Audit Session Metrics

Audit Session-Metrics

Reads a session-metrics JSON export and produces a prioritised, plain-English audit of token-usage waste. It runs on the session's current model (it no longer pins one — a hard model pin capped the usable context at that model's window and broke invocation on long sessions). The work is mostly summarisation over a small disk-read export, so for a ~10× cheaper run /model haiku before invoking (short/early sessions only — Haiku's 200k window can't hold a long conversation).

Supports three JSON scopes auto-detected from digest.scope:

• session — single session (session_*.json) — per-turn analysis
• project — all sessions for one project (project_*.json) — per-session analysis
• instance — all projects (instance/*/index.json) — per-project analysis

Dispatch — how to route this invocation

First positional argument received: $ARGUMENTS[0] Second positional argument received: $ARGUMENTS[1]

Read $ARGUMENTS[0] and match by literal equality:

$ARGUMENTS[1] must be the path to a JSON export written by session-metrics. Accepted patterns:

• exports/session-metrics/session_<id8>_<ts>.json — session scope
• exports/session-metrics/project_<ts>.json — project scope
• exports/session-metrics/instance/<datedir>/index.json — instance scope

If $ARGUMENTS[1] is missing, empty, or the file does not exist, print:

Usage: /audit-session-metrics {quick|detailed} <path-to-session-metrics.json>

Accepted JSON types: session: exports/session-metrics/session_*.json project: exports/session-metrics/project_*.json instance: exports/session-metrics/instance/*/index.json

…and stop without further work.

Steps

1. Run the extract helper once with the input path:

   python3 scripts/audit-extract.py $ARGUMENTS[1] --mode $ARGUMENTS[0]
   

   The helper emits a single JSON digest to stdout. Read digest.scope from the output — it will be "session", "project", or "instance". Do not read the raw .jsonl, and do not re-derive numbers the digest already carries.

2. Scope routing — read the matching reference file:

   digest.scope	$ARGUMENTS[0]	Reference file
   session	quick	references/quick-audit.md
   session	detailed	references/detailed-audit.md
   project	quick	references/project-quick-audit.md
   project	detailed	references/project-detailed-audit.md
   instance	quick or detailed	references/instance-quick-audit.md

   Follow the playbook step-by-step. Do not improvise additional phases.

3. For detailed mode on session scope only, the playbook also asks you to read the user's config files (~/.claude/CLAUDE.md, ./CLAUDE.md, ~/.claude/settings.json, ./.claude/settings.json, ./.claudeignore). Each is capped at ≤500 lines — if a file is bigger, that itself is a finding.

4. Output contract — three artefacts. All playbooks specify the same three-artefact contract:

   • JSON sidecar at <project>/exports/session-metrics/audit_<id8>_<ts>_<mode>.json — structured findings (versioned schema, enum'd metrics).
   • Markdown copy at <project>/exports/session-metrics/audit_<id8>_<ts>_<mode>.md — same content rendered for humans.
   • Inline chat output — the markdown content printed in your reply.

   <id8> and <ts> come from digest.session_id_short and digest.ts_str (the helper parses the input filename and normalises all three filename patterns).

5. Write order. Populate the JSON object first using the digest values, write the JSON sidecar, render the markdown using the template in the playbook, write the markdown copy, then print the markdown inline (without the H1 heading — the chat client already shows context above the audit). Finish with two stderr-style lines on their own: [audit] saved → <json-path> [audit] saved → <md-path>

   IMPORTANT: Use the Write tool directly for steps 3 and 5. Do NOT generate a Python script (e.g. writing to /tmp/audit_synthesis.py and executing it) — this adds unnecessary failure modes (syntax errors, f-string escaping) and is never required. Build the JSON in the AI's own context; call Write. Render the markdown in context; call Write again.

Tone

• Direct and specific. Cite the exact ratio, dollar figure, or turn index. No motivational language, no LLM-theory padding.
• Prioritise by impact. Sort findings so the costliest fix is first.
• Quote sparingly. Snippets capped at 5 lines each.
• Honour the playbook. If quick-audit.md asks for 5 rows, produce 5 rows — don't invent a 6th to look thorough.

Why this is a separate skill

The audit is a distinct, user-initiated analysis step over a finished export, not part of generating one. It reads only the on-disk JSON — never the conversation — so it stands alone as its own turn, which is why session-metrics suggests /audit-session-metrics rather than invoking it programmatically. Running it as a fresh slash command keeps the turn focused and lets the user decide when to spend on it. The work is summarisation-heavy and the input is tiny, so the cost path is /model haiku before invoking (short/early sessions only — Haiku's 200k window can't hold a long conversation) — ~10× cheaper than a frontier model, with identical output (every dollar figure is pre-computed by audit-extract.py, not guessed by the model).

Reference files

Session scope

• references/quick-audit.md — Distilled ratios + cache health + top expensive turns. Read when scope=session, mode=quick.
• references/detailed-audit.md — Quick audit findings plus config + re-read scans. Read when scope=session, mode=detailed.

Project scope

• references/project-quick-audit.md — Per-session cost outliers, cache health, breaks, weekly trend. Read when scope=project, mode=quick.
• references/project-detailed-audit.md — Project quick findings plus per-session turn-level drilldown on the most expensive sessions. Read when scope=project, mode=detailed.

Instance scope

• references/instance-quick-audit.md — Per-project cost breakdown, cache health, weekly cost trend. Used for both quick and detailed modes (no per-turn data available at instance scope).