neo4j/aip-skillbench-cohort-ab-sonnet-aipv0_3a2

AIP-SkillBench — 16-task combined cohort (Sonnet, AIP v0.3a2)

Raw evaluation-run data.

These are the first-version (v0.3a2) runs of cohorts A and B. They surfaced two AIP-compilation failure modes that motivated a spec bump to v0.3a3; the corrected re-runs are in the companion 24-task v0.3a3 dataset.

What this is

A head-to-head evaluation of two skill formats on the same tasks:

• human-curated — the task's original human-authored skill (prose).
• aip-from-curated — that same human skill compiled into AIP (a schema-validated execution-graph representation), authored against AIP spec v0.3a2.

Two balanced 8-task cohorts (A, B), each task × mode run for 5 independent trials.

field	value
tasks	16 (cohorts A & B, 8 each)
modes	human-curated, aip-from-curated
trials	5 per task × mode
total runs	16 × 2 × 5 = 160
solver agent	claude-agent-acp
solver model	claude-sonnet-4-6
AIP authoring	claude-opus against AIP spec v0.3a2
sandbox	docker
benchmark	extends SkillsBench

Why these runs matter — the v0.3a2 → v0.3a3 bump

These runs exposed two ways the v0.3a2 AIP compilation could produce a worse skill than the human prose it was built from, both stemming from the authored script being frozen at compile time:

• offer-letter-generator (cohort A) — the converted skill's template-filling script encoded a conditional-handling bug (it resolved an {{IF_…}} block against the wrong data key), so a required section was dropped on every trial — a deterministic regression below the human skill, which reasons over the document instead.
• bike-rebalance (cohort B) — the conversion produced an over-engineered optimizer (~1,100+ lines) heavy enough to exceed the per-trial time budget on most runs, where the leaner human-written approach finished.

These two patterns — a frozen incorrect script, and a frozen over-engineered/slow script — motivated AIP spec changes in v0.3a3 (a script-vs-prose decision rule, leanness guidance, and a functional-correctness check during authoring). After those changes the cohorts were re-authored and re-run; see the v0.3a3 dataset linked above.

Layout

cohort-a/   cohort-b/                  # one folder per cohort, each:
  campaign.json     # the run matrix (tasks, modes, trials, model, agent)
  status.json       # run totals (done / pass / fail / error)
  summary.csv       # one row per trial — the primary table
  summary.jsonl     # same, JSON Lines
  cells/            # per-trial working dirs: rewards, timing, result.json, agent trajectory
  logs/             # per-trial solver logs

summary.csv columns: task, model, mode, trial, status, reward, n_tool_calls, wall_clock, error, jobs_dir, trial_dir, started_at, finished_at, subprocess_rc.

Source code, skills, and how to reproduce

The benchmark harness, run configs, and the AIP-compiled (v0.3a2) skills themselves live in the GitHub repo. Check out the matching tag to see the exact skills used for these runs:

• Repo: https://github.com/zach-blumenfeld/aip-skillbench
• Tag: sonnet-aipv0.3a2
• AIP-compiled skills: generated-skills/<task>/aip-from-curated/…
• Human skills: vendor/skillsbench/tasks/<task>/environment/skills/…
• Run configs: configs/eval-cohort-{a,b}-sonnet.yaml

git clone https://github.com/zach-blumenfeld/aip-skillbench
cd aip-skillbench && git checkout sonnet-aipv0.3a2

Reading the data

import pandas as pd
a = pd.read_csv("hf://datasets/neo4j/aip-skillbench-cohort-ab-sonnet-aipv0_3a2/cohort-a/summary.csv")