Debrief Coding

Read .cache/codemap/logs/ JSONL telemetry; analyze usage; write diagnostic report. Discover legacy flat shards + recursive claude/, codex/, direct/ trees; keep legacy records unattributed. Codex hooks supply runtime-scoped CLI/tool shards, no skill-start events; missing skill telemetry and cross-layer joins remain evidence gaps.

NOT for: validating codemap installation health/integration (use /codemap-py:integration audit); building/querying structural index (use /codemap-py:scan-codebase or /codemap-py:query-code).

Flags

• --since <YYYY-MM-DD> — filter to records on or after this date (default: all)
• --session <id> — filter to a single session UUID
• --anonymize — run anonymize.py on every log shard of all three layers (CLI, skill, tool) before reading; replaces qualified names with stable pseudonyms; keeps salt in .cache/codemap/logs/.salt (never included in output). Directory input preserves runtime topology below the export root and pseudonymizes shard session stems.
• --output <path> — write report to this path (default: .reports/codemap/debrief-<YYYY-MM-DD>.md)

Step 0: Verify logs exist

find .cache/codemap/logs -type f -name '*.jsonl' -print 2>/dev/null  # timeout: 5000

No files → stop: "No codemap telemetry found. Run any /codemap-py:* skill or codemap-py query/index command to start collecting logs."

Per-session shards under logs/claude/, logs/codex/, logs/direct/: CLI cli_<session>.jsonl; skill skills_<session>.jsonl; tool tools_<session>.jsonl. Older flat shards = unattributed legacy evidence. Collect every matching shard recursively, not only root glob. Preserve topology; report overall, per-runtime, unattributed summaries. token_measurement unavailable: host hooks provide no token usage.

Step 1: Optionally anonymize

If --anonymize flag given:

Guard: anonymize every present CLI, skill, tool shard by passing log directory as --input; recursion covers flat + runtime shards. Copies land in .cache/codemap/export/ with same topology. anonymize.py refuses writes beside .salt; never target logs dir. Step 2 must not mix anonymized/original data or exempt a layer.

python3 "${CLAUDE_PLUGIN_ROOT:-plugins/codemap-py}/bin/anonymize.py" \
    --input .cache/codemap/logs --out-dir .cache/codemap/export  # timeout: 15000

If neither set contains files: print ⚠ --anonymize: no CLI or skill logs found — cannot produce anonymized report.; stop. If only one layer exists, anonymize it; note gap.

Step 2 uses -anon variants under .cache/codemap/export/. anonymize.py absent → warn; use originals.

Step 2: Read log files

Resolve shard tree via Glob; Read every returned path. Never rely on variables from prior Bash block: each tool call gets fresh shell; Read never expands shell variables. After --anonymize, glob mirrored .cache/codemap/export/, not originals:

• CLI shards: recursive Glob(".cache/codemap/logs/**/cli*.jsonl") (or .cache/codemap/export/**/cli*-anon.jsonl when anonymized)
• Skill shards: recursive Glob(".cache/codemap/logs/**/skills*.jsonl") (or .cache/codemap/export/**/skills*-anon.jsonl)
• Tool shards: recursive Glob(".cache/codemap/logs/**/tools*.jsonl") (or .cache/codemap/export/**/tools*-anon.jsonl when anonymized)

All layers anonymized: anonymize.py --input is layer-agnostic; Step 1 includes tool shards. In anonymize mode read tools from .cache/codemap/export/ too. Raw tools_*.jsonl would leak verbatim Grep/Glob patterns and Read paths (target) into shareable export.

Read every returned path; concatenate before analysis. Single-file reads miss sessions and forge near-empty dataset.

Each line = one JSON record. Filter --since against ts; filter --session when given.

--session guard: when --session <id> given, session UUID may be absent from one or both log files (e.g., skills.jsonl records only skill events, not all CLI events). Filtering absent session ID returns empty set for that file — expected, not error. Report "session not found in " rather than treating empty result as data loss.

CLI record fields: ts, layer, runtime, v, session, cmd, argv, result (nested: count, query_complete, completeness_reason, stale, method, not_covered, error; index records also carry trigger, changed_count, incremental, stale_before, and result_currency), timing_ms, stderr (optional), exit_code (optional).

Skill record fields: ts, layer, runtime, v, session, skill, event, intent, hook_session.

Tool fields (layer: "tool", log-tool-use.py): ts, layer, runtime, v, session, skill, event, tool (Grep|Read|Glob|Bash), target (Grep/Glob pattern/search path, Read file_path, Bash search command truncated to 200 chars). Count raw grep/read volume per runtime/session. Never infer flat legacy records as Claude from tool names.

Step 3: Analyse

Compute from filtered records:

Pre-filter (both layers):

• Exclude records with source: "bench" (benchmark/demo load) and CLI records with empty cmd (pre-0.23 test pollution) from organic stats; separately report "scripted/polluted records excluded: N".
• For records with v (plugin version, 0.23+), compute headline error rate, stale rate, completeness per distinct v + overall: release before/after signal.

CLI layer:

• Total invocations, success/error: exit_code: 0 = success; present nonzero = error; absent = unlogged, treat success unless result.error non-empty.
• Aggregate result.index.completeness_reason (0.23+ veto slug: stale / untracked / degraded / collision / root_mismatch / module_degraded; ok = complete): explains false query_complete.
• Subcommand distribution: count per cmd value
• Timing median/p95/max timing_ms; p95: sorted_ms = sorted(r["timing_ms"] for r in cli_records if r.get("timing_ms") is not None); p95 = sorted_ms[int(len(sorted_ms) * 0.95)] if sorted_ms else 0
• Coverage: fraction of results with "not_covered": true or non-empty not_covered
• Error patterns: group result.error strings by prefix (first 60 chars); list top-5 by count
• Stale-index warnings: fraction of results with "stale": true

Skill layer:

• Total skill starts by skill name
• Session count (distinct session values)
• Timeline: first and last ts in dataset

Cross-layer:

• Sessions in both layers → linked chains (skill invoked → N CLI calls)
• Average CLI calls per skill session
• Aggregate overall + by runtime (claude, codex, direct); flat legacy = unattributed.
• Report refresh triggers, changed-file counts, index-only sessions, incomplete-query reasons, stale/degraded fractions. Missing legacy provenance = unknown.
• Do not present debrief as measured token savings or live fresh-session activation evidence.

Avoidance join (guard-chain leak rate):

Join tools to CLI. Grep/Read/Glob targeting a module Codemap answered completely (query_complete: true) within window = avoidance event: agent re-derived exhaustive index result; guard chain leaked. join_avoidance.py joins with word-boundary-safe module match (ported from guard-redundant-scan.py), reporting per-session/per-skill rate:

python3 "${CLAUDE_PLUGIN_ROOT:-plugins/codemap-py}/bin/join_avoidance.py" --logs .cache/codemap/logs --window-min 10 --json  # timeout: 15000

Interpret rate + per_runtime: high rate = dead-chain signal (guard not firing, context unread, or model ignoring both). Preserve runtime/session grouping; keep unattributed separate; never infer legacy runtime. Feed count to product telemetry/self-diagnosis. If nonzero, list events modules.

Step 4: Write report

Output: --output; default .reports/codemap/debrief-<YYYY-MM-DD>.md using today.

mkdir -p .reports/codemap  # timeout: 5000

Create report via Write with sections:

# Codemap Debrief — <date>

**Scope**: <date range> · <total records> records · <anonymized: yes/no>

## Overview

<2–3 sentence summary: total CLI calls, distinct sessions, top subcommand, median timing>

## Subcommand distribution

| cmd | calls | % |
|-----|-------|---|
| ... | ...   |   |

## Performance

| metric | value |
|--------|-------|
| median timing_ms | ... |
| p95 timing_ms | ... |
| max timing_ms | ... |

## Coverage gaps

<fraction with not_covered; list top modules if available>

## Error patterns

<list top-5 error prefixes with counts; "none" if clean run>

## Skill invocations

| skill | starts |
|-------|--------|
| ...   | ...    |

## Session timeline

First: <ts> · Last: <ts> · Distinct sessions: N

<If --session given: full chronological event list for that session>

Print report path on completion.

Example invocations

/codemap-py:debrief-coding

/codemap-py:debrief-coding --since 2026-06-15

/codemap-py:debrief-coding --session 3f2e1a90-...

# use project-relative path, not /tmp
/codemap-py:debrief-coding --anonymize --output .reports/codemap/debrief-anon-$(date +%Y-%m-%d).md

Security note

Logs stay local in .cache/codemap/logs/. .cache/codemap/logs/.salt must stay local; never share with anonymized output. Anonymized logs are shareable; pseudonyms irreversible without salt.