git:20260803.a99f206 to git:20260806.8309675

---

name: debrief-coding

---

# Debrief Coding

Reads `.cache/codemap/logs/` JSONL telemetry, analyses usage patterns, writes a diagnostic report.

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

## Runtime note

Codex has no `bin/` PATH entry and no `$CLAUDE_PLUGIN_ROOT`-equivalent environment variable. Resolve this skill's installed plugin-root path once, substitute it for `PLUGIN_ROOT` below, and keep it in reasoning — shell state does not persist across tool calls. Telemetry evidence source is otherwise identical to the Claude sibling: local JSONL files under `.cache/codemap/logs/`, written regardless of which runtime's session produced them.

## 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 before reading; replaces qualified names with stable pseudonyms; keeps the salt in `.cache/codemap/logs/.salt` (never included in output)

- `--output <path>` — write the report to this path (default: `.reports/codemap/debrief-<YYYY-MM-DD>.md`)

## Step 0: Verify logs exist

```bash

ls .cache/codemap/logs/*.jsonl 2>/dev/null

```

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

Telemetry is sharded per session: CLI records in `cli_<session>.jsonl`, skill records in `skills_<session>.jsonl`, tool-use records in `tools_<session>.jsonl`; runs with no seeded session id fall back to unsuffixed `cli.jsonl` / `skills.jsonl` / `tools.jsonl`. Collect every matching file, not just the legacy unsuffixed names.

## Step 1: Optionally anonymize

If `--anonymize` is given, anonymize every present shard (never assume the legacy unsuffixed files exist — per-session sharding means they usually don't):

```bash

python PLUGIN_ROOT/bin/anonymize.py --input <shard-path>

```

Anonymized copies land in `.cache/codemap/export/` (separated from `.salt` — never target the logs directory itself). Don't mix anonymized and original data in Step 2. No shard present at all → stop: "no CLI or skill logs found — cannot produce an anonymized report." Only one layer present → anonymize it and note the gap.

## Step 2: Read log files

Read every CLI shard, every skill shard, and every tool shard, concatenating records before analysing — reading only one file misses per-session shards and reports a near-empty dataset.

Each line is one JSON record. Filter by `--since` (`ts` field) and `--session` if given.

`--session` guard: a session UUID may be absent from one or both log layers (e.g. `skills.jsonl` records only skill events, not all CLI events). An empty filtered result for one file is expected, not a data-loss signal.

CLI record fields: `ts`, `layer`, `session`, `cmd`, `argv`, `result` (nested: `count`, `exhaustive`, `stale`, `method`, `not_covered`, `error`), `timing_ms`, `stderr` (optional), `exit_code` (optional).

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

Tool record fields (`layer: "tool"`): `ts`, `layer`, `session`, `v` (plugin version, 0.23+), `tool`, `target`.

## Step 3: Analyse

**Pre-filter (both layers)**: drop records with `source: "bench"` and CLI records with an empty `cmd` from all organic-usage stats; report their counts separately as "scripted/polluted records excluded: N". When records carry `v` (plugin version, 0.23+), compute headline stats per distinct `v` as well as overall.

**CLI layer**: total invocations, success vs error rate (`exit_code: 0` = success; non-zero = error; absent = treat as success unless `result.error` is non-empty); completeness reasons (`result.index.completeness_reason`); subcommand distribution; timing (median, p95, max `timing_ms`); coverage (fraction with non-empty `not_covered`); top-5 error patterns by prefix; stale-index warning fraction.

**Skill layer**: total skill starts by name, distinct session count, first/last timestamp in the dataset.

**Cross-layer**: sessions appearing in both layers → linked chains (skill invoked → N CLI calls); average CLI calls per skill session.

**Avoidance join (guard-chain leak rate)**: join the tool layer against the CLI layer — a search/read whose target names a module codemap already answered completely (`query_complete: true`) within the window is an avoidance event, i.e. the model re-derived by hand what the index had already returned exhaustively.

```bash

python PLUGIN_ROOT/bin/join_avoidance.py --logs .cache/codemap/logs --window-min 10 --json

```

A high avoidance rate is a dead-chain signal: guard not firing, injected context not read, or the model ignoring both. Add the count and rate to the report's Overview; when non-zero, list the flagged modules.

## Step 4: Write report

Output path: `--output` if given, else `.reports/codemap/debrief-<YYYY-MM-DD>.md` (today's date). This path is product-level, not runtime-specific — identical to the Claude sibling's default.

```bash

mkdir -p .reports/codemap

```

Write the report with these sections: Overview (2–3 sentence summary), Subcommand distribution, Performance (median/p95/max `timing_ms`), Coverage gaps, Error patterns (top-5 with counts, "none" if clean), Skill invocations, Session timeline (first/last ts, distinct sessions; full chronological event list when `--session` was given).

Print the report path on completion.

## Security note

All logs are local to `.cache/codemap/logs/`. The salt file `.cache/codemap/logs/.salt` must stay local — never share it alongside anonymized output. Anonymized log files themselves are safe to share; without the salt, the pseudonyms are not reversible.