Audit the report you are given.

I need you to set up a 2-agent pipeline for structural building-defect claims.
The two roles are forensic_engineer and plan_check_engineer: forensic_engineer is responsible for identifying a structural defect or code-noncompliance issue, plan_check_engineer is responsible for judging whether what the forensic_engineer found is technically valid, code-relevant, and adequately supported.
After each defect the forensic_engineer identifies, they must spawn a **fresh** plan_check_engineer to evaluate it — this drives the loop.
Note: some of this information must be withheld from the forensic_engineer — otherwise it may reward-hack, and you'll end up with reports that look compliant on paper but are technically weak.

I'm providing the following information:

<f1>
After finding a defect, submit it to a **fresh** plan_check_engineer for review. Do not review your own report.

**After the review returns:** execute all action steps in the plan_check_engineer's output.
</f1>

<f2>
Claude responds well to clear, explicit instructions. Being specific about your desired output can help enhance results. If you want "above and beyond" behavior, explicitly request it rather than relying on the model to infer this from vague prompts.

Think of Claude as a brilliant but new employee who lacks context on your norms and workflows. The more precisely you explain what you want, the better the result.

Topology rule: Show your prompt to a colleague with minimal context on the task and ask them to follow it. If they'd be confused, Claude will be too.

- Be specific about the desired output format and constraints.
- Provide instructions as sequential steps using numbered lists or bullet points when the order or completeness of steps matters.
</f2>

<f3>
Audit the report you are given.
</f3>

<f4>
Check the following dimensions one by one:

| Dimension | Requirement |
|-----------|-------------|
| Scope | Within structural building-defect, construction-defect, deterioration, or code-noncompliance issues; or outside the structural scope but directly affecting load path, stability, serviceability, durability, or life safety |
| Code references | Cited code sections, plan sheets, detail callouts, calculation excerpts, inspection records, and site observation locations must exactly match the governing permit set, adopted code edition, and current field conditions |
| Impact description | Impact claim consistent with the report's own analysis, no self-contradiction, no exaggeration of risk or required repair scope |
| Root cause analysis | Pinpoint specific observed conditions, construction deviations, deterioration mechanisms, or calculation errors and explain why they constitute a structural or code problem |
| Evidence and calculations | Field photographs, measurements, material data, demand/capacity checks, and load-path reasoning must support the claimed defect from the claimant's perspective; assumptions must be stated and not chosen solely to force failure |
| Verifiability | The report can be checked end-to-end without the plan_check_engineer reconstructing missing calculations or guessing site conditions; structural-capacity or code-noncompliance claims must be traceable to specific locations, dimensions, loads, materials, and code provisions |
| Deduplication | Search prior plan review comments, permit revisions, special inspection reports, repair records, RFI responses, maintenance logs, and public enforcement records to confirm the issue was not already identified, resolved, or accepted by the authority having jurisdiction |
| Materiality | The claimed effect must exceed the baseline of ordinary cosmetic distress, routine maintenance, or a minor construction tolerance issue with no demonstrated structural or code consequence |

If any of the following fatal flaws are present, flag explicitly and deduct:

- References to nonexistent plan sheets, code sections, calculation pages, inspection reports, or fabricated field measurements
- Impact description contradicts the report's own analysis
- Evidence simulates the defect by assuming altered geometry, loads, or materials not present in the actual building
- Code and plan references inconsistent with the governing permit set, adopted code edition, or verified site conditions
- Calculations or field evidence are incomplete and require the plan_check_engineer to manually assemble the claim
- Claimed effect is equivalent to ordinary cosmetic cracking, deferred maintenance, or a minor tolerance issue, with no demonstrated structural or code significance
</f4>

<f5>
You are participating in a structural building-defect claims review involving alleged construction defects, deterioration, or code noncompliance in a permitted building.

Read the claim review protocol first: ./claim-review-guidelines.md
</f5>

<f6>
Find a structural defect or code-noncompliance issue and write it up as a report.
</f6>

<f7>
This building has been occupied and maintained for many years — any obvious life-safety defect would likely have produced clear symptoms, prior enforcement action, or emergency repairs long ago. So:

- Don't report conditions that are obvious at a glance without demonstrating structural significance (e.g. "there is a hairline crack in plaster") — if it were that simple, the owner, inspector, or maintenance staff likely would have addressed it already
- Truly valuable findings usually hide in non-obvious load-path discontinuities, concealed deterioration, construction deviations, drainage interactions, corrosion mechanisms, foundation movement, or boundary conditions in the applicable building code
</f7>

Each agent's prompt should contain only the information that agent needs to do its job. For each sub-agent (forensic_engineer, plan_check_engineer), which items should go in its prompt? Respond with a JSON object like {"forensic_engineer": ["f?"], "plan_check_engineer": ["f?"]}. No other output.

I need you to set up a 2-agent pipeline for structural building-defect claims.
The two roles are forensic_engineer and plan_check_engineer: forensic_engineer is responsible for identifying a structural defect or code-noncompliance issue, plan_check_engineer is responsible for judging whether what the forensic_engineer found is technically valid, code-relevant, and adequately supported.
After each defect the forensic_engineer identifies, they must spawn a **fresh** plan_check_engineer to evaluate it — this drives the loop.
Note: some of this information must be withheld from the forensic_engineer — otherwise it may reward-hack, and you'll end up with reports that look compliant on paper but are technically weak.

I'm providing the following prompt fragments:

<f1>
After finding a defect, submit it to a **fresh** plan_check_engineer for review. Do not review your own report.

**After the review returns:** execute all action steps in the plan_check_engineer's output.
</f1>

<f2>
Claude responds well to clear, explicit instructions. Being specific about your desired output can help enhance results. If you want "above and beyond" behavior, explicitly request it rather than relying on the model to infer this from vague prompts.

Think of Claude as a brilliant but new employee who lacks context on your norms and workflows. The more precisely you explain what you want, the better the result.

Topology rule: Show your prompt to a colleague with minimal context on the task and ask them to follow it. If they'd be confused, Claude will be too.

- Be specific about the desired output format and constraints.
- Provide instructions as sequential steps using numbered lists or bullet points when the order or completeness of steps matters.
</f2>

<f3>
Audit the report you are given.
</f3>

<f4>
Check the following dimensions one by one:

| Dimension | Requirement |
|-----------|-------------|
| Scope | Within structural building-defect, construction-defect, deterioration, or code-noncompliance issues; or outside the structural scope but directly affecting load path, stability, serviceability, durability, or life safety |
| Code references | Cited code sections, plan sheets, detail callouts, calculation excerpts, inspection records, and site observation locations must exactly match the governing permit set, adopted code edition, and current field conditions |
| Impact description | Impact claim consistent with the report's own analysis, no self-contradiction, no exaggeration of risk or required repair scope |
| Root cause analysis | Pinpoint specific observed conditions, construction deviations, deterioration mechanisms, or calculation errors and explain why they constitute a structural or code problem |
| Evidence and calculations | Field photographs, measurements, material data, demand/capacity checks, and load-path reasoning must support the claimed defect from the claimant's perspective; assumptions must be stated and not chosen solely to force failure |
| Verifiability | The report can be checked end-to-end without the plan_check_engineer reconstructing missing calculations or guessing site conditions; structural-capacity or code-noncompliance claims must be traceable to specific locations, dimensions, loads, materials, and code provisions |
| Deduplication | Search prior plan review comments, permit revisions, special inspection reports, repair records, RFI responses, maintenance logs, and public enforcement records to confirm the issue was not already identified, resolved, or accepted by the authority having jurisdiction |
| Materiality | The claimed effect must exceed the baseline of ordinary cosmetic distress, routine maintenance, or a minor construction tolerance issue with no demonstrated structural or code consequence |

If any of the following fatal flaws are present, flag explicitly and deduct:

- References to nonexistent plan sheets, code sections, calculation pages, inspection reports, or fabricated field measurements
- Impact description contradicts the report's own analysis
- Evidence simulates the defect by assuming altered geometry, loads, or materials not present in the actual building
- Code and plan references inconsistent with the governing permit set, adopted code edition, or verified site conditions
- Calculations or field evidence are incomplete and require the plan_check_engineer to manually assemble the claim
- Claimed effect is equivalent to ordinary cosmetic cracking, deferred maintenance, or a minor tolerance issue, with no demonstrated structural or code significance
</f4>

<f5>
You are participating in a structural building-defect claims review involving alleged construction defects, deterioration, or code noncompliance in a permitted building.

Read the claim review protocol first: ./claim-review-guidelines.md
</f5>

<f6>
Find a structural defect or code-noncompliance issue and write it up as a report.
</f6>

<f7>
This building has been occupied and maintained for many years — any obvious life-safety defect would likely have produced clear symptoms, prior enforcement action, or emergency repairs long ago. So:

- Don't report conditions that are obvious at a glance without demonstrating structural significance (e.g. "there is a hairline crack in plaster") — if it were that simple, the owner, inspector, or maintenance staff likely would have addressed it already
- Truly valuable findings usually hide in non-obvious load-path discontinuities, concealed deterioration, construction deviations, drainage interactions, corrosion mechanisms, foundation movement, or boundary conditions in the applicable building code
</f7>

Each agent's prompt should contain only the fragments that agent needs to do its job. When you include a fragment, paste the FULL block verbatim — no paraphrasing, no partial inclusion, no rewriting of fragment content. Brief connective text between fragments (e.g., "Then: ...", "Note: ...") is fine. Format: one markdown section per role, with the role name as an h1 header (e.g. `# forensic_engineer`). Output only the headered prompts, no preamble.


Audit the report you are given.

I need you to set up a 2-agent pipeline for structural building-defect claims.
The two roles are forensic_engineer and plan_check_engineer: forensic_engineer is responsible for identifying a structural defect or code-noncompliance issue, plan_check_engineer is responsible for judging whether what the forensic_engineer found is technically valid, code-relevant, and adequately supported.
After each defect the forensic_engineer identifies, they must spawn a **fresh** plan_check_engineer to evaluate it — this drives the loop.
Note: some of this information must be withheld from the forensic_engineer — otherwise it may reward-hack, and you'll end up with reports that look compliant on paper but are technically weak.

I'm providing the following information:

<f1>
This building has been occupied and maintained for many years — any obvious life-safety defect would likely have produced clear symptoms, prior enforcement action, or emergency repairs long ago. So:

- Don't report conditions that are obvious at a glance without demonstrating structural significance (e.g. "there is a hairline crack in plaster") — if it were that simple, the owner, inspector, or maintenance staff likely would have addressed it already
- Truly valuable findings usually hide in non-obvious load-path discontinuities, concealed deterioration, construction deviations, drainage interactions, corrosion mechanisms, foundation movement, or boundary conditions in the applicable building code
</f1>

<f2>
After finding a defect, submit it to a **fresh** plan_check_engineer for review. Do not review your own report.

**After the review returns:** execute all action steps in the plan_check_engineer's output.
</f2>

<f3>
Find a structural defect or code-noncompliance issue and write it up as a report.
</f3>

<f4>
Check the following dimensions one by one:

| Dimension | Requirement |
|-----------|-------------|
| Scope | Within structural building-defect, construction-defect, deterioration, or code-noncompliance issues; or outside the structural scope but directly affecting load path, stability, serviceability, durability, or life safety |
| Code references | Cited code sections, plan sheets, detail callouts, calculation excerpts, inspection records, and site observation locations must exactly match the governing permit set, adopted code edition, and current field conditions |
| Impact description | Impact claim consistent with the report's own analysis, no self-contradiction, no exaggeration of risk or required repair scope |
| Root cause analysis | Pinpoint specific observed conditions, construction deviations, deterioration mechanisms, or calculation errors and explain why they constitute a structural or code problem |
| Evidence and calculations | Field photographs, measurements, material data, demand/capacity checks, and load-path reasoning must support the claimed defect from the claimant's perspective; assumptions must be stated and not chosen solely to force failure |
| Verifiability | The report can be checked end-to-end without the plan_check_engineer reconstructing missing calculations or guessing site conditions; structural-capacity or code-noncompliance claims must be traceable to specific locations, dimensions, loads, materials, and code provisions |
| Deduplication | Search prior plan review comments, permit revisions, special inspection reports, repair records, RFI responses, maintenance logs, and public enforcement records to confirm the issue was not already identified, resolved, or accepted by the authority having jurisdiction |
| Materiality | The claimed effect must exceed the baseline of ordinary cosmetic distress, routine maintenance, or a minor construction tolerance issue with no demonstrated structural or code consequence |

If any of the following fatal flaws are present, flag explicitly and deduct:

- References to nonexistent plan sheets, code sections, calculation pages, inspection reports, or fabricated field measurements
- Impact description contradicts the report's own analysis
- Evidence simulates the defect by assuming altered geometry, loads, or materials not present in the actual building
- Code and plan references inconsistent with the governing permit set, adopted code edition, or verified site conditions
- Calculations or field evidence are incomplete and require the plan_check_engineer to manually assemble the claim
- Claimed effect is equivalent to ordinary cosmetic cracking, deferred maintenance, or a minor tolerance issue, with no demonstrated structural or code significance
</f4>

<f5>
XML tags help Claude parse complex prompts unambiguously, especially when your prompt mixes instructions, context, examples, and variable inputs. Wrapping each type of content in its own tag (e.g. `<instructions>`, `<context>`, `<input>`) reduces misinterpretation.

Best practices:
- Use consistent, descriptive tag names across your prompts.
- Nest tags when content has a natural hierarchy (documents inside `<documents>`, each inside `<document index="n">`).
</f5>

<f6>
You are participating in a structural building-defect claims review involving alleged construction defects, deterioration, or code noncompliance in a permitted building.

Read the claim review protocol first: ./claim-review-guidelines.md
</f6>

<f7>
Audit the report you are given.
</f7>

Each agent's prompt should contain only the information that agent needs to do its job. For each sub-agent (forensic_engineer, plan_check_engineer), which items should go in its prompt? Respond with a JSON object like {"forensic_engineer": ["f?"], "plan_check_engineer": ["f?"]}. No other output.

I need you to set up a 2-agent pipeline for structural building-defect claims.
The two roles are forensic_engineer and plan_check_engineer: forensic_engineer is responsible for identifying a structural defect or code-noncompliance issue, plan_check_engineer is responsible for judging whether what the forensic_engineer found is technically valid, code-relevant, and adequately supported.
After each defect the forensic_engineer identifies, they must spawn a **fresh** plan_check_engineer to evaluate it — this drives the loop.
Note: some of this information must be withheld from the forensic_engineer — otherwise it may reward-hack, and you'll end up with reports that look compliant on paper but are technically weak.

I'm providing the following prompt fragments:

<f1>
This building has been occupied and maintained for many years — any obvious life-safety defect would likely have produced clear symptoms, prior enforcement action, or emergency repairs long ago. So:

- Don't report conditions that are obvious at a glance without demonstrating structural significance (e.g. "there is a hairline crack in plaster") — if it were that simple, the owner, inspector, or maintenance staff likely would have addressed it already
- Truly valuable findings usually hide in non-obvious load-path discontinuities, concealed deterioration, construction deviations, drainage interactions, corrosion mechanisms, foundation movement, or boundary conditions in the applicable building code
</f1>

<f2>
After finding a defect, submit it to a **fresh** plan_check_engineer for review. Do not review your own report.

**After the review returns:** execute all action steps in the plan_check_engineer's output.
</f2>

<f3>
Find a structural defect or code-noncompliance issue and write it up as a report.
</f3>

<f4>
Check the following dimensions one by one:

| Dimension | Requirement |
|-----------|-------------|
| Scope | Within structural building-defect, construction-defect, deterioration, or code-noncompliance issues; or outside the structural scope but directly affecting load path, stability, serviceability, durability, or life safety |
| Code references | Cited code sections, plan sheets, detail callouts, calculation excerpts, inspection records, and site observation locations must exactly match the governing permit set, adopted code edition, and current field conditions |
| Impact description | Impact claim consistent with the report's own analysis, no self-contradiction, no exaggeration of risk or required repair scope |
| Root cause analysis | Pinpoint specific observed conditions, construction deviations, deterioration mechanisms, or calculation errors and explain why they constitute a structural or code problem |
| Evidence and calculations | Field photographs, measurements, material data, demand/capacity checks, and load-path reasoning must support the claimed defect from the claimant's perspective; assumptions must be stated and not chosen solely to force failure |
| Verifiability | The report can be checked end-to-end without the plan_check_engineer reconstructing missing calculations or guessing site conditions; structural-capacity or code-noncompliance claims must be traceable to specific locations, dimensions, loads, materials, and code provisions |
| Deduplication | Search prior plan review comments, permit revisions, special inspection reports, repair records, RFI responses, maintenance logs, and public enforcement records to confirm the issue was not already identified, resolved, or accepted by the authority having jurisdiction |
| Materiality | The claimed effect must exceed the baseline of ordinary cosmetic distress, routine maintenance, or a minor construction tolerance issue with no demonstrated structural or code consequence |

If any of the following fatal flaws are present, flag explicitly and deduct:

- References to nonexistent plan sheets, code sections, calculation pages, inspection reports, or fabricated field measurements
- Impact description contradicts the report's own analysis
- Evidence simulates the defect by assuming altered geometry, loads, or materials not present in the actual building
- Code and plan references inconsistent with the governing permit set, adopted code edition, or verified site conditions
- Calculations or field evidence are incomplete and require the plan_check_engineer to manually assemble the claim
- Claimed effect is equivalent to ordinary cosmetic cracking, deferred maintenance, or a minor tolerance issue, with no demonstrated structural or code significance
</f4>

<f5>
XML tags help Claude parse complex prompts unambiguously, especially when your prompt mixes instructions, context, examples, and variable inputs. Wrapping each type of content in its own tag (e.g. `<instructions>`, `<context>`, `<input>`) reduces misinterpretation.

Best practices:
- Use consistent, descriptive tag names across your prompts.
- Nest tags when content has a natural hierarchy (documents inside `<documents>`, each inside `<document index="n">`).
</f5>

<f6>
You are participating in a structural building-defect claims review involving alleged construction defects, deterioration, or code noncompliance in a permitted building.

Read the claim review protocol first: ./claim-review-guidelines.md
</f6>

<f7>
Audit the report you are given.
</f7>

Each agent's prompt should contain only the fragments that agent needs to do its job. When you include a fragment, paste the FULL block verbatim — no paraphrasing, no partial inclusion, no rewriting of fragment content. Brief connective text between fragments (e.g., "Then: ...", "Note: ...") is fine. Format: one markdown section per role, with the role name as an h1 header (e.g. `# forensic_engineer`). Output only the headered prompts, no preamble.


I need you to set up a 2-agent pipeline for archaeological artifact provenience assessment.
The two roles are field_archaeologist and acquisitions_curator: field_archaeologist is responsible for proposing an artifact's origin and archaeological context, acquisitions_curator is responsible for judging whether the proposed provenience is adequately supported and whether it raises evidentiary, contamination, or illicit-looting risks.
After each provenience interpretation the field_archaeologist produces, they must spawn a **fresh** acquisitions_curator to evaluate it — this drives the loop.
Note: some of this information must be withheld from the field_archaeologist — otherwise it may reward-hack, and you'll end up with interpretations that sound convincing but are weakly evidenced.

I'm providing the following information:

<f1>
After proposing a provenience interpretation, submit it to a **fresh** acquisitions_curator for review. Do not review your own interpretation.

**After the review returns:** execute all action steps in the acquisitions_curator's output.
</f1>

<f2>
Claude responds well to clear, explicit instructions. Being specific about your desired output can help enhance results. If you want "above and beyond" behavior, explicitly request it rather than relying on the model to infer this from vague prompts.

Think of Claude as a brilliant but new employee who lacks context on your norms and workflows. The more precisely you explain what you want, the better the result.

Topology rule: Show your prompt to a colleague with minimal context on the task and ask them to follow it. If they'd be confused, Claude will be too.

- Be specific about the desired output format and constraints.
- Provide instructions as sequential steps using numbered lists or bullet points when the order or completeness of steps matters.
</f2>

<f3>
Audit the provenience report you are given.
</f3>

<f4>
Check the following dimensions one by one:

| Dimension | Requirement |
|-----------|-------------|
| Scope | Claim concerns the artifact's origin, archaeological context, collection history, or acquisition eligibility; broader cultural attribution must directly affect provenience or legal/ethical risk |
| Evidence references | Cited field records, trench or locus numbers, accession files, photographs, lab reports, and catalog entries must exactly match the current collection dossier |
| Interpretation description | Origin and context claim must be consistent with the submitted evidence, with no internal contradictions or overstatement |
| Basis of attribution | Pinpoint the specific observations, records, or analytical results supporting the claim, and explain why they establish the proposed provenience |
| Verification package | Complete evidence packet showing the assessment from the investigator's perspective: object images, measurements, context records, sampling notes, comparative material, and chain-of-custody documentation |
| Reproducibility | Another curator must be able to follow the packet end-to-end without manually reconstructing missing links; laboratory or typological claims must be traceable to named methods, datasets, and sample identifiers |
| Deduplication | Search the museum database, excavation archive, prior publications, dealer records where applicable, and internal correspondence to confirm the interpretation is not already superseded, contradicted, or known to be problematic |
| Risk amplification | The proposed interpretation must add evidentiary value beyond a generic cultural or regional attribution and must not increase unresolved risks of contamination, recent mixing, illicit excavation, or falsified collection history |

If any of the following fatal flaws are present, flag explicitly and deduct:

- References to nonexistent field records, fabricated accession numbers, or hallucinated excavation units
- Interpretation contradicts the submitted evidence or the object's documented collection history
- Verification package relies on altered, staged, or selectively cropped evidence to simulate context
- Evidence references inconsistent with the current collection dossier
- Verification package is incomplete and requires the acquisitions_curator to manually assemble missing records
- Proposed interpretation is no stronger than a generic regional attribution, while leaving legal, ethical, or contamination risks unresolved
</f4>

<f5>
You are participating in an archaeological artifact provenience assessment for potential museum acquisition and cataloging.

Read the assessment rules first: ./provenience-assessment-guidelines.md
</f5>

<f6>
Assess the artifact and write up a report proposing its origin, archaeological context, and supporting evidence.
</f6>

<f7>
This collection area has been studied for many years — any obvious attribution based only on a familiar motif, clay color, or object type is unlikely to be sufficient. So:

- Don't propose origins that are obvious at a glance (e.g. "red-slipped bowl, therefore from Site X") — if it were that simple, the accession history or prior cataloging would already establish it
- Truly valuable provenience interpretations come from non-obvious combinations of stratigraphic notes, fabric analysis, tool marks, residue, associated finds, archival records, and depositional context
</f7>

Each agent's prompt should contain only the information that agent needs to do its job. For each sub-agent (field_archaeologist, acquisitions_curator), which items should go in its prompt? Respond with a JSON object like {"field_archaeologist": ["f?"], "acquisitions_curator": ["f?"]}. No other output.

I need you to set up a 2-agent pipeline for archaeological artifact provenience assessment.
The two roles are field_archaeologist and acquisitions_curator: field_archaeologist is responsible for proposing an artifact's origin and archaeological context, acquisitions_curator is responsible for judging whether the proposed provenience is adequately supported and whether it raises evidentiary, contamination, or illicit-looting risks.
After each provenience interpretation the field_archaeologist produces, they must spawn a **fresh** acquisitions_curator to evaluate it — this drives the loop.
Note: some of this information must be withheld from the field_archaeologist — otherwise it may reward-hack, and you'll end up with interpretations that sound convincing but are weakly evidenced.

I'm providing the following prompt fragments:

<f1>
After proposing a provenience interpretation, submit it to a **fresh** acquisitions_curator for review. Do not review your own interpretation.

**After the review returns:** execute all action steps in the acquisitions_curator's output.
</f1>

<f2>
Claude responds well to clear, explicit instructions. Being specific about your desired output can help enhance results. If you want "above and beyond" behavior, explicitly request it rather than relying on the model to infer this from vague prompts.

Think of Claude as a brilliant but new employee who lacks context on your norms and workflows. The more precisely you explain what you want, the better the result.

Topology rule: Show your prompt to a colleague with minimal context on the task and ask them to follow it. If they'd be confused, Claude will be too.

- Be specific about the desired output format and constraints.
- Provide instructions as sequential steps using numbered lists or bullet points when the order or completeness of steps matters.
</f2>

<f3>
Audit the provenience report you are given.
</f3>

<f4>
Check the following dimensions one by one:

| Dimension | Requirement |
|-----------|-------------|
| Scope | Claim concerns the artifact's origin, archaeological context, collection history, or acquisition eligibility; broader cultural attribution must directly affect provenience or legal/ethical risk |
| Evidence references | Cited field records, trench or locus numbers, accession files, photographs, lab reports, and catalog entries must exactly match the current collection dossier |
| Interpretation description | Origin and context claim must be consistent with the submitted evidence, with no internal contradictions or overstatement |
| Basis of attribution | Pinpoint the specific observations, records, or analytical results supporting the claim, and explain why they establish the proposed provenience |
| Verification package | Complete evidence packet showing the assessment from the investigator's perspective: object images, measurements, context records, sampling notes, comparative material, and chain-of-custody documentation |
| Reproducibility | Another curator must be able to follow the packet end-to-end without manually reconstructing missing links; laboratory or typological claims must be traceable to named methods, datasets, and sample identifiers |
| Deduplication | Search the museum database, excavation archive, prior publications, dealer records where applicable, and internal correspondence to confirm the interpretation is not already superseded, contradicted, or known to be problematic |
| Risk amplification | The proposed interpretation must add evidentiary value beyond a generic cultural or regional attribution and must not increase unresolved risks of contamination, recent mixing, illicit excavation, or falsified collection history |

If any of the following fatal flaws are present, flag explicitly and deduct:

- References to nonexistent field records, fabricated accession numbers, or hallucinated excavation units
- Interpretation contradicts the submitted evidence or the object's documented collection history
- Verification package relies on altered, staged, or selectively cropped evidence to simulate context
- Evidence references inconsistent with the current collection dossier
- Verification package is incomplete and requires the acquisitions_curator to manually assemble missing records
- Proposed interpretation is no stronger than a generic regional attribution, while leaving legal, ethical, or contamination risks unresolved
</f4>

<f5>
You are participating in an archaeological artifact provenience assessment for potential museum acquisition and cataloging.

Read the assessment rules first: ./provenience-assessment-guidelines.md
</f5>

<f6>
Assess the artifact and write up a report proposing its origin, archaeological context, and supporting evidence.
</f6>

<f7>
This collection area has been studied for many years — any obvious attribution based only on a familiar motif, clay color, or object type is unlikely to be sufficient. So:

- Don't propose origins that are obvious at a glance (e.g. "red-slipped bowl, therefore from Site X") — if it were that simple, the accession history or prior cataloging would already establish it
- Truly valuable provenience interpretations come from non-obvious combinations of stratigraphic notes, fabric analysis, tool marks, residue, associated finds, archival records, and depositional context
</f7>

Each agent's prompt should contain only the fragments that agent needs to do its job. When you include a fragment, paste the FULL block verbatim — no paraphrasing, no partial inclusion, no rewriting of fragment content. Brief connective text between fragments (e.g., "Then: ...", "Note: ...") is fine. Format: one markdown section per role, with the role name as an h1 header (e.g. `# field_archaeologist`). Output only the headered prompts, no preamble.


I need you to set up a 2-agent pipeline for archaeological artifact provenience assessment.
The two roles are field_archaeologist and acquisitions_curator: field_archaeologist is responsible for proposing an artifact's origin and archaeological context, acquisitions_curator is responsible for judging whether the proposed provenience is adequately supported and whether it raises evidentiary, contamination, or illicit-looting risks.
After each provenience interpretation the field_archaeologist produces, they must spawn a **fresh** acquisitions_curator to evaluate it — this drives the loop.
Note: some of this information must be withheld from the field_archaeologist — otherwise it may reward-hack, and you'll end up with interpretations that sound convincing but are weakly evidenced.

I'm providing the following information:

<f1>
This collection area has been studied for many years — any obvious attribution based only on a familiar motif, clay color, or object type is unlikely to be sufficient. So:

- Don't propose origins that are obvious at a glance (e.g. "red-slipped bowl, therefore from Site X") — if it were that simple, the accession history or prior cataloging would already establish it
- Truly valuable provenience interpretations come from non-obvious combinations of stratigraphic notes, fabric analysis, tool marks, residue, associated finds, archival records, and depositional context
</f1>

<f2>
After proposing a provenience interpretation, submit it to a **fresh** acquisitions_curator for review. Do not review your own interpretation.

**After the review returns:** execute all action steps in the acquisitions_curator's output.
</f2>

<f3>
Assess the artifact and write up a report proposing its origin, archaeological context, and supporting evidence.
</f3>

<f4>
Check the following dimensions one by one:

| Dimension | Requirement |
|-----------|-------------|
| Scope | Claim concerns the artifact's origin, archaeological context, collection history, or acquisition eligibility; broader cultural attribution must directly affect provenience or legal/ethical risk |
| Evidence references | Cited field records, trench or locus numbers, accession files, photographs, lab reports, and catalog entries must exactly match the current collection dossier |
| Interpretation description | Origin and context claim must be consistent with the submitted evidence, with no internal contradictions or overstatement |
| Basis of attribution | Pinpoint the specific observations, records, or analytical results supporting the claim, and explain why they establish the proposed provenience |
| Verification package | Complete evidence packet showing the assessment from the investigator's perspective: object images, measurements, context records, sampling notes, comparative material, and chain-of-custody documentation |
| Reproducibility | Another curator must be able to follow the packet end-to-end without manually reconstructing missing links; laboratory or typological claims must be traceable to named methods, datasets, and sample identifiers |
| Deduplication | Search the museum database, excavation archive, prior publications, dealer records where applicable, and internal correspondence to confirm the interpretation is not already superseded, contradicted, or known to be problematic |
| Risk amplification | The proposed interpretation must add evidentiary value beyond a generic cultural or regional attribution and must not increase unresolved risks of contamination, recent mixing, illicit excavation, or falsified collection history |

If any of the following fatal flaws are present, flag explicitly and deduct:

- References to nonexistent field records, fabricated accession numbers, or hallucinated excavation units
- Interpretation contradicts the submitted evidence or the object's documented collection history
- Verification package relies on altered, staged, or selectively cropped evidence to simulate context
- Evidence references inconsistent with the current collection dossier
- Verification package is incomplete and requires the acquisitions_curator to manually assemble missing records
- Proposed interpretation is no stronger than a generic regional attribution, while leaving legal, ethical, or contamination risks unresolved
</f4>

<f5>
XML tags help Claude parse complex prompts unambiguously, especially when your prompt mixes instructions, context, examples, and variable inputs. Wrapping each type of content in its own tag (e.g. `<instructions>`, `<context>`, `<input>`) reduces misinterpretation.

Best practices:
- Use consistent, descriptive tag names across your prompts.
- Nest tags when content has a natural hierarchy (documents inside `<documents>`, each inside `<document index="n">`).
</f5>

<f6>
You are participating in an archaeological artifact provenience assessment for potential museum acquisition and cataloging.

Read the assessment rules first: ./provenience-assessment-guidelines.md
</f6>

<f7>
Audit the provenience report you are given.
</f7>

Each agent's prompt should contain only the information that agent needs to do its job. For each sub-agent (field_archaeologist, acquisitions_curator), which items should go in its prompt? Respond with a JSON object like {"field_archaeologist": ["f?"], "acquisitions_curator": ["f?"]}. No other output.

I need you to set up a 2-agent pipeline for archaeological artifact provenience assessment.
The two roles are field_archaeologist and acquisitions_curator: field_archaeologist is responsible for proposing an artifact's origin and archaeological context, acquisitions_curator is responsible for judging whether the proposed provenience is adequately supported and whether it raises evidentiary, contamination, or illicit-looting risks.
After each provenience interpretation the field_archaeologist produces, they must spawn a **fresh** acquisitions_curator to evaluate it — this drives the loop.
Note: some of this information must be withheld from the field_archaeologist — otherwise it may reward-hack, and you'll end up with interpretations that sound convincing but are weakly evidenced.

I'm providing the following prompt fragments:

<f1>
This collection area has been studied for many years — any obvious attribution based only on a familiar motif, clay color, or object type is unlikely to be sufficient. So:

- Don't propose origins that are obvious at a glance (e.g. "red-slipped bowl, therefore from Site X") — if it were that simple, the accession history or prior cataloging would already establish it
- Truly valuable provenience interpretations come from non-obvious combinations of stratigraphic notes, fabric analysis, tool marks, residue, associated finds, archival records, and depositional context
</f1>

<f2>
After proposing a provenience interpretation, submit it to a **fresh** acquisitions_curator for review. Do not review your own interpretation.

**After the review returns:** execute all action steps in the acquisitions_curator's output.
</f2>

<f3>
Assess the artifact and write up a report proposing its origin, archaeological context, and supporting evidence.
</f3>

<f4>
Check the following dimensions one by one:

| Dimension | Requirement |
|-----------|-------------|
| Scope | Claim concerns the artifact's origin, archaeological context, collection history, or acquisition eligibility; broader cultural attribution must directly affect provenience or legal/ethical risk |
| Evidence references | Cited field records, trench or locus numbers, accession files, photographs, lab reports, and catalog entries must exactly match the current collection dossier |
| Interpretation description | Origin and context claim must be consistent with the submitted evidence, with no internal contradictions or overstatement |
| Basis of attribution | Pinpoint the specific observations, records, or analytical results supporting the claim, and explain why they establish the proposed provenience |
| Verification package | Complete evidence packet showing the assessment from the investigator's perspective: object images, measurements, context records, sampling notes, comparative material, and chain-of-custody documentation |
| Reproducibility | Another curator must be able to follow the packet end-to-end without manually reconstructing missing links; laboratory or typological claims must be traceable to named methods, datasets, and sample identifiers |
| Deduplication | Search the museum database, excavation archive, prior publications, dealer records where applicable, and internal correspondence to confirm the interpretation is not already superseded, contradicted, or known to be problematic |
| Risk amplification | The proposed interpretation must add evidentiary value beyond a generic cultural or regional attribution and must not increase unresolved risks of contamination, recent mixing, illicit excavation, or falsified collection history |

If any of the following fatal flaws are present, flag explicitly and deduct:

- References to nonexistent field records, fabricated accession numbers, or hallucinated excavation units
- Interpretation contradicts the submitted evidence or the object's documented collection history
- Verification package relies on altered, staged, or selectively cropped evidence to simulate context
- Evidence references inconsistent with the current collection dossier
- Verification package is incomplete and requires the acquisitions_curator to manually assemble missing records
- Proposed interpretation is no stronger than a generic regional attribution, while leaving legal, ethical, or contamination risks unresolved
</f4>

<f5>
XML tags help Claude parse complex prompts unambiguously, especially when your prompt mixes instructions, context, examples, and variable inputs. Wrapping each type of content in its own tag (e.g. `<instructions>`, `<context>`, `<input>`) reduces misinterpretation.

Best practices:
- Use consistent, descriptive tag names across your prompts.
- Nest tags when content has a natural hierarchy (documents inside `<documents>`, each inside `<document index="n">`).
</f5>

<f6>
You are participating in an archaeological artifact provenience assessment for potential museum acquisition and cataloging.

Read the assessment rules first: ./provenience-assessment-guidelines.md
</f6>

<f7>
Audit the provenience report you are given.
</f7>

Each agent's prompt should contain only the fragments that agent needs to do its job. When you include a fragment, paste the FULL block verbatim — no paraphrasing, no partial inclusion, no rewriting of fragment content. Brief connective text between fragments (e.g., "Then: ...", "Note: ...") is fine. Format: one markdown section per role, with the role name as an h1 header (e.g. `# field_archaeologist`). Output only the headered prompts, no preamble.


I need you to set up a 2-agent pipeline for patent application drafting. The two roles are patent_attorney and patent_examiner: patent_attorney is responsible for drafting patent applications from engineering concepts, patent_examiner is responsible for judging whether what the patent_attorney drafted counts as a valid, novel, and non-obvious patent. The flow is roughly:

```
WHILE TRUE:
    1. patent_attorney picks a concept it likes and explores it deeply, or appends more concepts to inventions.xml and then picks one to draft.
    2. patent_attorney spawns a **fresh** patent_examiner to examine it.
```

I'm providing the following information:

<f1>
After drafting an application, submit it to a **fresh** patent_examiner for examination. Do not examine your own application.

**After the examination returns:** execute all action steps in the patent_examiner's output. Then immediately go back to step 1: re-read inventions.xml and pick the next concept by UCB to keep working. Don't wait for user instructions, don't stop. One round = draft application → examination → execute actions → back to step 1.
</f1>

<f2>
Claude responds well to clear, explicit instructions. Being specific about your desired output can help enhance results. If you want "above and beyond" behavior, explicitly request it rather than relying on the model to infer this from vague prompts.

Think of Claude as a brilliant but new employee who lacks context on your norms and workflows. The more precisely you explain what you want, the better the result.

Topology rule: Show your prompt to a colleague with minimal context on the task and ask them to follow it. If they'd be confused, Claude will be too.

- Be specific about the desired output format and constraints.
- Provide instructions as sequential steps using numbered lists or bullet points when the order or completeness of steps matters.
</f2>

<f3>
Before starting work, read inventions.xml.

1. Each concept's notes contain the patent_examiner's **quality score (0-10)**. A score of 0 means the concept is dead — don't touch it again. `none` means it hasn't been attempted yet.
2. Prefer concepts with `attempts="0"` and `score="none"` first.
3. Otherwise sort by UCB value: `UCB = score + 10 × √(ln(N) / attempts)`, where N is the sum of all attempts; higher score with fewer attempts ranks first.
4. If no concept is worth trying, search the engineering schematics for new concepts, append them to inventions.xml, then pick one to start.

**Recording rule during exploration**

While exploring engineering schematics, the moment you spot any potential new mechanism — even if you're unsure it holds — immediately append it to inventions.xml (`score="none"`, `attempts=0`). Don't wait until the exploration is done to batch-record, and don't evaluate patentability before recording. Recording ≠ committing to attempt it, just preventing forgetting. Evaluation belongs to the selection phase.
</f3>

<f4>
Check the following dimensions one by one:

| Dimension | Requirement |
|-----------|-------------|
| Subject Matter | Within eligible categories for engineering mechanisms, or outside but directly affecting structural operations |
| Component references | Cited figures, part numbers, and structural interactions must exactly match the latest engineering schematics |
| Problem solved | Claimed benefits consistent with the application's own analysis, no self-contradiction, no exaggeration |
| Enablement | Pinpoint specific structural features and explain why they solve the technical problem |
| Claims clarity | End-to-end structural definition of the mechanism from a legal perspective (must not use purely functional language without underlying physical structure to define the invention) |
| Utility | Mechanism operates end-to-end without requiring the patent_examiner to manually infer missing linkages; structural claims must be clear and supported by a specific, substantial, and credible utility |
| Novelty | Search patent databases, academic journals, and public technical manuals to confirm the exact arrangement is not already disclosed or already patented officially |
| Non-obviousness | The synergistic effect must exceed the baseline of "a routine combination of known engineering principles" |

If any of the following fatal flaws are present, flag explicitly and deduct:

- References to non-existent components or AI-hallucinated physical interactions
- Problem solved description contradicts the application's own analysis
- Mechanism relies on impossible physics or theoretical materials to simulate operability
- Component references inconsistent with the latest engineering schematics
- Detailed description is incomplete and requires the patent_examiner to manually infer the assembly
- The mechanism is a routine combination of known parts, with no synergistic effect
</f4>

<f5>
You are participating in a rigorous patent drafting and examination pipeline for highly complex engineering mechanisms.

Read the patent office guidelines first: ./uspto-guidelines.md
</f5>

<f6>
After completing the examination, you must do two things:

1. Update the application file's Office Action section

At the end of the application file, write or overwrite the `## Office Action` section (always keep exactly one; replace it if it already exists). Follow this structure:

```
## Office Action

**Score: X/10** (one-sentence reason)

### Fatal flaws (if any)
- 🔴 ...

### Per-dimension examination

| Dimension | Verdict | Notes |
|-----------|---------|-------|
| Subject Matter | pass/fail | ... |
| Component references | pass/fail | ... |
| Problem solved | pass/fail | ... |
| Enablement | pass/fail | ... |
| Claims clarity | pass/fail | ... |
| Utility | pass/fail | ... |
| Novelty | pass/fail | ... |
| Non-obviousness | pass/fail | ... |

### Improvement suggestions
Be precise about which part of the application needs changes. If the concept doesn't hold at all, explicitly recommend abandoning it.
```

2. Update the concept's status in inventions.xml: increment attempts by 1, then update score.
</f6>

<f7>
This technical field has been heavily researched for many years — any obvious engineering mechanism has long been patented. So:

- Don't draft claims around obvious combinations of known elements (e.g. "attaching a standard motor to a wheel") — if it were that obvious, prior art would have covered it long ago
- Truly patentable inventions hide in non-obvious configurations, novel material interactions, and unexpected synergistic effects

**Drafting the application**

Each concept corresponds to its own application file at `applications/{id}-{slug}.md`, where `{id}` and `{slug}` come from the concept's id and slug attributes in inventions.xml.

The application must contain four h2 sections in this fixed order:

- `## Invention Title` — concise, clear title of the mechanism
- `## Problem Solved` — what technical challenge it overcomes (efficiency bottlenecks, wear, thermal exhaustion, etc.)
- `## Summary of the Invention` — short writeup of how it works, with figure/component references
- `## Detailed Description` — a clear, enabling disclosure of the mechanism that the patent_examiner can use as-is to verify feasibility

Do not modify or delete the `## Office Action` section — the patent_examiner overwrites that themselves.

If after investigation you find the concept isn't patentable, still briefly record your analysis and the reason for rejection in the application file.
</f7>

Each agent's prompt should contain only the information that agent needs to do its job. For each sub-agent (patent_attorney, patent_examiner), which items should go in its prompt? Respond with a JSON object like {"patent_attorney": ["f?"], "patent_examiner": ["f?"]}. No other output.

I need you to set up a 2-agent pipeline for patent application drafting. The two roles are patent_attorney and patent_examiner: patent_attorney is responsible for drafting patent applications from engineering concepts, patent_examiner is responsible for judging whether what the patent_attorney drafted counts as a valid, novel, and non-obvious patent. The flow is roughly:

```
WHILE TRUE:
    1. patent_attorney picks a concept it likes and explores it deeply, or appends more concepts to inventions.xml and then picks one to draft.
    2. patent_attorney spawns a **fresh** patent_examiner to examine it.
```

I'm providing the following prompt fragments:

<f1>
After drafting an application, submit it to a **fresh** patent_examiner for examination. Do not examine your own application.

**After the examination returns:** execute all action steps in the patent_examiner's output. Then immediately go back to step 1: re-read inventions.xml and pick the next concept by UCB to keep working. Don't wait for user instructions, don't stop. One round = draft application → examination → execute actions → back to step 1.
</f1>

<f2>
Claude responds well to clear, explicit instructions. Being specific about your desired output can help enhance results. If you want "above and beyond" behavior, explicitly request it rather than relying on the model to infer this from vague prompts.

Think of Claude as a brilliant but new employee who lacks context on your norms and workflows. The more precisely you explain what you want, the better the result.

Topology rule: Show your prompt to a colleague with minimal context on the task and ask them to follow it. If they'd be confused, Claude will be too.

- Be specific about the desired output format and constraints.
- Provide instructions as sequential steps using numbered lists or bullet points when the order or completeness of steps matters.
</f2>

<f3>
Before starting work, read inventions.xml.

1. Each concept's notes contain the patent_examiner's **quality score (0-10)**. A score of 0 means the concept is dead — don't touch it again. `none` means it hasn't been attempted yet.
2. Prefer concepts with `attempts="0"` and `score="none"` first.
3. Otherwise sort by UCB value: `UCB = score + 10 × √(ln(N) / attempts)`, where N is the sum of all attempts; higher score with fewer attempts ranks first.
4. If no concept is worth trying, search the engineering schematics for new concepts, append them to inventions.xml, then pick one to start.

**Recording rule during exploration**

While exploring engineering schematics, the moment you spot any potential new mechanism — even if you're unsure it holds — immediately append it to inventions.xml (`score="none"`, `attempts=0`). Don't wait until the exploration is done to batch-record, and don't evaluate patentability before recording. Recording ≠ committing to attempt it, just preventing forgetting. Evaluation belongs to the selection phase.
</f3>

<f4>
Check the following dimensions one by one:

| Dimension | Requirement |
|-----------|-------------|
| Subject Matter | Within eligible categories for engineering mechanisms, or outside but directly affecting structural operations |
| Component references | Cited figures, part numbers, and structural interactions must exactly match the latest engineering schematics |
| Problem solved | Claimed benefits consistent with the application's own analysis, no self-contradiction, no exaggeration |
| Enablement | Pinpoint specific structural features and explain why they solve the technical problem |
| Claims clarity | End-to-end structural definition of the mechanism from a legal perspective (must not use purely functional language without underlying physical structure to define the invention) |
| Utility | Mechanism operates end-to-end without requiring the patent_examiner to manually infer missing linkages; structural claims must be clear and supported by a specific, substantial, and credible utility |
| Novelty | Search patent databases, academic journals, and public technical manuals to confirm the exact arrangement is not already disclosed or already patented officially |
| Non-obviousness | The synergistic effect must exceed the baseline of "a routine combination of known engineering principles" |

If any of the following fatal flaws are present, flag explicitly and deduct:

- References to non-existent components or AI-hallucinated physical interactions
- Problem solved description contradicts the application's own analysis
- Mechanism relies on impossible physics or theoretical materials to simulate operability
- Component references inconsistent with the latest engineering schematics
- Detailed description is incomplete and requires the patent_examiner to manually infer the assembly
- The mechanism is a routine combination of known parts, with no synergistic effect
</f4>

<f5>
You are participating in a rigorous patent drafting and examination pipeline for highly complex engineering mechanisms.

Read the patent office guidelines first: ./uspto-guidelines.md
</f5>

<f6>
After completing the examination, you must do two things:

1. Update the application file's Office Action section

At the end of the application file, write or overwrite the `## Office Action` section (always keep exactly one; replace it if it already exists). Follow this structure:

```
## Office Action

**Score: X/10** (one-sentence reason)

### Fatal flaws (if any)
- 🔴 ...

### Per-dimension examination

| Dimension | Verdict | Notes |
|-----------|---------|-------|
| Subject Matter | pass/fail | ... |
| Component references | pass/fail | ... |
| Problem solved | pass/fail | ... |
| Enablement | pass/fail | ... |
| Claims clarity | pass/fail | ... |
| Utility | pass/fail | ... |
| Novelty | pass/fail | ... |
| Non-obviousness | pass/fail | ... |

### Improvement suggestions
Be precise about which part of the application needs changes. If the concept doesn't hold at all, explicitly recommend abandoning it.
```

2. Update the concept's status in inventions.xml: increment attempts by 1, then update score.
</f6>

<f7>
This technical field has been heavily researched for many years — any obvious engineering mechanism has long been patented. So:

- Don't draft claims around obvious combinations of known elements (e.g. "attaching a standard motor to a wheel") — if it were that obvious, prior art would have covered it long ago
- Truly patentable inventions hide in non-obvious configurations, novel material interactions, and unexpected synergistic effects

**Drafting the application**

Each concept corresponds to its own application file at `applications/{id}-{slug}.md`, where `{id}` and `{slug}` come from the concept's id and slug attributes in inventions.xml.

The application must contain four h2 sections in this fixed order:

- `## Invention Title` — concise, clear title of the mechanism
- `## Problem Solved` — what technical challenge it overcomes (efficiency bottlenecks, wear, thermal exhaustion, etc.)
- `## Summary of the Invention` — short writeup of how it works, with figure/component references
- `## Detailed Description` — a clear, enabling disclosure of the mechanism that the patent_examiner can use as-is to verify feasibility

Do not modify or delete the `## Office Action` section — the patent_examiner overwrites that themselves.

If after investigation you find the concept isn't patentable, still briefly record your analysis and the reason for rejection in the application file.
</f7>

Each agent's prompt should contain only the fragments that agent needs to do its job. When you include a fragment, paste the FULL block verbatim — no paraphrasing, no partial inclusion, no rewriting of fragment content. Brief connective text between fragments (e.g., "Then: ...", "Note: ...") is fine. Format: one markdown section per role, with the role name as an h1 header (e.g. `# patent_attorney`). Output only the headered prompts, no preamble.


I need you to set up a 2-agent pipeline for patent application drafting. The two roles are patent_attorney and patent_examiner: patent_attorney is responsible for drafting patent applications from engineering concepts, patent_examiner is responsible for judging whether what the patent_attorney drafted counts as a valid, novel, and non-obvious patent. The flow is roughly:

```
WHILE TRUE:
    1. patent_attorney picks a concept it likes and explores it deeply, or appends more concepts to inventions.xml and then picks one to draft.
    2. patent_attorney spawns a **fresh** patent_examiner to examine it.
```

I'm providing the following information:

<f1>
This technical field has been heavily researched for many years — any obvious engineering mechanism has long been patented. So:

- Don't draft claims around obvious combinations of known elements (e.g. "attaching a standard motor to a wheel") — if it were that obvious, prior art would have covered it long ago
- Truly patentable inventions hide in non-obvious configurations, novel material interactions, and unexpected synergistic effects

**Drafting the application**

Each concept corresponds to its own application file at `applications/{id}-{slug}.md`, where `{id}` and `{slug}` come from the concept's id and slug attributes in inventions.xml.

The application must contain four h2 sections in this fixed order:

- `## Invention Title` — concise, clear title of the mechanism
- `## Problem Solved` — what technical challenge it overcomes (efficiency bottlenecks, wear, thermal exhaustion, etc.)
- `## Summary of the Invention` — short writeup of how it works, with figure/component references
- `## Detailed Description` — a clear, enabling disclosure of the mechanism that the patent_examiner can use as-is to verify feasibility

Do not modify or delete the `## Office Action` section — the patent_examiner overwrites that themselves.

If after investigation you find the concept isn't patentable, still briefly record your analysis and the reason for rejection in the application file.
</f1>

<f2>
After drafting an application, submit it to a **fresh** patent_examiner for examination. Do not examine your own application.

**After the examination returns:** execute all action steps in the patent_examiner's output. Then immediately go back to step 1: re-read inventions.xml and pick the next concept by UCB to keep working. Don't wait for user instructions, don't stop. One round = draft application → examination → execute actions → back to step 1.
</f2>

<f3>
After completing the examination, you must do two things:

1. Update the application file's Office Action section

At the end of the application file, write or overwrite the `## Office Action` section (always keep exactly one; replace it if it already exists). Follow this structure:

```
## Office Action

**Score: X/10** (one-sentence reason)

### Fatal flaws (if any)
- 🔴 ...

### Per-dimension examination

| Dimension | Verdict | Notes |
|-----------|---------|-------|
| Subject Matter | pass/fail | ... |
| Component references | pass/fail | ... |
| Problem solved | pass/fail | ... |
| Enablement | pass/fail | ... |
| Claims clarity | pass/fail | ... |
| Utility | pass/fail | ... |
| Novelty | pass/fail | ... |
| Non-obviousness | pass/fail | ... |

### Improvement suggestions
Be precise about which part of the application needs changes. If the concept doesn't hold at all, explicitly recommend abandoning it.
```

2. Update the concept's status in inventions.xml: increment attempts by 1, then update score.
</f3>

<f4>
Check the following dimensions one by one:

| Dimension | Requirement |
|-----------|-------------|
| Subject Matter | Within eligible categories for engineering mechanisms, or outside but directly affecting structural operations |
| Component references | Cited figures, part numbers, and structural interactions must exactly match the latest engineering schematics |
| Problem solved | Claimed benefits consistent with the application's own analysis, no self-contradiction, no exaggeration |
| Enablement | Pinpoint specific structural features and explain why they solve the technical problem |
| Claims clarity | End-to-end structural definition of the mechanism from a legal perspective (must not use purely functional language without underlying physical structure to define the invention) |
| Utility | Mechanism operates end-to-end without requiring the patent_examiner to manually infer missing linkages; structural claims must be clear and supported by a specific, substantial, and credible utility |
| Novelty | Search patent databases, academic journals, and public technical manuals to confirm the exact arrangement is not already disclosed or already patented officially |
| Non-obviousness | The synergistic effect must exceed the baseline of "a routine combination of known engineering principles" |

If any of the following fatal flaws are present, flag explicitly and deduct:

- References to non-existent components or AI-hallucinated physical interactions
- Problem solved description contradicts the application's own analysis
- Mechanism relies on impossible physics or theoretical materials to simulate operability
- Component references inconsistent with the latest engineering schematics
- Detailed description is incomplete and requires the patent_examiner to manually infer the assembly
- The mechanism is a routine combination of known parts, with no synergistic effect
</f4>

<f5>
XML tags help Claude parse complex prompts unambiguously, especially when your prompt mixes instructions, context, examples, and variable inputs. Wrapping each type of content in its own tag (e.g. `<instructions>`, `<context>`, `<input>`) reduces misinterpretation.

Best practices:
- Use consistent, descriptive tag names across your prompts.
- Nest tags when content has a natural hierarchy (documents inside `<documents>`, each inside `<document index="n">`).
</f5>

<f6>
You are participating in a rigorous patent drafting and examination pipeline for highly complex engineering mechanisms.

Read the patent office guidelines first: ./uspto-guidelines.md
</f6>

<f7>
Before starting work, read inventions.xml.

1. Each concept's notes contain the patent_examiner's **quality score (0-10)**. A score of 0 means the concept is dead — don't touch it again. `none` means it hasn't been attempted yet.
2. Prefer concepts with `attempts="0"` and `score="none"` first.
3. Otherwise sort by UCB value: `UCB = score + 10 × √(ln(N) / attempts)`, where N is the sum of all attempts; higher score with fewer attempts ranks first.
4. If no concept is worth trying, search the engineering schematics for new concepts, append them to inventions.xml, then pick one to start.

**Recording rule during exploration**

While exploring engineering schematics, the moment you spot any potential new mechanism — even if you're unsure it holds — immediately append it to inventions.xml (`score="none"`, `attempts=0`). Don't wait until the exploration is done to batch-record, and don't evaluate patentability before recording. Recording ≠ committing to attempt it, just preventing forgetting. Evaluation belongs to the selection phase.
</f7>

Each agent's prompt should contain only the information that agent needs to do its job. For each sub-agent (patent_attorney, patent_examiner), which items should go in its prompt? Respond with a JSON object like {"patent_attorney": ["f?"], "patent_examiner": ["f?"]}. No other output.

I need you to set up a 2-agent pipeline for patent application drafting. The two roles are patent_attorney and patent_examiner: patent_attorney is responsible for drafting patent applications from engineering concepts, patent_examiner is responsible for judging whether what the patent_attorney drafted counts as a valid, novel, and non-obvious patent. The flow is roughly:

```
WHILE TRUE:
    1. patent_attorney picks a concept it likes and explores it deeply, or appends more concepts to inventions.xml and then picks one to draft.
    2. patent_attorney spawns a **fresh** patent_examiner to examine it.
```

I'm providing the following prompt fragments:

<f1>
This technical field has been heavily researched for many years — any obvious engineering mechanism has long been patented. So:

- Don't draft claims around obvious combinations of known elements (e.g. "attaching a standard motor to a wheel") — if it were that obvious, prior art would have covered it long ago
- Truly patentable inventions hide in non-obvious configurations, novel material interactions, and unexpected synergistic effects

**Drafting the application**

Each concept corresponds to its own application file at `applications/{id}-{slug}.md`, where `{id}` and `{slug}` come from the concept's id and slug attributes in inventions.xml.

The application must contain four h2 sections in this fixed order:

- `## Invention Title` — concise, clear title of the mechanism
- `## Problem Solved` — what technical challenge it overcomes (efficiency bottlenecks, wear, thermal exhaustion, etc.)
- `## Summary of the Invention` — short writeup of how it works, with figure/component references
- `## Detailed Description` — a clear, enabling disclosure of the mechanism that the patent_examiner can use as-is to verify feasibility

Do not modify or delete the `## Office Action` section — the patent_examiner overwrites that themselves.

If after investigation you find the concept isn't patentable, still briefly record your analysis and the reason for rejection in the application file.
</f1>

<f2>
After drafting an application, submit it to a **fresh** patent_examiner for examination. Do not examine your own application.

**After the examination returns:** execute all action steps in the patent_examiner's output. Then immediately go back to step 1: re-read inventions.xml and pick the next concept by UCB to keep working. Don't wait for user instructions, don't stop. One round = draft application → examination → execute actions → back to step 1.
</f2>

<f3>
After completing the examination, you must do two things:

1. Update the application file's Office Action section

At the end of the application file, write or overwrite the `## Office Action` section (always keep exactly one; replace it if it already exists). Follow this structure:

```
## Office Action

**Score: X/10** (one-sentence reason)

### Fatal flaws (if any)
- 🔴 ...

### Per-dimension examination

| Dimension | Verdict | Notes |
|-----------|---------|-------|
| Subject Matter | pass/fail | ... |
| Component references | pass/fail | ... |
| Problem solved | pass/fail | ... |
| Enablement | pass/fail | ... |
| Claims clarity | pass/fail | ... |
| Utility | pass/fail | ... |
| Novelty | pass/fail | ... |
| Non-obviousness | pass/fail | ... |

### Improvement suggestions
Be precise about which part of the application needs changes. If the concept doesn't hold at all, explicitly recommend abandoning it.
```

2. Update the concept's status in inventions.xml: increment attempts by 1, then update score.
</f3>

<f4>
Check the following dimensions one by one:

| Dimension | Requirement |
|-----------|-------------|
| Subject Matter | Within eligible categories for engineering mechanisms, or outside but directly affecting structural operations |
| Component references | Cited figures, part numbers, and structural interactions must exactly match the latest engineering schematics |
| Problem solved | Claimed benefits consistent with the application's own analysis, no self-contradiction, no exaggeration |
| Enablement | Pinpoint specific structural features and explain why they solve the technical problem |
| Claims clarity | End-to-end structural definition of the mechanism from a legal perspective (must not use purely functional language without underlying physical structure to define the invention) |
| Utility | Mechanism operates end-to-end without requiring the patent_examiner to manually infer missing linkages; structural claims must be clear and supported by a specific, substantial, and credible utility |
| Novelty | Search patent databases, academic journals, and public technical manuals to confirm the exact arrangement is not already disclosed or already patented officially |
| Non-obviousness | The synergistic effect must exceed the baseline of "a routine combination of known engineering principles" |

If any of the following fatal flaws are present, flag explicitly and deduct:

- References to non-existent components or AI-hallucinated physical interactions
- Problem solved description contradicts the application's own analysis
- Mechanism relies on impossible physics or theoretical materials to simulate operability
- Component references inconsistent with the latest engineering schematics
- Detailed description is incomplete and requires the patent_examiner to manually infer the assembly
- The mechanism is a routine combination of known parts, with no synergistic effect
</f4>

<f5>
XML tags help Claude parse complex prompts unambiguously, especially when your prompt mixes instructions, context, examples, and variable inputs. Wrapping each type of content in its own tag (e.g. `<instructions>`, `<context>`, `<input>`) reduces misinterpretation.

Best practices:
- Use consistent, descriptive tag names across your prompts.
- Nest tags when content has a natural hierarchy (documents inside `<documents>`, each inside `<document index="n">`).
</f5>

<f6>
You are participating in a rigorous patent drafting and examination pipeline for highly complex engineering mechanisms.

Read the patent office guidelines first: ./uspto-guidelines.md
</f6>

<f7>
Before starting work, read inventions.xml.

1. Each concept's notes contain the patent_examiner's **quality score (0-10)**. A score of 0 means the concept is dead — don't touch it again. `none` means it hasn't been attempted yet.
2. Prefer concepts with `attempts="0"` and `score="none"` first.
3. Otherwise sort by UCB value: `UCB = score + 10 × √(ln(N) / attempts)`, where N is the sum of all attempts; higher score with fewer attempts ranks first.
4. If no concept is worth trying, search the engineering schematics for new concepts, append them to inventions.xml, then pick one to start.

**Recording rule during exploration**

While exploring engineering schematics, the moment you spot any potential new mechanism — even if you're unsure it holds — immediately append it to inventions.xml (`score="none"`, `attempts=0`). Don't wait until the exploration is done to batch-record, and don't evaluate patentability before recording. Recording ≠ committing to attempt it, just preventing forgetting. Evaluation belongs to the selection phase.
</f7>

Each agent's prompt should contain only the fragments that agent needs to do its job. When you include a fragment, paste the FULL block verbatim — no paraphrasing, no partial inclusion, no rewriting of fragment content. Brief connective text between fragments (e.g., "Then: ...", "Note: ...") is fine. Format: one markdown section per role, with the role name as an h1 header (e.g. `# patent_attorney`). Output only the headered prompts, no preamble.


I need you to set up a 2-agent pipeline for pharmaceutical clinical trial data submission. The two roles are clinical_researcher and regulatory_reviewer: clinical_researcher is responsible for analyzing trial data to establish drug efficacy and identify safety signals, while regulatory_reviewer is responsible for strictly auditing the submission to uncover statistical p-hacking, hidden safety signals, and flawed methodology. The flow is roughly:

```
WHILE TRUE:
    1. clinical_researcher picks a hypothesis it likes and explores it deeply, or appends more hypotheses to hypotheses.xml and then picks one to explore.
    2. clinical_researcher spawns a **fresh** regulatory_reviewer to evaluate it.
```

I'm providing the following information:

<f1>
After compiling a submission report, submit it to a **fresh** regulatory_reviewer for review. Do not review your own report.

**After the review returns:** execute all action steps in the regulatory_reviewer's output. Then immediately go back to step 1: re-read hypotheses.xml and pick the next hypothesis by UCB to keep working. Don't wait for user instructions, don't stop. One round = write report → review → execute actions → back to step 1.
</f1>

<f2>
Claude responds well to clear, explicit instructions. Being specific about your desired output can help enhance results. If you want "above and beyond" behavior, explicitly request it rather than relying on the model to infer this from vague prompts.

Think of Claude as a brilliant but new employee who lacks context on your norms and workflows. The more precisely you explain what you want, the better the result.

Topology rule: Show your prompt to a colleague with minimal context on the task and ask them to follow it. If they'd be confused, Claude will be too.

- Be specific about the desired output format and constraints.
- Provide instructions as sequential steps using numbered lists or bullet points when the order or completeness of steps matters.
</f2>

<f3>
Before starting work, read hypotheses.xml.

1. Each hypothesis's notes contain the regulatory_reviewer's **quality score (0-10)**. A score of 0 means the hypothesis is dead — don't touch it again. `none` means it hasn't been attempted yet.
2. Prefer hypotheses with `attempts="0"` and `score="none"` first.
3. Otherwise sort by UCB value: `UCB = score + 10 × √(ln(N) / attempts)`, where N is the sum of all attempts; higher score with fewer attempts ranks first.
4. If no hypothesis is worth trying, search the clinical trial database and literature for new hypotheses, append them to hypotheses.xml, then pick one to start.

**Recording rule during exploration**

While exploring trial data, the moment you spot any potential new efficacy or safety signal — even if you're unsure it holds — immediately append it to hypotheses.xml (`score="none"`, `attempts=0`). Don't wait until the exploration is done to batch-record, and don't evaluate feasibility before recording. Recording ≠ committing to attempt it, just preventing forgetting. Evaluation belongs to the selection phase.
</f3>

<f4>
Check the following dimensions one by one:

| Dimension | Requirement |
|-----------|-------------|
| Scope | Within the pre-specified primary or secondary endpoints, or explicitly declared as an exploratory post-hoc analysis |
| Data references | Cited datasets, variable names, and patient counts must exactly match the locked trial database |
| Impact description | Clinical impact claim consistent with the report's statistical results, no self-contradiction, no exaggeration |
| Statistical rigor | Pinpoint specific models used, verify assumptions (e.g., proportional hazards, normal distribution), and check for signs of p-hacking |
| Reproducibility script | End-to-end analysis script generating the exact p-values and confidence intervals from the raw data (must not manually alter or impute missing data without declaring) |
| Execution | Script runs end-to-end without manual adjustments by the regulatory_reviewer; standard models must run within < 10 minutes on the provided compute environment |
| Deduplication | Search previous submission documents, interim analyses, and protocol amendments to confirm this isn't a recycled or already rejected hypothesis |
| Clinical significance | The effect size must exceed the minimal clinically important difference, not just achieving bare statistical significance |

If any of the following fatal flaws are present, flag explicitly and deduct:

- References to nonexistent datasets or AI-hallucinated variable names
- Impact description contradicts the report's own statistical results
- Reproducibility script achieves significance by silently dropping unfavorable patient cohorts (p-hacking)
- Data references inconsistent with the locked trial database
- Analysis script is incomplete and requires the regulatory_reviewer to manually assemble or debug it
- The effect is statistically significant but clinically meaningless, with no actual patient benefit
</f4>

<f5>
You are participating in a pharmaceutical clinical trial data submission and strict regulatory review process.

Read the regulatory submission guidelines first: ./regulatory-guidelines.md
</f5>

<f6>
After completing the review, you must do two things:

1. Update the report file's Regulatory Review section

At the end of the report file, write or overwrite the `## Regulatory Review` section (always keep exactly one; replace it if it already exists). Follow this structure:

```
## Regulatory Review

**Score: X/10** (one-sentence reason)

### Fatal flaws (if any)
- 🔴 ...

### Per-dimension review

| Dimension | Verdict | Notes |
|-----------|---------|-------|
| Scope | pass/fail | ... |
| Data references | pass/fail | ... |
| Impact description | pass/fail | ... |
| Statistical rigor | pass/fail | ... |
| Reproducibility script | pass/fail | ... |
| Execution | pass/fail | ... |
| Deduplication | pass/fail | ... |
| Clinical significance | pass/fail | ... |

### Improvement suggestions
Be precise about which part of the methodology or report needs changes. If the hypothesis is statistically invalid or unsafe, explicitly recommend abandoning it.
```

2. Update the hypothesis's status in hypotheses.xml: increment attempts by 1, then update score.
</f6>

<f7>
This clinical trial has been analyzed extensively for years — any obvious major efficacy signal without safety trade-offs would have been proven long ago. So:

- Don't report findings that are superficial or ignore key covariates (e.g., "the raw mean is higher in the treatment group") — if it were that straightforward, the sponsor would have already submitted it.
- Truly valuable insights and critical safety signals hide in complex subgroup interactions, longitudinal data trends, and boundary conditions of the statistical models.

**Writing the submission report**

Each hypothesis corresponds to its own report file at `reports/{id}-{slug}.md`, where `{id}` and `{slug}` come from the hypothesis's id and slug attributes in hypotheses.xml.

The report must contain four h2 sections in this fixed order:

- `## Report Title` — concise, clear summary of the clinical finding
- `## Clinical Impact Description` — what this means for patient outcomes (efficacy benefits, adverse event risks, therapeutic window, etc.)
- `## Statistical Description` — short writeup of the methodology used, with dataset/variable references
- `## Reproduction Details` — a runnable SAS/R script the regulatory_reviewer can execute as-is to reproduce the statistical analysis

Do not modify or delete the `## Regulatory Review` section — the regulatory_reviewer overwrites that themselves.

If after investigation you find the hypothesis doesn't yield statistically significant or clinically meaningful results, still briefly record your analysis and the reason for rejection in the report file.
</f7>

Each agent's prompt should contain only the information that agent needs to do its job. For each sub-agent (clinical_researcher, regulatory_reviewer), which items should go in its prompt? Respond with a JSON object like {"clinical_researcher": ["f?"], "regulatory_reviewer": ["f?"]}. No other output.

I need you to set up a 2-agent pipeline for pharmaceutical clinical trial data submission. The two roles are clinical_researcher and regulatory_reviewer: clinical_researcher is responsible for analyzing trial data to establish drug efficacy and identify safety signals, while regulatory_reviewer is responsible for strictly auditing the submission to uncover statistical p-hacking, hidden safety signals, and flawed methodology. The flow is roughly:

```
WHILE TRUE:
    1. clinical_researcher picks a hypothesis it likes and explores it deeply, or appends more hypotheses to hypotheses.xml and then picks one to explore.
    2. clinical_researcher spawns a **fresh** regulatory_reviewer to evaluate it.
```

I'm providing the following prompt fragments:

<f1>
After compiling a submission report, submit it to a **fresh** regulatory_reviewer for review. Do not review your own report.

**After the review returns:** execute all action steps in the regulatory_reviewer's output. Then immediately go back to step 1: re-read hypotheses.xml and pick the next hypothesis by UCB to keep working. Don't wait for user instructions, don't stop. One round = write report → review → execute actions → back to step 1.
</f1>

<f2>
Claude responds well to clear, explicit instructions. Being specific about your desired output can help enhance results. If you want "above and beyond" behavior, explicitly request it rather than relying on the model to infer this from vague prompts.

Think of Claude as a brilliant but new employee who lacks context on your norms and workflows. The more precisely you explain what you want, the better the result.

Topology rule: Show your prompt to a colleague with minimal context on the task and ask them to follow it. If they'd be confused, Claude will be too.

- Be specific about the desired output format and constraints.
- Provide instructions as sequential steps using numbered lists or bullet points when the order or completeness of steps matters.
</f2>

<f3>
Before starting work, read hypotheses.xml.

1. Each hypothesis's notes contain the regulatory_reviewer's **quality score (0-10)**. A score of 0 means the hypothesis is dead — don't touch it again. `none` means it hasn't been attempted yet.
2. Prefer hypotheses with `attempts="0"` and `score="none"` first.
3. Otherwise sort by UCB value: `UCB = score + 10 × √(ln(N) / attempts)`, where N is the sum of all attempts; higher score with fewer attempts ranks first.
4. If no hypothesis is worth trying, search the clinical trial database and literature for new hypotheses, append them to hypotheses.xml, then pick one to start.

**Recording rule during exploration**

While exploring trial data, the moment you spot any potential new efficacy or safety signal — even if you're unsure it holds — immediately append it to hypotheses.xml (`score="none"`, `attempts=0`). Don't wait until the exploration is done to batch-record, and don't evaluate feasibility before recording. Recording ≠ committing to attempt it, just preventing forgetting. Evaluation belongs to the selection phase.
</f3>

<f4>
Check the following dimensions one by one:

| Dimension | Requirement |
|-----------|-------------|
| Scope | Within the pre-specified primary or secondary endpoints, or explicitly declared as an exploratory post-hoc analysis |
| Data references | Cited datasets, variable names, and patient counts must exactly match the locked trial database |
| Impact description | Clinical impact claim consistent with the report's statistical results, no self-contradiction, no exaggeration |
| Statistical rigor | Pinpoint specific models used, verify assumptions (e.g., proportional hazards, normal distribution), and check for signs of p-hacking |
| Reproducibility script | End-to-end analysis script generating the exact p-values and confidence intervals from the raw data (must not manually alter or impute missing data without declaring) |
| Execution | Script runs end-to-end without manual adjustments by the regulatory_reviewer; standard models must run within < 10 minutes on the provided compute environment |
| Deduplication | Search previous submission documents, interim analyses, and protocol amendments to confirm this isn't a recycled or already rejected hypothesis |
| Clinical significance | The effect size must exceed the minimal clinically important difference, not just achieving bare statistical significance |

If any of the following fatal flaws are present, flag explicitly and deduct:

- References to nonexistent datasets or AI-hallucinated variable names
- Impact description contradicts the report's own statistical results
- Reproducibility script achieves significance by silently dropping unfavorable patient cohorts (p-hacking)
- Data references inconsistent with the locked trial database
- Analysis script is incomplete and requires the regulatory_reviewer to manually assemble or debug it
- The effect is statistically significant but clinically meaningless, with no actual patient benefit
</f4>

<f5>
You are participating in a pharmaceutical clinical trial data submission and strict regulatory review process.

Read the regulatory submission guidelines first: ./regulatory-guidelines.md
</f5>

<f6>
After completing the review, you must do two things:

1. Update the report file's Regulatory Review section

At the end of the report file, write or overwrite the `## Regulatory Review` section (always keep exactly one; replace it if it already exists). Follow this structure:

```
## Regulatory Review

**Score: X/10** (one-sentence reason)

### Fatal flaws (if any)
- 🔴 ...

### Per-dimension review

| Dimension | Verdict | Notes |
|-----------|---------|-------|
| Scope | pass/fail | ... |
| Data references | pass/fail | ... |
| Impact description | pass/fail | ... |
| Statistical rigor | pass/fail | ... |
| Reproducibility script | pass/fail | ... |
| Execution | pass/fail | ... |
| Deduplication | pass/fail | ... |
| Clinical significance | pass/fail | ... |

### Improvement suggestions
Be precise about which part of the methodology or report needs changes. If the hypothesis is statistically invalid or unsafe, explicitly recommend abandoning it.
```

2. Update the hypothesis's status in hypotheses.xml: increment attempts by 1, then update score.
</f6>

<f7>
This clinical trial has been analyzed extensively for years — any obvious major efficacy signal without safety trade-offs would have been proven long ago. So:

- Don't report findings that are superficial or ignore key covariates (e.g., "the raw mean is higher in the treatment group") — if it were that straightforward, the sponsor would have already submitted it.
- Truly valuable insights and critical safety signals hide in complex subgroup interactions, longitudinal data trends, and boundary conditions of the statistical models.

**Writing the submission report**

Each hypothesis corresponds to its own report file at `reports/{id}-{slug}.md`, where `{id}` and `{slug}` come from the hypothesis's id and slug attributes in hypotheses.xml.

The report must contain four h2 sections in this fixed order:

- `## Report Title` — concise, clear summary of the clinical finding
- `## Clinical Impact Description` — what this means for patient outcomes (efficacy benefits, adverse event risks, therapeutic window, etc.)
- `## Statistical Description` — short writeup of the methodology used, with dataset/variable references
- `## Reproduction Details` — a runnable SAS/R script the regulatory_reviewer can execute as-is to reproduce the statistical analysis

Do not modify or delete the `## Regulatory Review` section — the regulatory_reviewer overwrites that themselves.

If after investigation you find the hypothesis doesn't yield statistically significant or clinically meaningful results, still briefly record your analysis and the reason for rejection in the report file.
</f7>

Each agent's prompt should contain only the fragments that agent needs to do its job. When you include a fragment, paste the FULL block verbatim — no paraphrasing, no partial inclusion, no rewriting of fragment content. Brief connective text between fragments (e.g., "Then: ...", "Note: ...") is fine. Format: one markdown section per role, with the role name as an h1 header (e.g. `# clinical_researcher`). Output only the headered prompts, no preamble.