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 an entry's Recurrence-Count reaches the promotion threshold (>= 3) — verify the candidate 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
HEALS.md	Verified runtime recoveries filed by self-healing — including Handoff blocks flagging recurring patterns ready for promotion

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 / in_progress / resolved / wont_fix / promoted / promoted_to_skill (the writer's vocabulary; promotion readiness is computed from Recurrence-Count, not stored as a status)
• 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
• Task-ID, Session-ID, Occurrence-ID, Source-Ref, Copied-From — optional provenance fields from newer writers

Step 2: Group and Aggregate

Build canonical occurrences before grouping:

1. Collapse copied entries with the same entry ID and normalized content, even when they appear in multiple repo locations.
2. Collapse matching Occurrence-ID values across .learnings/, cloud/local mirrors, forks, forwarded transcripts, and trace sources.
3. When Occurrence-ID is absent, use explicit Task-ID, Session-ID, Source-Ref, and Copied-From lineage plus normalized evidence to identify copies. Do not infer independence from different file paths or checkpoint IDs alone.
4. Treat one .learnings entry and one trace event describing the same task occurrence as one occurrence, while retaining both source references as corroborating evidence.
5. For legacy entries without stable task/session provenance, label lineage unknown. They may contribute their declared recurrence once per canonical entry, but all unknown-lineage evidence counts as at most one distinct task and cannot by itself prove the cross-task threshold.

Then group canonical occurrences by Pattern-Key. For each group:

1. Count deduplicated recurrences across canonical occurrences. Do not sum duplicate copies of the same entry's Recurrence-Count.
2. Count distinct tasks from stable task/session lineage, not source-file count
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 proven by stable provenance
• Within a 30-day window

Statuses are part of promotion state, not just display metadata:

• promoted, promoted_to_skill, resolved, and wont_fix are terminal for the recorded occurrence. Keep them as history, but do not surface a terminal-only group as promotion-ready.
• If a newer pending or in_progress occurrence appears after the latest terminal event, classify it as a regression/reopened pattern and compute readiness from the post-terminal occurrences.
• A Handoff block is evidence that the threshold was previously reached, not permission to re-promote an already terminal pattern.

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:

## 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
- Terminal-only patterns → report as historical, not promotion-ready
- Legacy groups without enough stable task provenance → report as unverified cross-task recurrence

Step 5: Handoff

The gap report feeds into:

1. self-improvement skill — the main agent follows its promotion workflow to revalidate candidates, show proposed diffs, and apply human-approved rules to project instruction files. Pass the Pattern-Key, evidence, recurrence, gap type, recommended action, related files, and tracker. No dedicated agent preset is required.
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: 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:

# 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, the current repo doesn't have Entire enabled, or entire rewind --list succeeds with zero checkpoints, --deep falls back to .learnings/-only mode and reports the exact limitation in the gap report. An empty checkpoint list is not evidence that no sessions occurred.

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 — error, failed, Traceback, etc. are candidate signals, not proof of failure; corroborate with the recorded tool status → 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. Preserve the trace source, checkpoint, session, and task lineage needed to deduplicate it against .learnings/ evidence.

How the two sources merge in the gap report

When --deep runs, each pattern in the gap report gets a sources field:

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 — a documented Claude, Copilot, Codex, or other session source may be used when the environment 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.

Do not scrape arbitrary private transcript locations or invent an adapter when no documented source is available. Any adapter must emit the same stable task/session/source lineage and occurrence fingerprints used by Step 2. Report unsupported or empty sources instead of treating them as independent evidence.

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. The self-improvement promotion workflow preserves it in the handoff and optionally as a comment in project instruction files; eval-creator references it in eval cases. To audit inline provenance where enabled, 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 the human-gated self-improvement workflow)
• 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