Annotate every ranked TOK hotspot with the load-pattern triple (loadPattern/survivesCompaction/derivationConfidence): - hotspotLoadPattern() maps each discovery `type` → a deriveLoadPattern kind. Rules reuse activeConfig.rules for precise `scoped` handling; claude-md maps by scope. Two new deriveLoadPattern kinds back the rest: `command` (on-demand — body loads on /invoke) and `harness-config` (external — settings/keybindings/.mcp.json/hooks.json/plugin.json configure the CLI, not the model context, so they cost no per-turn context tokens). Honest split: the .mcp.json FILE is external; the MCP server's tool schemas are a separate `always` hotspot. Byte-stability — the opposite of B1's manifest. token-hotspots IS a byte-equal SC-6/SC-7 CLI, and its hotspots ride inside scan-orchestrator + posture, so the change touched SIX frozen-v5.0.0 comparisons across five test files. Resolved by preserving the frozen baselines: a shared tests/helpers/strip-hotspot-load-pattern.mjs strips the additive triple before each byte-equal compare (proves the original schema is byte-identical). SC-5 default-output snapshots (scan-orchestrator + token-hotspots) regenerated — diff reviewed as additive-only. Tests 1008→1012. Self-audit A/A, scanner count unchanged at 13 (C bumps to 14). Completes v5.6 B (B1 manifest + B2 token-hotspots). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
135 lines
6.6 KiB
Markdown
135 lines
6.6 KiB
Markdown
---
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name: config-audit:tokens
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description: Show ranked token hotspots and prompt-cache pattern findings — what's costing the most per turn and how to reduce it
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argument-hint: "[path] [--global]"
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allowed-tools: Read, Bash
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model: sonnet
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---
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# Config-Audit: Token Hotspots
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Show the configuration sources that contribute the most tokens per turn, ranked by estimated tokens, with prompt-cache-aware recommendations for reducing cache misses, schema bloat, and deep import chains.
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Complementary to `/config-audit whats-active`:
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- **`whats-active`** = inventory view (what loads).
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- **`tokens`** = action view (what to trim and why).
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## UX Rules (MANDATORY — from `.claude/rules/ux-rules.md`)
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1. **Never show raw JSON or stderr output.** Always use `--output-file` + `2>/dev/null`.
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2. **Narrate before acting.** Tell the user what you're about to do.
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3. **Read, don't dump.** Read the JSON file and render formatted tables.
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4. **End with context-sensitive next steps.**
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## Implementation
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### Step 1: Parse `$ARGUMENTS`
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Split `$ARGUMENTS` into a path and flags. Path is the first non-flag argument. Default to `.` (current working directory). Recognized flags:
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- `--global` — also include the user-level `~/.claude/` cascade
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- `--json` — emit raw JSON instead of rendered tables (power-user mode; bypasses the humanizer for byte-stable v5.0.0 output)
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- `--raw` — pass-through to the scanner; produces v5.0.0 verbatim JSON (bypasses the humanizer). Use when piping into v5.0.0-baseline diff tooling.
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- `--with-telemetry-recipe` — include `telemetry_recipe_path` in the JSON output, pointing to `knowledge/cache-telemetry-recipe.md`. Use this when you want to verify a structural fix actually improved cache hit rate (manual jq recipe, opt-in)
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### Step 2: Run the CLI silently
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Tell the user: **"Analysing token hotspots for `<path>`..."**
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Default mode (no `--json`, no `--raw`) emits a humanized JSON envelope: each finding carries `userImpactCategory`, `userActionLanguage`, and `relevanceContext` in addition to the v5.0.0 fields. Pass `--raw` through verbatim if the user requested it.
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```bash
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TMPFILE="/tmp/config-audit-tokens-$$.json"
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RAW_FLAG=""
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if echo "$ARGUMENTS" | grep -q -- "--raw"; then RAW_FLAG="--raw"; fi
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node ${CLAUDE_PLUGIN_ROOT}/scanners/token-hotspots-cli.mjs <path> --output-file "$TMPFILE" [--global] $RAW_FLAG 2>/dev/null; echo $?
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```
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**Exit code handling:**
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- `0` → continue
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- `3` → tell user: "Couldn't analyse tokens. Check that the path exists and is a directory." Stop.
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### Step 3: If `--json` was requested, cat the file and stop
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```bash
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cat "$TMPFILE"
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```
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Do NOT render tables in JSON mode.
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### Step 4: Read JSON and render
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Use the Read tool on `$TMPFILE`. Extract:
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- `total_estimated_tokens` — top-line number
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- `hotspots[]` — top 10 ranked sources; each carries a **load pattern** (`loadPattern` ∈ always / on-demand / external, plus `survivesCompaction` / `derivationConfidence`)
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- `findings[]` — prompt-cache pattern findings (CA-TOK-001..003); each finding in default mode carries humanizer fields (`userImpactCategory`, `userActionLanguage`, `relevanceContext`) alongside the v5.0.0 fields
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- `counts` — severity breakdown
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A hotspot's **load pattern** matters as much as its size: an **always**-loaded source (CLAUDE.md, MCP tool schemas) is paid on *every* turn, an **on-demand** one (skill body, path-scoped rule) only when invoked/matched, and an **external** one (hooks, harness-config files like settings.json/.mcp.json) costs no per-turn context tokens at all. A big always-loaded hotspot is the most worth trimming.
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Render as markdown. Group findings by `userImpactCategory` (e.g., "Wasted tokens" vs "Configuration mistake") rather than re-deriving severity prose; lead each line with `userActionLanguage` ("Fix this now", "Fix soon", "Optional cleanup", etc.) so the urgency phrasing stays consistent with the rest of the toolchain. The humanizer already replaced jargon-heavy `title`/`description`/`recommendation` strings with plain-language equivalents — render them verbatim.
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```markdown
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**Token hotspots for `<path>`** — ~{total_estimated_tokens} estimated tokens loaded per turn
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### Top hotspots (ranked by estimated tokens)
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| Rank | Source | Tokens | Load | Recommendations |
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|------|--------|--------|------|-----------------|
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| {rank} | `{source}` | ~{estimated_tokens} | {loadPattern} | {recommendations joined as `· ` bullets} |
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_Load column: **always** (every turn) / **on-demand** (on invoke/match) / **external** (out-of-context). Append `°` when `derivationConfidence` is `inferred`._
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### Findings, grouped by impact
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{Group findings[] by their userImpactCategory. Within each group, sort by userActionLanguage urgency (Fix this now → Fix soon → Fix when convenient → Optional cleanup → FYI), then render:}
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- **{userActionLanguage}** — {title} ({id})
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- {description}
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- **Fix:** {recommendation}
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- _{relevanceContext}_ when not "affects-everyone" (mention the scope so the user knows whether a fix touches shared config or just their machine)
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### Severity summary
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| Severity | Count |
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|----------|-------|
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| critical | {counts.critical} |
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| high | {counts.high} |
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| medium | {counts.medium} |
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| low | {counts.low} |
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| info | {counts.info} |
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_Estimates assume ~4 chars/token (Claude ballpark). Real token count varies ±20%._
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```
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### Step 5: Cleanup and next steps
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```bash
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rm -f "$TMPFILE"
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```
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```markdown
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### What's next
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- **`/config-audit whats-active`** — full inventory of what loads (plugins, skills, MCP, hooks)
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- **`/config-audit posture`** — overall health scorecard (Token Efficiency is the 8th area)
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- **`/config-audit fix`** — auto-fix deterministic issues (where applicable)
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- See `knowledge/prompt-cache-patterns.md` for the full pattern catalogue (CA-TOK-001 … 003)
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- **Verify cache hit rate after a fix:** rerun with `--with-telemetry-recipe` to surface the path to `knowledge/cache-telemetry-recipe.md` — a copy-paste `jq` recipe that reads cache hit rate from your session transcripts. Opt-in. The TOK scanner is structural; this recipe is the runtime escape hatch.
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```
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## Scope and limits
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- **Read-only.** Inspects config files; never writes.
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- **Single repo.** Scans one path per invocation.
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- **Structural only.** Hotspots are deterministic byte→token estimates from disk; runtime cache hit-rate is out of scope.
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- **Heuristic estimates.** ~4 chars/token for markdown, ~3.5 for JSON. Real counts vary ±20%.
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## Error handling
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| Condition | Action |
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|-----------|--------|
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| Exit code 3 | Tell user path is invalid, suggest checking path exists |
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| JSON parse fails | Tell user to re-run, mention as a bug to report |
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| Empty hotspots | Suggest adding a CLAUDE.md or running `/config-audit feature-gap` first |
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