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[deep-research] Deep Research: Pattern Library Analysis (2026-09-28 snapshot)

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Assessment

Difficulty
1/5
Estimated time
Under an hour
Newbie friendliness
65/100
Issue type
Documentation
Clarity
Clearly specified
Activity status
Active
Tech stack
github-actions, javascript
Domain
data

Research direction

This is a read-only summary of patterns/manifest.json and patterns/archetypes/*.json, not a request to change code. Start by inspecting those committed inputs and verify the reported counts or findings against them; the issue says all files parsed successfully. Done means recording the analysis in the run log without modifying repository files.

Written by the indexing model from the issue text.

Description

deep-research

Read-only analysis of the committed pattern library (patterns/manifest.json + patterns/archetypes/*.json), generated 2026-09-28 from 563 source repos / 345 active workflows / 929 total workflows scanned. No repository writes were made; this issue is purely a summary for the run log.

Archetype coverage (32 files, 31 user-facing + hidden custom)

Empirical archetypes (count > 0), by success rate:

Archetype Success Rate Runs (n)
content-moderation 100% 6
issue-triage 57% 94
documentation-updater 57% 24
repo-maintainer 55% 21
daily-test-improver 53% 5
custom (hidden) 49% 469
pr-review 47% 80
status-report 45% 81
dependency-monitor 37% 62
code-improvement 32% 86
skill-pr-reviewer 30% 1

Curated archetypes with no measured runs yet (count: 0, retained per policy): accessibility-expert, agent-cost-tracker, backlog-drip, batched-ci-doctor, ci-failure-triage, code-health-auditor, community-digest, contribution-guidelines-checker, issue-hierarchy-manager, link-checker, linter-applier, linter-miner, linter-refiner, linter-workflows, nitpick-reviewer, performance-nut, pr-fix-assistant, pr-iteration-loop, repo-qa-assistant, security-scanner, user-simulator (21 archetypes).

custom (count: 469, 49% baseline) is excluded from the wizard's HOW-step archetype cards but kept as a supporting profile/match target.

Configuration defaults

  • No pinned default model (model: null) — premium models only selected deliberately.
  • Timeout defaults by trigger: schedule 30 min; issues, push, slash_command, workflow_run, discussion, pull_request all 15 min (archetypes may override upward).
  • Prompt size sweet spot: 3,000–8,000 bytes.

Trigger combinations (curated, 15 tracked)

All 15 tracked combos are marked "Recommended"; 13 of 15 hit 90–100% success. Notable:

  • permissions+schedule and lone reaction — 100% (n=20 each).
  • issues+reaction+stop-after — 100% (n=13).
  • bots+permissions+skip-if-match, manual-approval+pull_request+roles, cron+env+name+schedule+steps, etc. — 100% (n=10 each).
  • No sub-90% combo appears in this curated top-15 "Recommended" list.

Configuration profiles (archetype × trigger × safe-outputs detail, 20 entries)

Highlights of best/worst within archetype:

  • custom: schedule+skip-if-match → create-pull-request = 93.0% (n=43, best in dataset); schedule → create-issue = 53.6% (n=69); pull_request → add-comment = 60.0% (n=30).
  • status-report: schedule+stop-after → create-discussion = 97.5% (n=40, near-best overall) vs. plain schedule → create-issue = 47.1% (n=87) vs. adding mentions/allowed-github-references → 33.6% (n=143, degrades further).
  • repo-maintainer: permissions+reaction+slash_command+steps (rich safe-outputs incl. push-to-pull-request-branch) = 90.0% (n=30) vs. reaction+schedule+slash_command (no permissions/steps scoping) = 41.0% (n=39).
  • issue-triage: issues+reaction → add-comment+add-labels = 83.3% (n=24); issue_comment+issues → add-comment/messages/noop = 80.0% (n=30).
  • pr-review: pull_request → add-comment or review-comment variants = 71.4% (n=21 each) vs. slash_command → review-comment/messages = 20.0% (n=20).
  • dependency-monitor: schedule → create-pull-request = 36.4% (n=176, largest sample in the whole profile set, still underperforms).
  • documentation-updater: schedule → create-pull-request = 56.7% (n=30) vs. push → create-pull-request = 26.7% (n=30).
  • code-improvement: workflow_run → add-comment+create-issue = 21.7% (n=23, best code-improvement profile, still poor); schedule+skip-if-match → create-pull-request = 12.0% (n=83); stop-after+workflow_run (broad safe-output set) = 0.0% (n=20, confirms workflow_run chaining anti-pattern).

Anti-patterns (20 named, all 0% success, each seen in exactly 1 repo)

All are singleton-repo failures, clustered around: documentation/doc-sync variants (documentation-updater, documentation-audit, update-docs, doc-sync, docs-sync, daily-repo-status ×2, localize-pt, news-translate), broad unscoped mandates (dependabot-remediation, breaking-change-checker, ci-doctor, csharp-guideline-improver), issue-triage/pr-review singleton failures, a duplicate "update-github-info" pattern (seen in 2 different repos), and two Imperia360-specific patterns (imperia-market-prices, imperia-cross-auditor). degraded_workflows was an empty array in this manifest snapshot.

Research findings

  • Bimodal outcomes: 38% of workflows always succeed, 21% always fail, 41% mixed — averages are misleading.
  • DO NOT constraints: workflows with explicit boundary instructions are 61% more likely to be healthy (p=0.009).
  • Slash commands: act as dispatchers routing to target workflows via workflow_dispatch; their own execution metrics are recorded under the dispatched run, not the dispatcher.
  • workflow_run risk: chaining via workflow_run has only 13–16% success rate — prefer pre-steps or schedule, confirmed by the 0% code-improvement workflow_run+stop-after profile above.
  • Pre-steps help: workflows with pre-steps show +13pp internal and +5pp community activity.
  • Prompt size matters: active workflows have 35–48% larger prompts than inactive ones.
  • Template clones are fragile: ~32% of workflows are unmodified template copies, which underperform customized workflows.

Data integrity note

patterns/manifest.json is present and valid, and references all 32 archetype files under patterns/archetypes/, all of which parsed successfully and contained analyzable pattern data (either empirical success_rate/count data or curated defaults with count: 0). No missing-or-invalid-input condition was encountered.

This is a read-only reporting workflow; no code, workflow, or pattern files were modified.

Generated by 🔬 Deep Research · copilot · auto · 26.7 AIC · ⌖ 5.45 AIC · ⊞ 5.9K · ◷

Dominant language
JavaScript
Stars
5
Forks
1
Avg merge
1d 7h
Merged PRs (30d)
30

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