learning-aggregator

# Learning Aggregator Reads accumulated `.learnings/` files across all sessions, finds patterns, and produces a ranked list of promotion candidates. This is the outer loop's **inspect** step. Without this skill, `.learnings/` is a write-only log. Patterns accumulate but nobody synthesizes them. The same gap resurfaces two weeks later because no one looked. ## When to Use - **Weekly cadence** — scheduled or manual, review accumulated learnings - **Before major tasks** — check if the task area has known patterns - **After a burst of sessions** — consolidate findings from a sprint or incident - **When self-improvement flags `promotion_ready`** — verify the flag with full context ## What It Produces A **gap report** — a ranked list of patterns that have crossed (or are approaching) the promotion threshold, with evidence and recommended actions. ## Step 1: Read All Learning Files Read these files in `.learnings/`: | File | Contains | |------|----------| | `LEARNINGS.md` | Corrections, knowledge gaps, best practices, recurring patterns | | `ERRORS.md` | Command failures, API errors, exceptions | | `FEATURE_REQUESTS.md` | Missing capabilities | Parse each entry's metadata: - `Pattern-Key` — the stable deduplication key - `Recurrence-Count` — how many times this pattern has been seen - `First-Seen` / `Last-Seen` — date range - `Priority` — low / medium / high / critical - `Status` — pending / promotion_ready / promoted / dismissed - `Area` — frontend / backend / infra / tests / docs / config - `Related Files` — which parts of the codebase are affected - `Source` — conversation / error / user_feedback / simplify-and-harden - `Tags` — free-form labels ## Step 2: Group and Aggregate Group entries by `Pattern-Key`. For each group: 1. **Sum recurrences** across all entries with the same key 2. **Count distinct tasks** — how many different sessions/tasks encountered this 3. **Compute time window** — days between First-Seen and Last-Seen 4. **Collect all related files** — union of all entries' file references 5. **Take highest priority** across entries in the group 6. **Collect evidence** — the Summary and Details from each entry For entries without a `Pattern-Key`, use conservative grouping only: - **Exact match**: Same `Area` AND at least 2 identical `Tags` - **File overlap**: Same `Related Files` path (exact path match, not substring) - **Do NOT fuzzy-match** on Summary text — false groupings are worse than ungrouped entries Flag ungrouped entries separately with a recommendation to assign a `Pattern-Key`. Ungrouped entries are common and expected — they may be one-off issues or genuinely novel problems. ## Step 3: Rank and Classify ### Promotion Threshold An entry is **promotion-ready** when: - `Recurrence-Count >= 3` across the group - Seen in `>= 2 distinct tasks` - Within a `30-day window` ### Approaching Threshold An entry is **approaching** when: - `Recurrence-Count >= 2` or - `Priority: high/critical` with any recurrence ### Classification For each promotion candidate, classify the gap type: | Gap Type | Signal | Fix Target | |----------|--------|------------| | **Knowledge gap** | Agent didn't know X | Update project instruction files (CLAUDE.md, AGENTS.md, .github/copilot-instructions.md) | | **Tool gap** | Agent improvised around missing capability | Add or update MCP tool / script | | **Skill gap** | Same behavior pattern keeps failing | Create or update a skill (use `/skill-creator`, validate with `quick_validate.py`, register `skill-check` eval) | | **Ambiguity** | Conflicting interpretations of spec/prompt | Tighten instructions or add examples | | **Reasoning failure** | Agent had the knowledge but reasoned wrong | Add explicit decision rules or constraints | ## Step 4: Produce Gap Report Output a structured report: ```markdown ## Learning Aggregator: Gap Report **Scan date:** YYYY-MM-DD **Period:** [since date] to [now] **Entries scanned:** N **Patterns found:** N **Promotion-ready:** N **Approaching threshold:** N ### Promotion-Ready Patterns #### 1. [Pattern-Key] — [Summary] - **Recurrence:** N times across M tasks - **Window:** First-Seen → Last-Seen - **Priority:** high - **Gap type:** knowledge gap - **Area:** backend - **Related files:** path/to/file.ext - **Evidence:** - [LRN-YYYYMMDD-001] Summary of first occurrence - [LRN-YYYYMMDD-002] Summary of second occurrence - [ERR-YYYYMMDD-001] Summary of related error - **Recommended action:** Add rule to project instruction files (CLAUDE.md, AGENTS.md, .github/copilot-instructions.md): "[concise prevention rule]" - **Eval candidate:** Yes — [description of what to test] #### 2. ... ### Approaching Threshold #### 1. [Pattern-Key] — [Summary] - **Recurrence:** 2 times across 1 task - **Needs:** 1 more recurrence or 1 more distinct task - ... ### Ungrouped Entries (no Pattern-Key) - [LRN-YYYYMMDD-005] "Summary" — needs pattern_key assignment - ... ### Dismissed / Stale - Entries with Last-Seen > 90 days ago and Status: pending → recommend dismissal ``` ## Step 5: Handoff The gap report feeds into: 1. **harness-updater agent** — takes promotion-ready patterns and applies them to project instruction files (CLAUDE.md, AGENTS.md, .github/copilot-instructions.md) 2. **eval-creator skill** — takes eval candidates and creates permanent test cases 3. **Human review** — for patterns classified as "reasoning failure" or "ambiguity" (these need human judgment) ## Filtering - `--since YYYY-MM-DD` — only scan entries after this date - `--min-recurrence N` — raise the promotion threshold - `--area AREA` — filter to a specific area (frontend, backend, etc.) - `--deep` — also analyze session traces via Entire (see Session Trace Analysis below) ## Session Trace Analysis The outer loop reads from two complementary sources: | Source | What it is | Cadence | Cost | |--------|-----------|---------|------| | `.learnings/` | Explicit entries written by self-improvement during sessions. Agent's own reflections: corrections, knowledge gaps, recurring patterns it noticed. | Every session (hot path) | Near-zero | | Session traces | Full session transcripts captured by [Entire](https://entire.io): prompts, tool calls, outputs, files modified, token usage, checkpoints. | Weekly or on-demand (cold path) | Expensive — only run at cadence | The default mode reads `.learnings/` and produces a gap report from what the agent explicitly logged. The `--deep` mode also analyzes session traces and merges findings from both sources. ### Why both sources matter `.learnings/` captures what the agent **noticed and chose to log** — a curated subset. Session traces capture **everything that happened**, including patterns the agent worked around, retried, or never recognized as failures. Examples of patterns visible in traces but absent from `.learnings/`: - **Retry loops**: The same tool call repeated 3+ times with small variations. The agent eventually got it right but never logged the initial failures. - **Silent user corrections**: The user said "no, that's wrong" mid-flow. The agent corrected course but didn't log the misunderstanding. - **Worked-around test failures**: A test failed, the agent changed approach, the new approach passed, the original failure was forgotten. - **Context handoff causes**: Which drift signals actually triggered handoffs, not just that handoffs happened. - **Token/time anomalies**: Sessions with disproportionate cost vs output — a signal of inefficiency the agent is unaware of. These patterns are high-value for the outer loop because the agent can't self-report them. Session traces are the only source. ### When to trigger --deep mode Trace analysis is **not** per-session. It's cadenced: - **Weekly scheduled** (recommended minimum): after a sprint or burst of sessions - **Post-incident**: when something went wrong and you want to understand why - **Pre-promotion**: before committing a pattern to project instruction files, verify it actually recurs in real sessions - **Manual invocation**: `/learning-aggregator --deep --since 7d` Running trace analysis per-session would burn tokens without producing new signal — cross-session patterns only emerge over multiple sessions. ### Reading traces with Entire When `--deep` is requested, the skill uses the `entire` CLI to query shadow branch data: ```bash # Check availability entire --version # List recent checkpoints as JSON (id, date, session_id, message, tool_use_id) entire rewind --list # Read a checkpoint's full transcript entire explain --checkpoint <id> --full --no-pager # Or raw JSONL entire explain --checkpoint <id> --raw-transcript --no-pager # Filter to one session entire explain --session <session-id-prefix> # Generate AI summary (expensive, use sparingly) entire explain --checkpoint <id> --generate ``` If `entire` is not installed or the current repo doesn't have Entire enabled, `--deep` falls back to `.learnings/`-only mode and reports the limitation in the gap report. ### What to extract from a trace For each checkpoint within the time window, parse the raw transcript and look for: 1. **Tool call repetition** — same tool + similar args > 3 times → likely a retry loop. Pattern-key: `retry-loop.<tool>` 2. **User correction markers** — user messages containing "no", "wrong", "actually", "instead" immediately after an agent action → Pattern-key: `correction.<area>` 3. **Error patterns in tool output** — matches against the same regex set as `error-detector.sh` (error, failed, Traceback, etc.) → Pattern-key: `error.<category>` 4. **Handoff triggers** — context-surfing exit events and which drift signals fired → Pattern-key: `drift.<signal>` 5. **Approach changes** — agent switching strategy mid-task without explicit pivot → Pattern-key: `approach-switch.<domain>` 6. **Token anomalies** — sessions with token count > 2x the median for similar task types → Pattern-key: `cost.<task-type>` Each finding is normalized to the same taxonomy as self-improvement (`harden.input_validation`, `simplify.dead_code`, etc.) where possible. ### How the two sources merge in the gap report When `--deep` runs, each pattern in the gap report gets a `sources` field: ```yaml promotion_ready: - pattern_key: "harden.input_validation" recurrence_count: 5 sources: - .learnings/LEARNINGS.md (3 entries) - entire:traces (5 occurrences across 4 sessions) confidence: high # appears in both sources evidence: - "LRN-20260401-001: Missing bounds check on pagination" - "entire:1ca16f9b: Retry loop on /api/search — pageSize rejected 4 times" - "entire:8bf2e4cd: User correction 'validate before DB query'" entire_checkpoints: - 1ca16f9bb3801ee2a02f2384f31355a54b81ea00 - 8bf2e4cd63d01040b38df07c43f73e0f15d05ac9 ``` A pattern in both sources is higher confidence than one from either alone. A pattern only in `.learnings/` might be over-logged by a diligent agent. A pattern only in traces might be noise. The overlap is where the signal is strongest. ### Trace source compatibility The default implementation targets Entire (v0.5.4+) via the `entire rewind --list` and `entire explain` commands. The concept is source-agnostic — any session capture tool that exposes: - A list of recent checkpoints (with id, timestamp, session id) - The ability to read a checkpoint's transcript - Timestamps for cadence filtering ...can serve as a trace source. Adapters for other capture tools can be added in `scripts/` or via gh-aw `mcp-scripts`. ## Persistence Reads `.learnings/` from the working directory. This is the only persistence mode — the skill does not integrate with external memory backends in interactive sessions. For CI-side durable storage across workflow runs, see `learning-aggregator-ci`, which can optionally back its state with gh-aw's `repo-memory` (git-branch persistence). The resulting branch is a normal git branch and can be fetched locally if desired, but the interactive skill itself only reads local files. ### Tracker-id in gap reports Each promotion candidate in the gap report includes a `tracker` field set to the pattern-key. This tracker propagates through the full chain: harness-updater embeds it as a comment in project instruction files, eval-creator references it in eval cases. To audit the full lifecycle of a pattern, search for `tracker:[pattern-key]` across the repo and GitHub. ## What This Skill Does NOT Do - Does not modify `.learnings/` files (read-only analysis) - Does not apply promotions (that's harness-updater) - Does not create evals (that's eval-creator) - Does not fix code or run tests - Does not replace human judgment for ambiguous patterns - Does not run `--deep` trace analysis per-session — only on cadence or explicit invocation - Does not require Entire — falls back to `.learnings/`-only mode when trace source is unavailable