Agent skill · pskoett
learning-aggregator-ci
[Beta] CI-only learning aggregation workflow using gh-aw (GitHub Agentic Workflows). Scans .learnings/ files on a schedule, groups entries by pattern_key, identifies promotion-ready patterns, and posts a gap report as a PR or issue comment. Use when: you want automated cross-session pattern detection in CI/headless pipelines without interactive prompts. For interactive use, use learning-aggregator.
What it needs
About 5k tokens when loaded.
What this skill does
Learning Aggregator CI Install For interactive sessions, use: Fallback using the Agent Skills CLI: Purpose Runs the outer loop's inspect step in CI. Reads accumulated .learnings/ files, groups entries by patternkey, computes cross-session recurrence, and produces a ranked gap report — all without human interaction. The interactive learning-aggregator skill is designed for in-session use where the user can review and act on findings immediately. This CI variant runs on a schedule (weekly, per-sprint, or on-demand) and posts its findings as a GitHub issue comment for async review. Context Limitation (Important) CI agents do not have session context. They cannot see what the user is currently working on or what task area is relevant. The CI variant scans all .learnings/ entries without relevance filtering. The gap report is comprehensive rather than targeted. Prerequisites GitHub Actions enabled on the repository gh CLI authenticated with repo access gh-aw extension installed (gh extension install github/gh-aw, v0.40.1+) .learnings/ directory with structured entries from self-improvement CI Contract Hard rules for headless execution: 1. Read-only — do not modify .learnings/ files, project instruction files (CLAUDE.md, AGENTS.md, .github/copilot-instructions.md), or any repo files 2. Headless — no interactive prompts, no approval gates 3. Structured output — emit findings as YAML under learningaggregatorci key 4. Single comment — post one consolidated comment per run, not per finding 5. Deterministic — same .learnings/ state produces the same gap report Authoring Workflow (gh-aw) 1. Copy references/workflow-example.md into .github/workflows/learning-aggregator-ci.md 2. Customize the schedule for your cadence (supports fuzzy schedules like weekly on mondays) 3. Validate: gh aw compile (optionally add --actionlint --zizmor for full security scan) 4. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills pskoett/learning-aggregator-ci--2034e6