
Self Improvement Ci
- 499 installs
- 272 repo stars
- Updated June 12, 2026
- pskoett/pskoett-ai-skills
self-improvement-ci is a pskoett-ai-skills workflow that captures recurring CI and PR check failure patterns via gh-aw and turns them into structured prevention rules for developers running headless agent learning in pip
About
self-improvement-ci is a pskoett/pskoett-ai-skills agent skill that runs self-improvement inside CI using GitHub Agentic Workflows (gh-aw) without interactive prompts. It inspects pull request check results, captures recurring failure patterns and quality signals, emits structured learning candidates, and proposes durable prevention rules for future agent runs. Install via `gh skill install pskoett/pskoett-skills self-improvement-ci` or `npx skills add pskoett/pskoett-skills/skills/self-improvement-ci`. Developers reach for self-improvement-ci when CI repeatedly surfaces the same lint, test, or review failures and they want automated learning capture instead of manual postmortems.
- Inspects PR check results and CI failures in headless mode
- Ingests learning candidates from simplify-and-harden-ci
- Processes Handoff blocks from .learnings/HEALS.md filed by self-healing and self-healing-ci
- Deduplicates recurring patterns using stable pattern_key
- Emits promotion-ready suggestions for agent context and system prompts as machine-readable output
Self Improvement Ci by the numbers
- 499 all-time installs (skills.sh)
- +5 installs in the week ending Aug 4, 2026 (Skillselion tracking)
- Ranked #236 of 1,352 Code Review & Quality skills by installs in the Skillselion catalog
- Data as of Aug 4, 2026 (Skillselion catalog sync)
npx skills add https://github.com/pskoett/pskoett-ai-skills --skill self-improvement-ciAdd your badge
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| Installs | 499 |
|---|---|
| repo stars | ★ 272 |
| Last updated | June 12, 2026 |
| Repository | pskoett/pskoett-ai-skills ↗ |
How do you capture CI failure patterns automatically?
Automatically capture recurring failure patterns from CI and PR checks and turn them into structured prevention rules for future agent runs.
Who is it for?
Teams using GitHub PR checks and gh-aw who want headless CI learning loops that harden agent behavior over time.
Skip if: Local-only development without CI pipelines or teams that want interactive chat-based improvement instead of automated capture.
When should I use this skill?
Pull request checks show recurring failures and you want automated learning capture and prevention rules in CI without interactive prompts.
What you get
Structured learning candidates and durable prevention rules derived from PR check history.
- Structured learning candidates
- Durable prevention rules
- CI-captured failure pattern report
Files
Self-Improvement CI
Install
gh skill install pskoett/pskoett-skills self-improvement-ciFallback using the Agent Skills CLI:
npx skills add pskoett/pskoett-skills/skills/self-improvement-ciPurpose
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
Handoffblocks from.learnings/HEALS.md(filed byself-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_keyrecurrence 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-awCI 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: trueRecurrence and Promotion Rules
- Track recurrence by
pattern_key - Default threshold for promotion:
recurrence_count >= 3- seen in
>= 2distinct tasks/runs - within a 30-day window
- Promotion targets:
CLAUDE.mdAGENTS.md.github/copilot-instructions.mdSOUL.md/TOOLS.mdwhen 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 --strict4. Trigger test run manually:
gh aw run self-improvement-ci --pushHeal 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-cito ingest
simplify_and_harden.learning_loop.candidates
- Pair with
self-healing-ci, whose HEALS.mdHandoffblocks this skill consumes (see Heal Handoff Intake) - Feed promoted patterns back into
self-improvementmemory workflow for durable prevention rules
Workflow Example (Non-Active)
This is an example template only. Keep it outside .github/workflows so nothing runs automatically.
When you are ready to enable CI automation: 1. Copy this template into .github/workflows/self-improvement-ci.md 2. Adjust thresholds and promotion policy 3. Validate with gh aw compile --validate --strict
---
on:
pull_request:
types: [opened, synchronize, reopened, ready_for_review]
workflow_dispatch:
permissions:
contents: read
actions: read
pull-requests: read
tools:
github:
toolsets: [pull_requests, actions]
safe-outputs:
add-comment:
max: 1
strict: true
---
Run Self-Improvement CI for this pull request.
Rules:
1) Collect failure and warning signals from relevant checks.
2) Ingest `simplify_and_harden.learning_loop.candidates` when available.
3) Deduplicate findings by `pattern_key`.
4) Emit structured YAML under key `self_improvement_ci` with recurrence metadata.
5) Recommend promotion when recurrence thresholds are met.
6) Do not modify repository files in CI.Related skills
FAQ
How do you install self-improvement-ci?
self-improvement-ci installs with `gh skill install pskoett/pskoett-skills self-improvement-ci` or fallback `npx skills add pskoett/pskoett-skills/skills/self-improvement-ci` for Agent Skills CLI users.
Does self-improvement-ci require interactive chat?
self-improvement-ci is CI-only and headless: it inspects PR check results via gh-aw, emits learning candidates, and proposes prevention rules without interactive agent prompts.