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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-ci

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Listed on Skillselion
Installs499
repo stars272
Last updatedJune 12, 2026
Repositorypskoett/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

SKILL.mdMarkdownGitHub ↗

Self-Improvement CI

Install

gh skill install pskoett/pskoett-skills self-improvement-ci

Fallback using the Agent Skills CLI:

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
  • 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:

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

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

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.

Code Review & Qualitydevopsintegrationstesting

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