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[deep-research] Deep Research: gh-aw pattern library analysis (2026-08-31 snapshot)

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Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
20/100
Tipo de issue
Documentación
Claridad
Necesita aclaración
Estado de actividad
Activo
Stack tecnológico
github-actions
Área
documentation

Línea de trabajo

Comienza con patterns/manifest.json y compara los 29 archivos de patterns/archetypes/. Trata las métricas y los resultados de investigación proporcionados como la línea base actual; el issue no define ningún cambio solicitado ni criterios de aceptación, así que aclara el entregable previsto antes de modificar nada.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

deep-research

Deep Research: gh-aw Pattern Library Analysis

Source: patterns/manifest.json (generated 2026-08-31T09:42:11Z from 223 source repos, 175 active workflows, 671 total workflows scanned) plus 29 archetype files under patterns/archetypes/.

Archetype coverage
  • Manifest lists 29 archetypes with empirical run data ranging from count: 0 (curated, unmeasured) to count: 356 (custom, the largest bucket).
  • Archetypes with measured success (n>0), ranked:
    • daily-test-improver 100% (n=3)
    • documentation-updater 68% (n=9)
    • issue-triage 52% (n=72)
    • custom 50% (n=356, aggregate bucket)
    • dependency-monitor 50% (n=48)
    • code-improvement 46% (n=73)
    • pr-review 42% (n=63)
    • status-report 38% (n=36)
    • repo-maintainer 33% (n=8)
    • content-moderation 0% (n=3)
  • 20 archetypes (e.g. accessibility-expert, security-scanner, linter-workflows, community-digest, pr-iteration-loop, etc.) have count: 0 — curated/no empirical runs yet, but each still carries a recommended-trigger profile, prompt-style guidance, and tips derived from design heuristics rather than measured outcomes.
Recommended configuration defaults (config_defaults)
  • No pinned model by default (model: null).
  • Timeout by trigger: schedule 30 min; issues, push, slash_command, workflow_run, discussion, pull_request 15 min each (archetypes may override upward, e.g. pr-iteration-loop at 45 min).
  • Prompt size sweet spot: 3,000–8,000 bytes; phase-based archetypes (code-improvement, daily-test-improver, repo-maintainer, pr-iteration-loop, linter-workflows, security-scanner) recommend larger 5,000–20,000 byte prompts.
Trigger combinations (trigger_combos, 15 tracked)
  • 13 of 15 combos are 90–100% success and marked "Recommended"; none are flagged as risky in the curated list.
  • Standouts: bots+check_suite+permissions+steps, reaction+schedule+stop-after, pull_request_target+roles+skip-bots, issues+roles+skip-if-no-match — all 100% (n=10 each).
  • Lone reaction trigger: 99% success (n=90), the largest sample size in the table.
  • Softest tracked combo: bots+roles+schedule+stale-check at 90% (n=20).
Configuration profiles (archetype × trigger × safe-output detail)

Notable contrasts within the same archetype:

  • code-improvement: schedule+skip-if-match → create-pull-request measured 0% (n=23); workflow_run → add-comment/create-issue also 0% (n=20). Confirms workflow_run chaining risk.
  • custom: schedule → create-pull-request+noop is the standout profile at 95.2% (n=21, confidence 0.773), followed by schedule → create-issue+noop+threat-detection at 83.9% (n=31) and schedule → create-issue+noop at 80.0% (n=20).
  • dependency-monitor: schedule → create-pull-request 37.5% (n=56); adding allowed-domains dropped to 31.0% (n=42).
  • issue-triage: issues+roles → add-comment+add-labels+assign-to-agent underperformed at 12.2% (n=82) — assignment step correlates with lower success.
  • status-report: schedule → create-issue 18.0% (n=61); adding mentions/allowed-github-references → 0% (n=40).
Anti-patterns (20 entries, all 0% success, each seen once)

All are single-repo, single-occurrence named patterns (e.g. daily-repo-status ×2, supply-chain-review ×2, ci-coach, code-simplifier, arm-api-review, hero-scenarios, data-plane-api-review, dash, tester, scribe, archie, sentinel, sdk-build-repair, ci-doctor, news-translate, localize-pt, issue-go-yes, issue-triage). Common theme: broad, unscoped daily/status/supply-chain/CI-coach mandates without narrow targets.

Research findings
  • Outcomes are bimodal: 38% of workflows always succeed, 21% always fail, 41% mixed — averages are misleading.
  • Explicit DO NOT constraints correlate with 61% higher health likelihood (p=0.009).
  • workflow_run chaining: 13–16% success — prefer pre-steps or schedule.
  • Pre-steps correlate with +13pp internal / +5pp community activity.
  • Active workflows have 35–48% larger prompts than inactive ones.
  • 32% of workflows are unmodified template clones, with lower success than customized ones.
Archetype-level tips worth highlighting
  • status-report, documentation-updater, code-improvement: use skip-if-match/expires/close-older-issues to avoid duplicate scheduled outputs.
  • dependency-monitor: explicitly list monitored ecosystems; add package-registry domains to network.allowed since "defaults"/"github" alone don't cover them.
  • pr-review: set tools.github.min-integrity to restrict acting on untrusted content.
  • security-scanner (0 runs, curated): scope to a bounded recent commit window, report as code-scanning alerts (not issues), call noop when clean.
  • pr-iteration-loop (0 runs, curated): persist iteration history in repo-memory, accept iterations only when they pass verification, pause after bounded retries.

No degraded_workflows were flagged in this manifest snapshot.

This is a read-only analysis of the committed pattern library only; no scanner was run and no other repository files were consulted.

Generated by 🔬 Deep Research · copilot · auto · 39.9 AIC · ⌖ 4.87 AIC · ⊞ 6.2K · ◷

Lenguaje dominante
JavaScript
Estrellas
6
Forks
1
Merge medio
1 d 13 h
PR fusionados (30 d)
21

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