---
name: map-tokenreport
description: |
  Show per-subtask/agent token accounting (input, output, cache read/creation, cost, cache-hit ratio) for the current branch. Use when asked for token usage, run cost, or a token report. Do NOT use to plan or run work; use map-efficient.
effort: low
disable-model-invocation: true
argument-hint: "[branch]"
---
# /map-tokenreport - Token Accounting Report

Purpose: surface how many tokens (and how much money) the current branch's MAP
run spent, attributed to the subtask, phase, and agent that spent them.
Read-only reporting — this skill does not plan, implement, or run quality
gates.

## Constraints (NEVER)

This skill is strictly read-only reporting.

- **NEVER** edit code, state, or git from this skill — it renders an existing rollup and stops.
- **NEVER** run or resume a MAP workflow here; if work is needed, hand off to `/map-efficient`.
- **NEVER** present `est_cost_usd` as a billing source of truth — it is a per-model estimate.

The numbers come from the `map-token-meter` hook (wired on `SubagentStop` and
`Stop`), which reads each Claude Code transcript's per-turn `usage` block and
appends attributed rows to `.map/<branch>/token_log.jsonl`, rolled up into
`.map/<branch>/token_accounting.json`. This skill just renders that rollup.

## What it shows

- **input / output** tokens, **cache_read** (cheap cache hits) and
  **cache_creation** (cache writes) — tracked separately because they bill at
  very different rates.
- **est_cost_usd** — priced per model via `MODEL_TOKEN_PRICES` in
  `map_step_runner.py` (estimate, not a billing source of truth).
- **cache_hit_ratio** = `cache_read / (input + cache_read)` — how well prompt
  caching is paying off.
- Breakdowns by subtask, by agent (actor / monitor / research-agent /
  orchestrator / ...), and by phase.
- **research_roi** — research-agent / Codex researcher tokens and cost compared
  with downstream Actor/Monitor tokens, so users can see whether delegated
  exploration paid for itself.

## Steps

### Step 1: Resolve the branch

```bash
BRANCH=$(git rev-parse --abbrev-ref HEAD | sed -E 's|/|-|g; s|[^a-zA-Z0-9_.-]|-|g; s|-{2,}|-|g; s|^-||; s|-$||')
```

If the user passed an explicit branch argument, use it instead of the detected
one.

### Step 2: Render the report

```bash
python3 .map/scripts/map_step_runner.py token_report "$BRANCH"
```

`token_report` re-reads `token_log.jsonl`, rebuilds `token_accounting.json`,
and prints a per-subtask table, a per-agent table, research ROI, and a TOTAL
line with the cache-hit ratio and estimated cost. It is idempotent and read-only
against your code.

### Step 3: Optional — inspect the raw rollup

For the per-phase split or per-subtask research ROI details, read the JSON
directly:

```bash
jq '{aggregate, by_agent, by_phase, research_roi}' ".map/${BRANCH}/token_accounting.json"
```

### Step 4: Summarize

Emit exactly this report shape, then STOP (this skill never changes code):

```text
Tokens (<branch>): in <I> / out <O> / cache_rd <CR> / cache_cr <CC>
Cache-hit ratio: <X>%   ·   Est. cost: $<C>   ·   Research share: <R>%
Most expensive: <subtask|agent> ($<amount>)
Flags: <low cache-hit | high research cost | none>
```

Fill every field from the `token_report` output. If a value is unavailable, write `n/a` — never omit a line or invent a number.

## Examples

Report token usage for the current branch:

```text
/map-tokenreport
```

Report for a specific branch:

```text
/map-tokenreport feat/add-multiply
```

Typical output:

```text
Token accounting — feat-add-multiply (93 assistant turns)

subtask              input      output     cache_rd    cache_cr     $cost
-------------------------------------------------------------------------
ST-001                 183     150,879   14,085,841     792,780     47.31
-------------------------------------------------------------------------
TOTAL                  183     150,879   14,085,841     792,780     47.31

cache hit ratio: 100.0%   est cost: $47.31
```

## Troubleshooting

- **`token_accounting.json` does not exist / report is empty.** The
  `map-token-meter` hook has not fired for this branch yet. It records on
  `SubagentStop` and `Stop`, so a brand-new branch with no completed turns has
  nothing to show. Confirm the hook is wired in `.claude/settings.json`
  (`SubagentStop` and `Stop` entries) and that `.map/<branch>/token_log.jsonl`
  exists.
- **Everything is attributed to `orchestrator` / `unattributed`.** Those turns
  ran outside a MAP subtask (e.g. direct edits, or before `step_state.json` had
  a `current_subtask_id`). Per-subtask attribution appears once a
  `/map-efficient` run sets the active subtask.
- **Cost looks high relative to raw input.** That is expected when most input
  is cached: `cache_creation` is the dominant cost on cache-heavy runs. Compare
  `cache_creation` vs `cache_read` and the cache-hit ratio to see where the
  spend goes.
- **Unknown model in cost estimate.** `MODEL_TOKEN_PRICES` falls back to the
  default model price for unrecognized model ids; update that table in
  `map_step_runner.py` when a new model ships.