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 c

Self Improvement CI Install Fallback using the Agent Skills CLI: 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 Ingest Handoff blocks from .learnings/HEALS.md (filed by self healing / self healing ci ) and surface them as promotion candidates Deduplicate recurring patterns by stable pattern key Emit promotion ready suggestions for agent context/system prompts This skill is read only with respect to the repository (see CI Contract): it does not write .learnings/ entries. Its candidates are emitted as machine readable output, and promotions are proposed as a PR or comment for human review. 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: 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 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: 4. Trigger test run manually: Heal Handoff Intake self healing ci appends Handoff blocks to .learnings/HEALS.md entries that meet the promotion rule. On each run: 1. Read .learnings/HEALS.md (read only) and collect entries with a Handoff block 2. Map each to a candidate: pattern key from the HEAL's Pattern Key , suggested rule from the Distilled Rule , recurrence fields from the entry metadata 3. Mark promotion ready: true when the promotion rule holds, and include the candidate in the output schema alongside simplify and harden ci candidates 4. Propose the promotion (target file + rule text) as a PR or comment — never write instruction files directly from CI Integration with Other Skills Pair with simplify and harden ci to ingest simplify and harden.learning loop.candidates Pair with self healing ci , whose HEALS.md Handoff blocks this skill consumes (see Heal Handoff Intake) Feed promoted patterns back into self improvement memory workflow for durable prevention rules

self-improvement-ci