npx skills add pskoett/pskoett-skills/skills/learning-aggregator-ci
npx skills add pskoett/pskoett-skills/skills/learning-aggregator

Purpose

Runs the outer loop's inspect step in CI. Reads accumulated .learnings/ files, groups entries by pattern_key, 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:

Read-only — do not modify .learnings/ files, project instruction files (CLAUDE.md, AGENTS.md, .github/copilot-instructions.md), or any repo files
Headless — no interactive prompts, no approval gates
Structured output — emit findings as YAML under learning_aggregator_ci key
Single comment — post one consolidated comment per run, not per finding
Deterministic — same .learnings/ state produces the same gap report

Authoring Workflow (gh-aw)

Copy references/workflow-example.md into .github/workflows/learning-aggregator-ci.md
Customize the schedule for your cadence (supports fuzzy schedules like weekly on mondays)
Validate: gh aw compile (optionally add --actionlint --zizmor for full security scan)
Push to enable

Persistence and Chaining

cache-memory: stores aggregation state (pattern groups, recurrence counts) across runs. Survives up to 90 days in Actions cache. Avoids re-scanning unchanged entries on every run.
call-workflow: triggers eval-creator-ci after aggregation completes to create evals from newly promoted patterns. Compile-time fan-out with proper dependency wiring.
upload-artifact: persists the gap report YAML for consumption by downstream workflows or human review.

Workflow Rules

The CI agent follows these rules in order:

Read all files in .learnings/: LEARNINGS.md, ERRORS.md, FEATURE_REQUESTS.md, HEALS.md
Parse each entry's metadata: Pattern-Key, Recurrence-Count, First-Seen, Last-Seen, Priority, Status, Area, Related Files, Tags. For HEAL entries, also parse Trigger, Active-Context, and any Handoff block — Handoff blocks at the promotion threshold are promotion-ready by definition and must appear in the gap report
Group entries by Pattern-Key (exact match only — no fuzzy grouping in CI)
For each group: sum recurrences, count distinct tasks, compute time window, collect evidence
Flag entries without Pattern-Key as ungrouped
Classify each group's gap type: knowledge gap, tool gap, skill gap, ambiguity, or reasoning failure
Rank groups by: promotion-ready first, then approaching threshold, then by priority (critical > high > medium > low)
Emit structured YAML under key learning_aggregator_ci
Post gap report as a comment on the triggering issue or as a new issue if running on schedule
Do not modify repository files

Promotion threshold (same rule as learning-aggregator and self-improvement): a group is promotion-ready when Recurrence-Count >= 3, seen in >= 2 distinct tasks, within a 30-day window.

Output Schema

learning_aggregator_ci:
  version: "0.1.0"
  source:
    run_id: "<workflow run ID>"
    trigger: "schedule | workflow_dispatch | issue_comment"
    scan_date: "YYYY-MM-DD"
  scan:
    entries_total: 42
    entries_with_pattern_key: 35
    entries_ungrouped: 7
    patterns_found: 18
    promotion_ready: 3
    approaching_threshold: 5
  promotion_ready:
    - pattern_key: "harden.input_validation"
      recurrence_count: 5
      distinct_tasks: 3
      window_days: 21
      priority: "high"
      gap_type: "knowledge_gap"
      area: "backend"
      evidence:
        - "LRN-20260301-001: Missing bounds check on pagination params"
        - "ERR-20260308-002: Unconstrained string length caused OOM"
        - "LRN-20260315-003: API params not validated before DB query"
      recommended_action: "Add to project instruction files: Always validate and bound-check external inputs before use"
      eval_candidate: true
  approaching:
    - pattern_key: "simplify.dead_code"
      recurrence_count: 2
      distinct_tasks: 1
      priority: "low"
      needs: "1 more distinct task"
  ungrouped:
    - id: "LRN-20260320-005"
      summary: "Discovered undocumented rate limit on external API"
      recommendation: "Assign pattern_key for future tracking"
  stale:
    - pattern_key: "harden.error_handling"
      last_seen: "2025-12-01"
      recommendation: "Dismiss — not seen in 90+ days"
  summary:
    promotion_ready_total: 3
    approaching_total: 5
    ungrouped_total: 7
    stale_total: 1
    followup_required: true

Recommended Outputs

Output	Destination	Content
Gap report	Issue comment or new issue	Human-readable summary with promotion candidates and evidence
YAML artifact	Workflow artifact	Machine-readable `learning_aggregator_ci` payload
Check annotation	Check run summary	Count of promotion-ready and approaching patterns

Trigger Configuration

Recommended: weekly schedule + manual dispatch

on:
  schedule:
    - cron: '0 9 * * 1'  # Monday 9am UTC
  workflow_dispatch:
  issue_comment:
    types: [created]

The schedule ensures regular outer-loop cadence. Manual dispatch allows on-demand runs after incidents or sprints. Issue comment trigger allows /aggregate-learnings commands.

Integration with Other Skills

Upstream (feeds from)

self-improvement (interactive) — produces .learnings/LEARNINGS.md, ERRORS.md, FEATURE_REQUESTS.md entries
self-healing / self-healing-ci — produce .learnings/HEALS.md entries including Handoff blocks
self-improvement-ci — emits learning candidates as machine-readable output (artifacts/comments); it is read-only and does not write .learnings/ files itself
simplify-and-harden-ci — produces learning_loop.candidates consumed by self-improvement-ci

Downstream (feeds into)

harness-updater (interactive) — takes promotion-ready patterns from the gap report and applies them
eval-creator-ci — takes eval candidates and creates permanent test cases
Human review — gap report posted as issue comment for team triage

Data Flow

self-improvement → .learnings/*.md   ←  self-healing(-ci) → HEALS.md
                       ↓
              learning-aggregator-ci (scheduled)
                       ↓
              gap report (issue comment + artifact)
                       ↓
              harness-updater (interactive, human-gated)
                       ↓
              eval-creator-ci (creates evals from promoted patterns)

Differences from Interactive Version

Aspect	Interactive (`learning-aggregator`)	CI (`learning-aggregator-ci`)
Trigger	Manual or session-start	Scheduled cron or workflow_dispatch
Relevance filter	Filters by current task area	Scans all entries (no task context)
Grouping	Conservative + area/tag matching	Pattern-key exact match only
Output	In-session gap report	Issue comment + YAML artifact
Human interaction	User reviews inline	Async review via GitHub
Scope	Current session context	Full .learnings/ history

learning-aggregator-ci

Learning Aggregator CI

Install