ktg-plugin-marketplace/plugins/config-audit/commands/tokens.md

---
name: config-audit:tokens
description: Show ranked token hotspots and Opus 4.7 pattern findings — what's costing the most per turn and how to reduce it
argument-hint: "[path] [--global]"
allowed-tools: Read, Bash
model: sonnet
---

# Config-Audit: Token Hotspots

Show the configuration sources that contribute the most tokens per turn, ranked by estimated tokens, with Opus 4.7-specific recommendations for reducing prompt-cache misses, schema bloat, and deep import chains.

Complementary to `/config-audit whats-active`:
- **`whats-active`** = inventory view (what loads).
- **`tokens`** = action view (what to trim and why).

## UX Rules (MANDATORY — from `.claude/rules/ux-rules.md`)

1. **Never show raw JSON or stderr output.** Always use `--output-file` + `2>/dev/null`.
2. **Narrate before acting.** Tell the user what you're about to do.
3. **Read, don't dump.** Read the JSON file and render formatted tables.
4. **End with context-sensitive next steps.**

## Implementation

### Step 1: Parse `$ARGUMENTS`

Split `$ARGUMENTS` into a path and flags. Path is the first non-flag argument. Default to `.` (current working directory). Recognized flags:

- `--global` — also include the user-level `~/.claude/` cascade
- `--json` — emit raw JSON instead of rendered tables (power-user mode; bypasses the humanizer for byte-stable v5.0.0 output)
- `--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.
- `--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)

### Step 2: Run the CLI silently

Tell the user: **"Analysing token hotspots for `<path>`..."**

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.

```bash
TMPFILE="/tmp/config-audit-tokens-$$.json"
RAW_FLAG=""
if echo "$ARGUMENTS" | grep -q -- "--raw"; then RAW_FLAG="--raw"; fi
node ${CLAUDE_PLUGIN_ROOT}/scanners/token-hotspots-cli.mjs <path> --output-file "$TMPFILE" [--global] $RAW_FLAG 2>/dev/null; echo $?
```

**Exit code handling:**
- `0` → continue
- `3` → tell user: "Couldn't analyse tokens. Check that the path exists and is a directory." Stop.

### Step 3: If `--json` was requested, cat the file and stop

```bash
cat "$TMPFILE"
```

Do NOT render tables in JSON mode.

### Step 4: Read JSON and render

Use the Read tool on `$TMPFILE`. Extract:

- `total_estimated_tokens` — top-line number
- `hotspots[]` — top 10 ranked sources
- `findings[]` — Opus 4.7 pattern findings (CA-TOK-001..003); each finding in default mode carries humanizer fields (`userImpactCategory`, `userActionLanguage`, `relevanceContext`) alongside the v5.0.0 fields
- `counts` — severity breakdown

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.

```markdown
**Token hotspots for `<path>`** — ~{total_estimated_tokens} estimated tokens loaded per turn

### Top hotspots (ranked by estimated tokens)

| Rank | Source | Tokens | Recommendations |
|------|--------|--------|-----------------|
| {rank} | `{source}` | ~{estimated_tokens} | {recommendations joined as `· ` bullets} |

### Findings, grouped by impact

{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:}

- **{userActionLanguage}** — {title}  ({id})
  - {description}
  - **Fix:** {recommendation}
  - _{relevanceContext}_ when not "affects-everyone" (mention the scope so the user knows whether a fix touches shared config or just their machine)

### Severity summary

| Severity | Count |
|----------|-------|
| critical | {counts.critical} |
| high | {counts.high} |
| medium | {counts.medium} |
| low | {counts.low} |
| info | {counts.info} |

_Estimates assume ~4 chars/token (Claude ballpark). Real token count varies ±20%._
```

### Step 5: Cleanup and next steps

```bash
rm -f "$TMPFILE"
```

```markdown
### What's next

- **`/config-audit whats-active`** — full inventory of what loads (plugins, skills, MCP, hooks)
- **`/config-audit posture`** — overall health scorecard (Token Efficiency is the 8th area)
- **`/config-audit fix`** — auto-fix deterministic issues (where applicable)
- See `knowledge/opus-4.7-patterns.md` for the full pattern catalogue (CA-TOK-001 … 003)
- **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.
```

## Scope and limits

- **Read-only.** Inspects config files; never writes.
- **Single repo.** Scans one path per invocation.
- **Structural only.** Hotspots are deterministic byte→token estimates from disk; runtime cache hit-rate is out of scope.
- **Heuristic estimates.** ~4 chars/token for markdown, ~3.5 for JSON. Real counts vary ±20%.

## Error handling

| Condition | Action |
|-----------|--------|
| Exit code 3 | Tell user path is invalid, suggest checking path exists |
| JSON parse fails | Tell user to re-run, mention as a bug to report |
| Empty hotspots | Suggest adding a CLAUDE.md or running `/config-audit feature-gap` first |