictWriter(handle, fieldnames=["sample_id", "read_pair", "file_path"])
        writer.writeheader()
        writer.writerows(rows)

    print(f"Wrote {len(rows)} FASTQ records to {output_csv}")

if __name__ == "__main__":
    input_dir = sys.argv[1]
    output_csv = sys.argv[2]
    build_manifest(input_dir, output_csv)
```

This script recursively scans folders, extracts sample IDs and read-pair labels, and writes a reproducible manifest.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Return a JSON object that summarizes a failed data transfer. The message is: `Transfer failed for sample S-6523 from site CHI04 because the destination S3 prefix was missing.` Use keys: sample_id, site_id, problem, recommended_action.

[Assistant]
```json
{
  "sample_id": "S-6523",
  "site_id": "CHI04",
  "problem": "The transfer failed because the destination S3 prefix was missing.",
  "recommended_action": "Create or correct the destination S3 prefix, then rerun the transfer after confirming the target path."
}
```


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a pandas function that groups a DataFrame by `assay` and reports duplicate `file_path` values within each group. Return a DataFrame of problems.

[Assistant]
Here is a pandas function for that check:

```python
import pandas as pd

def find_duplicates_within_group(
    df: pd.DataFrame,
    group_col: str = "assay",
    value_col: str = "file_path",
) -> pd.DataFrame:
    required = {group_col, value_col}
    missing = required - set(df.columns)
    if missing:
        raise ValueError(f"Missing required columns: {sorted(missing)}")

    duplicated_rows = df[df.duplicated(subset=[group_col, value_col], keep=False)].copy()

    if duplicated_rows.empty:
        return pd.DataFrame(columns=[group_col, value_col, "duplicate_count"])

    summary = (
        duplicated_rows
        .groupby([group_col, value_col])
        .size()
        .reset_index(name="duplicate_count")
        .sort_values(["duplicate_count", group_col], ascending=[False, True])
    )

    return summary
```

This returns only problematic group/value combinations, which makes it easier to review duplicate sample or file records.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a Python script that validates a CSV file named `metadata.csv`. The required columns are: fastq_1, assay, sample_id. The script should report missing columns, empty values in required columns, duplicate sample IDs if sample_id exists, and print a short summary.

[Assistant]
Below is a practical validation script for `metadata.csv`.

```python
import sys
import pandas as pd

REQUIRED_COLUMNS = ['fastq_1', 'assay', 'sample_id']

def validate_csv(path: str) -> int:
    errors = []

    try:
        df = pd.read_csv(path)
    except FileNotFoundError:
        print(f"ERROR: File not found: {path}")
        return 1
    except Exception as exc:
        print(f"ERROR: Could not read CSV: {exc}")
        return 1

    missing_columns = [col for col in REQUIRED_COLUMNS if col not in df.columns]
    if missing_columns:
        errors.append(f"Missing required columns: {missing_columns}")

    for col in REQUIRED_COLUMNS:
        if col in df.columns:
            empty_count = df[col].isna().sum() + (df[col].astype(str).str.strip() == "").sum()
            if empty_count > 0:
                errors.append(f"Column '{col}' has {empty_count} empty values")

    if "sample_id" in df.columns:
        duplicate_count = df["sample_id"].duplicated().sum()
        if duplicate_count > 0:
            errors.append(f"Found {duplicate_count} duplicate sample_id values")

    print("CSV validation summary")
    print(f"Rows: {len(df)}")
    print(f"Columns: {len(df.columns)}")

    if errors:
        print("\nValidation failed:")
        for error in errors:
            print(f"- {error}")
        return 1

    print("Validation passed.")
    return 0

if __name__ == "__main__":
    csv_path = sys.argv[1] if len(sys.argv) > 1 else "metadata.csv"
    raise SystemExit(validate_csv(csv_path))
```

This script exits with status code `0` when the file passes validation and `1` when it finds problems.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Convert this manual workflow into a reproducible checklist for `imaging_metadata`: download files, check row counts, validate required columns, archive the raw input, run the analysis script, save logs, and upload results.

[Assistant]
A reproducible checklist for `imaging_metadata` could look like this:

1. Create a dated run folder.
2. Download or receive the input files.
3. Save raw files in a read-only `raw/` folder.
4. Record file names, sizes, and checksums.
5. Validate row counts and required columns.
6. Run the analysis script with explicit command-line arguments.
7. Save stdout and stderr logs.
8. Write outputs to a versioned `results/` folder.
9. Upload final outputs to the approved destination.
10. Record the software version, run date, and operator.

Example folder layout:

```text
imaging_metadata/
  raw/
  scripts/
  logs/
  results/
  manifests/
```

This structure makes the workflow easier to audit, rerun, and debug.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a pandas function that groups a DataFrame by `subject_id` and reports duplicate `sample_id` values within each group. Return a DataFrame of problems.

[Assistant]
Here is a pandas function for that check:

```python
import pandas as pd

def find_duplicates_within_group(
    df: pd.DataFrame,
    group_col: str = "subject_id",
    value_col: str = "sample_id",
) -> pd.DataFrame:
    required = {group_col, value_col}
    missing = required - set(df.columns)
    if missing:
        raise ValueError(f"Missing required columns: {sorted(missing)}")

    duplicated_rows = df[df.duplicated(subset=[group_col, value_col], keep=False)].copy()

    if duplicated_rows.empty:
        return pd.DataFrame(columns=[group_col, value_col, "duplicate_count"])

    summary = (
        duplicated_rows
        .groupby([group_col, value_col])
        .size()
        .reset_index(name="duplicate_count")
        .sort_values(["duplicate_count", group_col], ascending=[False, True])
    )

    return summary
```

This returns only problematic group/value combinations, which makes it easier to review duplicate sample or file records.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Generate a bash script that checks whether `/mnt/analysis` is mounted. If it is not mounted, print an error and exit with a non-zero status code. If it is mounted, print available disk space.

[Assistant]
Here is a safe bash script:

```bash
#!/usr/bin/env bash
set -euo pipefail

MOUNT_POINT="/mnt/analysis"

if ! mountpoint -q "$MOUNT_POINT"; then
  echo "ERROR: $MOUNT_POINT is not mounted." >&2
  exit 1
fi

echo "Mount point is available: $MOUNT_POINT"
echo "Disk usage:"
df -h "$MOUNT_POINT"
```

Explanation:

- `set -euo pipefail` makes the script fail more safely.
- `mountpoint -q` checks whether the path is actually a mounted filesystem.
- `>&2` sends the error message to standard error.
- `exit 1` signals failure to a scheduler or pipeline.
- `df -h` prints human-readable disk usage.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Return a JSON object that summarizes a failed data transfer. The message is: `Transfer failed for sample S-9863 from site PHL03 because the destination S3 prefix was missing.` Use keys: sample_id, site_id, problem, recommended_action.

[Assistant]
```json
{
  "sample_id": "S-9863",
  "site_id": "PHL03",
  "problem": "The transfer failed because the destination S3 prefix was missing.",
  "recommended_action": "Create or correct the destination S3 prefix, then rerun the transfer after confirming the target path."
}
```


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a pandas function that groups a DataFrame by `site_id` and reports duplicate `specimen_id` values within each group. Return a DataFrame of problems.

[Assistant]
Here is a pandas function for that check:

```python
import pandas as pd

def find_duplicates_within_group(
    df: pd.DataFrame,
    group_col: str = "site_id",
    value_col: str = "specimen_id",
) -> pd.DataFrame:
    required = {group_col, value_col}
    missing = required - set(df.columns)
    if missing:
        raise ValueError(f"Missing required columns: {sorted(missing)}")

    duplicated_rows = df[df.duplicated(subset=[group_col, value_col], keep=False)].copy()

    if duplicated_rows.empty:
        return pd.DataFrame(columns=[group_col, value_col, "duplicate_count"])

    summary = (
        duplicated_rows
        .groupby([group_col, value_col])
        .size()
        .reset_index(name="duplicate_count")
        .sort_values(["duplicate_count", group_col], ascending=[False, True])
    )

    return summary
```

This returns only problematic group/value combinations, which makes it easier to review duplicate sample or file records.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Return a JSON object that summarizes a failed data transfer. The message is: `Transfer failed for sample S-8259 from site SFO05 because the destination S3 prefix was missing.` Use keys: sample_id, site_id, problem, recommended_action.

[Assistant]
```json
{
  "sample_id": "S-8259",
  "site_id": "SFO05",
  "problem": "The transfer failed because the destination S3 prefix was missing.",
  "recommended_action": "Create or correct the destination S3 prefix, then rerun the transfer after confirming the target path."
}
```


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a Python script that validates a CSV file named `samples.csv`. The required columns are: assay, fastq_1, sample_id. The script should report missing columns, empty values in required columns, duplicate sample IDs if sample_id exists, and print a short summary.

[Assistant]
Below is a practical validation script for `samples.csv`.

```python
import sys
import pandas as pd

REQUIRED_COLUMNS = ['assay', 'fastq_1', 'sample_id']

def validate_csv(path: str) -> int:
    errors = []

    try:
        df = pd.read_csv(path)
    except FileNotFoundError:
        print(f"ERROR: File not found: {path}")
        return 1
    except Exception as exc:
        print(f"ERROR: Could not read CSV: {exc}")
        return 1

    missing_columns = [col for col in REQUIRED_COLUMNS if col not in df.columns]
    if missing_columns:
        errors.append(f"Missing required columns: {missing_columns}")

    for col in REQUIRED_COLUMNS:
        if col in df.columns:
            empty_count = df[col].isna().sum() + (df[col].astype(str).str.strip() == "").sum()
            if empty_count > 0:
                errors.append(f"Column '{col}' has {empty_count} empty values")

    if "sample_id" in df.columns:
        duplicate_count = df["sample_id"].duplicated().sum()
        if duplicate_count > 0:
            errors.append(f"Found {duplicate_count} duplicate sample_id values")

    print("CSV validation summary")
    print(f"Rows: {len(df)}")
    print(f"Columns: {len(df.columns)}")

    if errors:
        print("\nValidation failed:")
        for error in errors:
            print(f"- {error}")
        return 1

    print("Validation passed.")
    return 0

if __name__ == "__main__":
    csv_path = sys.argv[1] if len(sys.argv) > 1 else "samples.csv"
    raise SystemExit(validate_csv(csv_path))
```

This script exits with status code `0` when the file passes validation and `1` when it finds problems.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a Python script that validates a CSV file named `manifest.csv`. The required columns are: run_id, sample_id, assay. The script should report missing columns, empty values in required columns, duplicate sample IDs if sample_id exists, and print a short summary.

[Assistant]
Below is a practical validation script for `manifest.csv`.

```python
import sys
import pandas as pd

REQUIRED_COLUMNS = ['run_id', 'sample_id', 'assay']

def validate_csv(path: str) -> int:
    errors = []

    try:
        df = pd.read_csv(path)
    except FileNotFoundError:
        print(f"ERROR: File not found: {path}")
        return 1
    except Exception as exc:
        print(f"ERROR: Could not read CSV: {exc}")
        return 1

    missing_columns = [col for col in REQUIRED_COLUMNS if col not in df.columns]
    if missing_columns:
        errors.append(f"Missing required columns: {missing_columns}")

    for col in REQUIRED_COLUMNS:
        if col in df.columns:
            empty_count = df[col].isna().sum() + (df[col].astype(str).str.strip() == "").sum()
            if empty_count > 0:
                errors.append(f"Column '{col}' has {empty_count} empty values")

    if "sample_id" in df.columns:
        duplicate_count = df["sample_id"].duplicated().sum()
        if duplicate_count > 0:
            errors.append(f"Found {duplicate_count} duplicate sample_id values")

    print("CSV validation summary")
    print(f"Rows: {len(df)}")
    print(f"Columns: {len(df.columns)}")

    if errors:
        print("\nValidation failed:")
        for error in errors:
            print(f"- {error}")
        return 1

    print("Validation passed.")
    return 0

if __name__ == "__main__":
    csv_path = sys.argv[1] if len(sys.argv) > 1 else "manifest.csv"
    raise SystemExit(validate_csv(csv_path))
```

This script exits with status code `0` when the file passes validation and `1` when it finds problems.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a pandas function that groups a DataFrame by `site_id` and reports duplicate `file_path` values within each group. Return a DataFrame of problems.

[Assistant]
Here is a pandas function for that check:

```python
import pandas as pd

def find_duplicates_within_group(
    df: pd.DataFrame,
    group_col: str = "site_id",
    value_col: str = "file_path",
) -> pd.DataFrame:
    required = {group_col, value_col}
    missing = required - set(df.columns)
    if missing:
        raise ValueError(f"Missing required columns: {sorted(missing)}")

    duplicated_rows = df[df.duplicated(subset=[group_col, value_col], keep=False)].copy()

    if duplicated_rows.empty:
        return pd.DataFrame(columns=[group_col, value_col, "duplicate_count"])

    summary = (
        duplicated_rows
        .groupby([group_col, value_col])
        .size()
        .reset_index(name="duplicate_count")
        .sort_values(["duplicate_count", group_col], ascending=[False, True])
    )

    return summary
```

This returns only problematic group/value combinations, which makes it easier to review duplicate sample or file records.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a Python script that validates a CSV file named `sequencing_manifest.csv`. The required columns are: subject_id, sample_id, file_path. The script should report missing columns, empty values in required columns, duplicate sample IDs if sample_id exists, and print a short summary.

[Assistant]
Below is a practical validation script for `sequencing_manifest.csv`.

```python
import sys
import pandas as pd

REQUIRED_COLUMNS = ['subject_id', 'sample_id', 'file_path']

def validate_csv(path: str) -> int:
    errors = []

    try:
        df = pd.read_csv(path)
    except FileNotFoundError:
        print(f"ERROR: File not found: {path}")
        return 1
    except Exception as exc:
        print(f"ERROR: Could not read CSV: {exc}")
        return 1

    missing_columns = [col for col in REQUIRED_COLUMNS if col not in df.columns]
    if missing_columns:
        errors.append(f"Missing required columns: {missing_columns}")

    for col in REQUIRED_COLUMNS:
        if col in df.columns:
            empty_count = df[col].isna().sum() + (df[col].astype(str).str.strip() == "").sum()
            if empty_count > 0:
                errors.append(f"Column '{col}' has {empty_count} empty values")

    if "sample_id" in df.columns:
        duplicate_count = df["sample_id"].duplicated().sum()
        if duplicate_count > 0:
            errors.append(f"Found {duplicate_count} duplicate sample_id values")

    print("CSV validation summary")
    print(f"Rows: {len(df)}")
    print(f"Columns: {len(df.columns)}")

    if errors:
        print("\nValidation failed:")
        for error in errors:
            print(f"- {error}")
        return 1

    print("Validation passed.")
    return 0

if __name__ == "__main__":
    csv_path = sys.argv[1] if len(sys.argv) > 1 else "sequencing_manifest.csv"
    raise SystemExit(validate_csv(csv_path))
```

This script exits with status code `0` when the file passes validation and `1` when it finds problems.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Generate a bash script that checks whether `/mnt/analysis` is mounted. If it is not mounted, print an error and exit with a non-zero status code. If it is mounted, print available disk space.

[Assistant]
Here is a safe bash script:

```bash
#!/usr/bin/env bash
set -euo pipefail

MOUNT_POINT="/mnt/analysis"

if ! mountpoint -q "$MOUNT_POINT"; then
  echo "ERROR: $MOUNT_POINT is not mounted." >&2
  exit 1
fi

echo "Mount point is available: $MOUNT_POINT"
echo "Disk usage:"
df -h "$MOUNT_POINT"
```

Explanation:

- `set -euo pipefail` makes the script fail more safely.
- `mountpoint -q` checks whether the path is actually a mounted filesystem.
- `>&2` sends the error message to standard error.
- `exit 1` signals failure to a scheduler or pipeline.
- `df -h` prints human-readable disk usage.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a Python script that scans a folder for FASTQ files ending in `.fastq.gz` and creates a CSV manifest with sample_id, read_pair, and file_path. Assume filenames look like SAMPLE123_R1.fastq.gz or SAMPLE123_R2.fastq.gz.

[Assistant]
Here is a simple FASTQ manifest builder:

```python
from pathlib import Path
import csv
import re
import sys

FASTQ_PATTERN = re.compile(r"(?P<sample_id>.+)_(?P<read_pair>R[12])\.fastq\.gz$")

def build_manifest(input_dir: str, output_csv: str) -> None:
    input_path = Path(input_dir)

    if not input_path.exists():
        raise FileNotFoundError(f"Input directory does not exist: {input_dir}")

    rows = []

    for fastq in sorted(input_path.rglob("*.fastq.gz")):
        match = FASTQ_PATTERN.match(fastq.name)
        if not match:
            continue

        rows.append({
            "sample_id": match.group("sample_id"),
            "read_pair": match.group("read_pair"),
            "file_path": str(fastq.resolve()),
        })

    with open(output_csv, "w", newline="", encoding="utf-8") as handle:
        writer = csv.DictWriter(handle, fieldnames=["sample_id", "read_pair", "file_path"])
        writer.writeheader()
        writer.writerows(rows)

    print(f"Wrote {len(rows)} FASTQ records to {output_csv}")

if __name__ == "__main__":
    input_dir = sys.argv[1]
    output_csv = sys.argv[2]
    build_manifest(input_dir, output_csv)
```

This script recursively scans folders, extracts sample IDs and read-pair labels, and writes a reproducible manifest.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a Python script that validates a CSV file named `metadata.csv`. The required columns are: sample_id, site_id, subject_id. The script should report missing columns, empty values in required columns, duplicate sample IDs if sample_id exists, and print a short summary.

[Assistant]
Below is a practical validation script for `metadata.csv`.

```python
import sys
import pandas as pd

REQUIRED_COLUMNS = ['sample_id', 'site_id', 'subject_id']

def validate_csv(path: str) -> int:
    errors = []

    try:
        df = pd.read_csv(path)
    except FileNotFoundError:
        print(f"ERROR: File not found: {path}")
        return 1
    except Exception as exc:
        print(f"ERROR: Could not read CSV: {exc}")
        return 1

    missing_columns = [col for col in REQUIRED_COLUMNS if col not in df.columns]
    if missing_columns:
        errors.append(f"Missing required columns: {missing_columns}")

    for col in REQUIRED_COLUMNS:
        if col in df.columns:
            empty_count = df[col].isna().sum() + (df[col].astype(str).str.strip() == "").sum()
            if empty_count > 0:
                errors.append(f"Column '{col}' has {empty_count} empty values")

    if "sample_id" in df.columns:
        duplicate_count = df["sample_id"].duplicated().sum()
        if duplicate_count > 0:
            errors.append(f"Found {duplicate_count} duplicate sample_id values")

    print("CSV validation summary")
    print(f"Rows: {len(df)}")
    print(f"Columns: {len(df.columns)}")

    if errors:
        print("\nValidation failed:")
        for error in errors:
            print(f"- {error}")
        return 1

    print("Validation passed.")
    return 0

if __name__ == "__main__":
    csv_path = sys.argv[1] if len(sys.argv) > 1 else "metadata.csv"
    raise SystemExit(validate_csv(csv_path))
```

This script exits with status code `0` when the file passes validation and `1` when it finds problems.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write an AWS CLI command to sync `./qc_reports` to `s3://research-demo-landing-zone/reports/monthly`. Include a dry-run version first and a real sync version. Also explain what each important flag does.

[Assistant]
Use the dry-run command first:

```bash
aws s3 sync "./qc_reports" "s3://research-demo-landing-zone/reports/monthly" \
  --dryrun \
  --only-show-errors
```

After reviewing the dry-run output, run the real sync:

```bash
aws s3 sync "./qc_reports" "s3://research-demo-landing-zone/reports/monthly" \
  --only-show-errors
```

Explanation:

- `aws s3 sync` copies new or changed files between a local folder and S3.
- `./qc_reports` is the local source folder.
- `s3://research-demo-landing-zone/reports/monthly` is the destination S3 path.
- `--dryrun` previews what would be copied without uploading anything.
- `--only-show-errors` keeps the output cleaner by hiding routine transfer messages.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Convert this manual workflow into a reproducible checklist for `rna_seq_qc`: download files, check row counts, validate required columns, archive the raw input, run the analysis script, save logs, and upload results.

[Assistant]
A reproducible checklist for `rna_seq_qc` could look like this:

1. Create a dated run folder.
2. Download or receive the input files.
3. Save raw files in a read-only `raw/` folder.
4. Record file names, sizes, and checksums.
5. Validate row counts and required columns.
6. Run the analysis script with explicit command-line arguments.
7. Save stdout and stderr logs.
8. Write outputs to a versioned `results/` folder.
9. Upload final outputs to the approved destination.
10. Record the software version, run date, and operator.

Example folder layout:

```text
rna_seq_qc/
  raw/
  scripts/
  logs/
  results/
  manifests/
```

This structure makes the workflow easier to audit, rerun, and debug.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a Python script that validates a CSV file named `manifest.csv`. The required columns are: subject_id, sample_id, site_id. The script should report missing columns, empty values in required columns, duplicate sample IDs if sample_id exists, and print a short summary.

[Assistant]
Below is a practical validation script for `manifest.csv`.

```python
import sys
import pandas as pd

REQUIRED_COLUMNS = ['subject_id', 'sample_id', 'site_id']

def validate_csv(path: str) -> int:
    errors = []

    try:
        df = pd.read_csv(path)
    except FileNotFoundError:
        print(f"ERROR: File not found: {path}")
        return 1
    except Exception as exc:
        print(f"ERROR: Could not read CSV: {exc}")
        return 1

    missing_columns = [col for col in REQUIRED_COLUMNS if col not in df.columns]
    if missing_columns:
        errors.append(f"Missing required columns: {missing_columns}")

    for col in REQUIRED_COLUMNS:
        if col in df.columns:
            empty_count = df[col].isna().sum() + (df[col].astype(str).str.strip() == "").sum()
            if empty_count > 0:
                errors.append(f"Column '{col}' has {empty_count} empty values")

    if "sample_id" in df.columns:
        duplicate_count = df["sample_id"].duplicated().sum()
        if duplicate_count > 0:
            errors.append(f"Found {duplicate_count} duplicate sample_id values")

    print("CSV validation summary")
    print(f"Rows: {len(df)}")
    print(f"Columns: {len(df.columns)}")

    if errors:
        print("\nValidation failed:")
        for error in errors:
            print(f"- {error}")
        return 1

    print("Validation passed.")
    return 0

if __name__ == "__main__":
    csv_path = sys.argv[1] if len(sys.argv) > 1 else "manifest.csv"
    raise SystemExit(validate_csv(csv_path))
```

This script exits with status code `0` when the file passes validation and `1` when it finds problems.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Explain this cron expression in plain English: `30 23 * * 5`. Also give one example of a data-engineering task that might use it.

[Assistant]
The cron expression is:

```text
30 23 * * 5
```

Cron fields are interpreted as:

```text
minute hour day-of-month month day-of-week
```

For this expression:

- minute: `30`
- hour: `23`
- day of month: `*`
- month: `*`
- day of week: `5`

A data-engineering use case would be scheduling a recurring job that checks whether expected files arrived, validates a manifest, or syncs completed analysis results to object storage.

Example command entry:

```cron
30 23 * * 5 /usr/local/bin/run_manifest_validation.sh >> /var/log/manifest_validation.log 2>&1
```

The `>>` appends logs to a file, and `2>&1` sends error output to the same log.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a pandas function that groups a DataFrame by `assay` and reports duplicate `specimen_id` values within each group. Return a DataFrame of problems.

[Assistant]
Here is a pandas function for that check:

```python
import pandas as pd

def find_duplicates_within_group(
    df: pd.DataFrame,
    group_col: str = "assay",
    value_col: str = "specimen_id",
) -> pd.DataFrame:
    required = {group_col, value_col}
    missing = required - set(df.columns)
    if missing:
        raise ValueError(f"Missing required columns: {sorted(missing)}")

    duplicated_rows = df[df.duplicated(subset=[group_col, value_col], keep=False)].copy()

    if duplicated_rows.empty:
        return pd.DataFrame(columns=[group_col, value_col, "duplicate_count"])

    summary = (
        duplicated_rows
        .groupby([group_col, value_col])
        .size()
        .reset_index(name="duplicate_count")
        .sort_values(["duplicate_count", group_col], ascending=[False, True])
    )

    return summary
```

This returns only problematic group/value combinations, which makes it easier to review duplicate sample or file records.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a Python script that scans a folder for FASTQ files ending in `.fastq.gz` and creates a CSV manifest with sample_id, read_pair, and file_path. Assume filenames look like SAMPLE123_R1.fastq.gz or SAMPLE123_R2.fastq.gz.

[Assistant]
Here is a simple FASTQ manifest builder:

```python
from pathlib import Path
import csv
import re
import sys

FASTQ_PATTERN = re.compile(r"(?P<sample_id>.+)_(?P<read_pair>R[12])\.fastq\.gz$")

def build_manifest(input_dir: str, output_csv: str) -> None:
    input_path = Path(input_dir)

    if not input_path.exists():
        raise FileNotFoundError(f"Input directory does not exist: {input_dir}")

    rows = []

    for fastq in sorted(input_path.rglob("*.fastq.gz")):
        match = FASTQ_PATTERN.match(fastq.name)
        if not match:
            continue

        rows.append({
            "sample_id": match.group("sample_id"),
            "read_pair": match.group("read_pair"),
            "file_path": str(fastq.resolve()),
        })

    with open(output_csv, "w", newline="", encoding="utf-8") as handle:
        writer = csv.DictWriter(handle, fieldnames=["sample_id", "read_pair", "file_path"])
        writer.writeheader()
        writer.writerows(rows)

    print(f"Wrote {len(rows)} FASTQ records to {output_csv}")

if __name__ == "__main__":
    input_dir = sys.argv[1]
    output_csv = sys.argv[2]
    build_manifest(input_dir, output_csv)
```

This script recursively scans folders, extracts sample IDs and read-pair labels, and writes a reproducible manifest.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a Python script that validates a CSV file named `manifest.csv`. The required columns are: subject_id, site_id, sample_id. The script should report missing columns, empty values in required columns, duplicate sample IDs if sample_id exists, and print a short summary.

[Assistant]
Below is a practical validation script for `manifest.csv`.

```python
import sys
import pandas as pd

REQUIRED_COLUMNS = ['subject_id', 'site_id', 'sample_id']

def validate_csv(path: str) -> int:
    errors = []

    try:
        df = pd.read_csv(path)
    except FileNotFoundError:
        print(f"ERROR: File not found: {path}")
        return 1
    except Exception as exc:
        print(f"ERROR: Could not read CSV: {exc}")
        return 1

    missing_columns = [col for col in REQUIRED_COLUMNS if col not in df.columns]
    if missing_columns:
        errors.append(f"Missing required columns: {missing_columns}")

    for col in REQUIRED_COLUMNS:
        if col in df.columns:
            empty_count = df[col].isna().sum() + (df[col].astype(str).str.strip() == "").sum()
            if empty_count > 0:
                errors.append(f"Column '{col}' has {empty_count} empty values")

    if "sample_id" in df.columns:
        duplicate_count = df["sample_id"].duplicated().sum()
        if duplicate_count > 0:
            errors.append(f"Found {duplicate_count} duplicate sample_id values")

    print("CSV validation summary")
    print(f"Rows: {len(df)}")
    print(f"Columns: {len(df.columns)}")

    if errors:
        print("\nValidation failed:")
        for error in errors:
            print(f"- {error}")
        return 1

    print("Validation passed.")
    return 0

if __name__ == "__main__":
    csv_path = sys.argv[1] if len(sys.argv) > 1 else "manifest.csv"
    raise SystemExit(validate_csv(csv_path))
```

This script exits with status code `0` when the file passes validation and `1` when it finds problems.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Convert this manual workflow into a reproducible checklist for `imaging_metadata`: download files, check row counts, validate required columns, archive the raw input, run the analysis script, save logs, and upload results.

[Assistant]
A reproducible checklist for `imaging_metadata` could look like this:

1. Create a dated run folder.
2. Download or receive the input files.
3. Save raw files in a read-only `raw/` folder.
4. Record file names, sizes, and checksums.
5. Validate row counts and required columns.
6. Run the analysis script with explicit command-line arguments.
7. Save stdout and stderr logs.
8. Write outputs to a versioned `results/` folder.
9. Upload final outputs to the approved destination.
10. Record the software version, run date, and operator.

Example folder layout:

```text
imaging_metadata/
  raw/
  scripts/
  logs/
  results/
  manifests/
```

This structure makes the workflow easier to audit, rerun, and debug.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Convert this manual workflow into a reproducible checklist for `proteomics_upload`: download files, check row counts, validate required columns, archive the raw input, run the analysis script, save logs, and upload results.

[Assistant]
A reproducible checklist for `proteomics_upload` could look like this:

1. Create a dated run folder.
2. Download or receive the input files.
3. Save raw files in a read-only `raw/` folder.
4. Record file names, sizes, and checksums.
5. Validate row counts and required columns.
6. Run the analysis script with explicit command-line arguments.
7. Save stdout and stderr logs.
8. Write outputs to a versioned `results/` folder.
9. Upload final outputs to the approved destination.
10. Record the software version, run date, and operator.

Example folder layout:

```text
proteomics_upload/
  raw/
  scripts/
  logs/
  results/
  manifests/
```

This structure makes the workflow easier to audit, rerun, and debug.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Write a Python script that validates a CSV file named `sequencing_manifest.csv`. The required columns are: subject_id, specimen_type, collection_date. The script should report missing columns, empty values in required columns, duplicate sample IDs if sample_id exists, and print a short summary.

[Assistant]
Below is a practical validation script for `sequencing_manifest.csv`.

```python
import sys
import pandas as pd

REQUIRED_COLUMNS = ['subject_id', 'specimen_type', 'collection_date']

def validate_csv(path: str) -> int:
    errors = []

    try:
        df = pd.read_csv(path)
    except FileNotFoundError:
        print(f"ERROR: File not found: {path}")
        return 1
    except Exception as exc:
        print(f"ERROR: Could not read CSV: {exc}")
        return 1

    missing_columns = [col for col in REQUIRED_COLUMNS if col not in df.columns]
    if missing_columns:
        errors.append(f"Missing required columns: {missing_columns}")

    for col in REQUIRED_COLUMNS:
        if col in df.columns:
            empty_count = df[col].isna().sum() + (df[col].astype(str).str.strip() == "").sum()
            if empty_count > 0:
                errors.append(f"Column '{col}' has {empty_count} empty values")

    if "sample_id" in df.columns:
        duplicate_count = df["sample_id"].duplicated().sum()
        if duplicate_count > 0:
            errors.append(f"Found {duplicate_count} duplicate sample_id values")

    print("CSV validation summary")
    print(f"Rows: {len(df)}")
    print(f"Columns: {len(df.columns)}")

    if errors:
        print("\nValidation failed:")
        for error in errors:
            print(f"- {error}")
        return 1

    print("Validation passed.")
    return 0

if __name__ == "__main__":
    csv_path = sys.argv[1] if len(sys.argv) > 1 else "sequencing_manifest.csv"
    raise SystemExit(validate_csv(csv_path))
```

This script exits with status code `0` when the file passes validation and `1` when it finds problems.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Explain this cron expression in plain English: `0 6 1 * *`. Also give one example of a data-engineering task that might use it.

[Assistant]
The cron expression is:

```text
0 6 1 * *
```

Cron fields are interpreted as:

```text
minute hour day-of-month month day-of-week
```

For this expression:

- minute: `0`
- hour: `6`
- day of month: `1`
- month: `*`
- day of week: `*`

A data-engineering use case would be scheduling a recurring job that checks whether expected files arrived, validates a manifest, or syncs completed analysis results to object storage.

Example command entry:

```cron
0 6 1 * * /usr/local/bin/run_manifest_validation.sh >> /var/log/manifest_validation.log 2>&1
```

The `>>` appends logs to a file, and `2>&1` sends error output to the same log.


[System] You are a careful bioinformatics and data-engineering coding assistant. You help with Python, pandas, bash, AWS CLI, cron, file validation, scientific data organization, and reproducible workflows. When you write code, prefer clear comments, safe defaults, and practical error handling.

[User] Explain this cron expression in plain English: `*/15 * * * *`. Also give one example of a data-engineering task that might use it.

[Assistant]
The cron expression is:

```text
*/15 * * * *
```

Cron fields are interpreted as:

```text
minute hour day-of-month month day-of-week
```

For this expression:

- minute: `*/15`
- hour: `*`
- day of month: `*`
- month: `*`
- day of week: `*`

A data-engineering use case would be scheduling a recurring job that checks whether expected files arrived, validates a manifest, or syncs completed analysis results to object storage.

Example command entry:

```cron
*/15 * * * * /usr/local/bin/run_manifest_validation.sh >> /var/log/manifest_validation.log 2>&1
```

The `>>` appends logs to a file, and `2>&1` sends error output to the same log.