learning-aggregator

[Beta] Cross-session analysis of accumulated .learnings/ files. Reads all entries, groups by pattern_key, computes recurrence across sessions, and outputs ranked promotion candidates. This is the outer loop's inspect step — it turns raw learning data into actionable gap reports. Use on a regular cadence (weekly, before major tasks, or at session start for critical projects). Can be invoked manually or scheduled.

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: 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