self-improvement-ci

name	self-improvement-ci
description	CI-only self-improvement workflow using gh-aw (GitHub Agentic Workflows). Captures recurring failure patterns and quality signals from pull request checks, emits structured learning candidates, and proposes durable prevention rules without interactive prompts. Use when: you want automated learning capture in CI/headless pipelines.

Self-Improvement CI

Install

npx skills add pskoett/pskoett-ai-skills/skills/self-improvement-ci

Purpose

Run self-improvement in CI without interactive chat loops:

• Inspect PR check results and CI failures
• Ingest learning candidates from simplify-and-harden-ci
• Deduplicate recurring patterns by stable pattern_key
• Emit promotion-ready suggestions for agent context/system prompts

Use self-improvement for interactive/local sessions.

Context Limitation (Important)

CI agents do not have peak task context from the original implementation session. Use this skill to aggregate recurring patterns across runs, not to infer nuanced one-off intent.

Implications:

• Favor stable pattern_key recurrence signals over single-run conclusions
• Require recurrence thresholds before promotion
• Route uncertain or high-impact recommendations to interactive review

Prerequisites

1. GitHub Actions enabled for the repository
2. GitHub CLI authenticated (gh auth status)
3. gh-aw installed for authoring/validation:

gh extension install github/gh-aw

CI Contract

The CI skill must:

1. Read only PR-scoped data (checks, workflow outcomes, existing learning entries)
2. Avoid direct code modifications in CI
3. Emit machine-readable learning output
4. Recommend promotion only when recurrence thresholds are met

Output Schema

self_improvement_ci:
  source:
    pr_number: 123
    commit_sha: "abc123"
  candidates:
    - pattern_key: "harden.input_validation"
      source: "simplify-and-harden-ci"
      recurrence_count: 3
      first_seen: "2026-02-01"
      last_seen: "2026-02-20"
      severity: "high"
      suggested_rule: "Validate and bound-check external inputs before use."
      promotion_ready: true
  summary:
    candidates_total: 4
    promotion_ready_total: 1
    followup_required: true

Recurrence and Promotion Rules

• Track recurrence by pattern_key
• Default threshold for promotion:
  • recurrence_count >= 3
  • seen in >= 2 distinct tasks/runs
  • within a 30-day window
• Promotion targets:
  • CLAUDE.md
  • AGENTS.md
  • .github/copilot-instructions.md
  • SOUL.md / TOOLS.md when using openclaw workspace memory

Authoring Workflow (gh-aw)

Example-only templates live in references/workflow-example.md. Keep examples outside .github/workflows until you explicitly decide to enable CI automation.

When ready:

1. Copy the template into .github/workflows/self-improvement-ci.md
2. Customize tool access, outputs, and policy thresholds
3. Validate:

gh aw compile --validate --strict

4. Trigger test run manually:

gh aw run self-improvement-ci --push

Integration with Other Skills

• Pair with simplify-and-harden-ci to ingest simplify_and_harden.learning_loop.candidates
• Feed promoted patterns back into self-improvement memory workflow for durable prevention rules