config-audit/commands/tokens.md

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

Config-Audit: Token Hotspots

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.

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.

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

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[] — 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
• 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.

**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

rm -f "$TMPFILE"

### 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/prompt-cache-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