self-improvement-ci
Automated learning capture in CI pipelines that deduplicates failure patterns and proposes prevention rules.
- Inspects PR check results and CI failures to identify recurring patterns tracked by stable
pattern_key, promoting only when recurrence thresholds are met (3+ occurrences across 2+ distinct runs within 30 days) - Ingests learning candidates from
simplify-and-harden-ciand emits machine-readable YAML output without interactive prompts, suitable for headless GitHub Actions workflows - Promotes durable prevention rules to documentation targets like
CLAUDE.md,AGENTS.md, and.github/copilot-instructions.mdwhen recurrence signals justify it - Requires GitHub Actions, authenticated GitHub CLI, and
gh-awextension for workflow authoring and validation
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
More from pskoett/pskoett-ai-skills
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Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks. For CI-only/headless learning capture, use self-improvement-ci.
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454simplify-and-harden
Post-completion self-review for coding agents that runs simplify, harden, and micro-documentation passes on non-trivial code changes. Use when: a coding task is complete in a general agent session and you want a bounded quality and security sweep before signaling done. For CI pipeline execution, use simplify-and-harden-ci.
426plan-interview
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415intent-framed-agent
Frames coding-agent work sessions with explicit intent capture and drift monitoring. Use when a session transitions from planning/Q&A to implementation for coding tasks, refactors, feature builds, bug fixes, or other multi-step execution where scope drift is a risk.
378dx-data-navigator
Query Developer Experience (DX) data via the DX Data MCP server PostgreSQL database. Use this skill when analyzing developer productivity metrics, team performance, PR/code review metrics, deployment frequency, incident data, AI tool adoption, survey responses, DORA metrics, or any engineering analytics. Triggers on questions about DX scores, team comparisons, cycle times, code quality, developer sentiment, AI coding assistant adoption, sprint velocity, or engineering KPIs.
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