self-improvement-ci
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.
What this skill does
# Self-Improvement CI
## Install
```bash
gh skill install pskoett/pskoett-skills self-improvement-ci
```
Fallback using the Agent Skills CLI:
```bash
npx skills add pskoett/pskoett-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:
```bash
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
```yaml
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:
```bash
gh aw compile --validate --strict
```
4. Trigger test run manually:
```bash
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
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