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deepswe-analyze

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Updated 10-15 22:16

About this skill

deepswe-analyze

One command that reproduces the 2026-09-08 DeepSWE analysis on a new week's results. Everything numeric is recomputed from the trials; nothing is copied from a previous week.

Inputs

  • repo — the analysis checkout, default /w/seungpil.lee/deepswe (harbor 0.22, .venv, deepswe/ package, refresh.sh).
  • week — meeting id YYYY-MM-DD. Data goes to site/weeks/<week>/, prose to docs/weeks/<week>/.
  • jobs — harbor job dirs for this week (jobs/<name>/<task>__<trial>/). Trials from other scaffolds or benchmarks are fine as long as collect.py has an adapter for them (see "Adapters").
  • Optional: --sample-wins N (judge only N passing trials per model; default all), --crosscheck N (default 30).

Procedure — run every step, in order, and do not stop early

  1. Briefs — scripts/pipeline.sh <repo> <week> briefs <jobs...> Writes jobs/analysis-briefs/<id>.md for failed trials (task statement, reference patch, model patch, failing tests, the FULL transcript with [call N] markers) and jobs/analysis-wins/<id>.md for passing ones. jobs/analysis-briefs/pending.json lists ids that still need a verdict.
  2. Failure verdicts — launch the Workflow in references/workflows/1-failure-judge.js with args = pending ids, or args = {"file": "<path to a one-id-per-line text file>", "count": N} when the list is long: each agent reads only its own line with sed, so no id list passes through any context. Do not try to have an agent echo the list back — 1,800 ids exceeds the output cap. One Sonnet agent per trial, effort: high. Judge every failed trial — do not sample — unless the user asks for a sample. The agent must read the whole brief in chunks and write jobs/analysis/<id>.json with the 13-key schema in that script. Prefix every prompt with "Do this work YOURSELF. Do not spawn sub-agents." — without it Sonnet delegates and writes nothing.
  3. Validate — scripts/pipeline.sh <repo> <week> validate. Removes verdicts that fail the schema or the self-consistency rules in deepswe/analyze.py (inconsistency()); re-run step 2 for the removed ids.
  4. Taxonomy relabel — Workflow references/workflows/2-taxonomy-relabel.js. Adds the taxonomy block (root cause / phase / post-failure behaviour, definitions in references/taxonomy.md, provenance arXiv 2607.09510 and 2509.13941). Labels come from the verdict evidence; keep confidence.
  5. Success verdicts + contrast pairs — Workflow references/workflows/3-winners-contrast.js. Success judges MUST carry the calibration line "passing does NOT prove the requirements were met" (without it every trial came back 100% covered). Contrast pairs: same model, same repo or task, one pass one fail, judged by Sonnet with decisive_factor in {approach_differed, verification_differed, task_was_harder, luck}.
  6. Agreement check — a second, blind Sonnet agent re-judges --crosscheck trials independently on the three taxonomy axes (same model family, separate context, no access to the first verdict); compute Cohen's kappa per axis. Report all three. If root-cause kappa < 0.6, stop and tell the user before publishing; do not silently proceed.
  7. Collect + deploy — scripts/pipeline.sh <repo> <week> collect <jobs...> then ... deploy <jobs...>. collect --week writes site/weeks/<week>/{trials,meta}.json, traj/, and the headline entry in site/weeks.json (the meetings home reads only that file).
  8. Weekly table — write docs/weeks/<week>/summary.md (three sentences, insight first) and notes.md with: pass-rate table vs leaderboard, root-cause / phase distribution with the change from the previous week, contrast decisive-factor split, kappa row, incidents, decisions needed. Numbers only from site/weeks/<week>/trials.json. Then bash refresh.sh <week> once more so the home card picks up the summary.

Adapters (extend before running a new scaffold or benchmark)

deepswe/collect.py assumes mini-swe-agent trajectories (messages with one shell command per turn) and DeepSWE task metadata (task.toml → language, category, gold patch under solution/). For a new scaffold add a turns_from_* reader that yields the same (turn, think, calls[phase,cmd,out]) shape and tag each trial with scaffold; for a new benchmark add a task_meta reader and tag benchmark. The viewer filters and the findings cards work off those fields unchanged.

Guard rails learned the hard way

  • Never run collect with a subset of job dirs into an existing week folder: it overwrites trials.json.
  • Judges see the outcome; failure-side and success-side prompts both need the calibration warning.
  • post_failure_behaviour had kappa 0.29 when labelled from extracted evidence instead of the raw transcript; treat it as reference-only unless relabelled from the full transcript.
  • Duplicate (task, model) trials (smoke + full) must be marked superseded, or per-model counts drift.
  • Do not grep site/index.html; some tools treat it as binary and return nothing. Use Python.

Output the skill returns to the caller

A markdown block: per-model pass rate, root-cause family split, phase split, contrast split, kappa per axis, count of trials judged / removed / re-judged, site URL for ?week=<week>, and the open decisions.