summary

A reporting command for TokenWise routing data, which records how tasks are assigned to models and what they cost. It summarizes activity over a chosen period, such as a week, month, or custom number of days.

In plain words
What is it for?
Use it to create weekly, monthly, custom-period, or all-time reports, including trends, task categories, per-model totals, and optional JSON or Markdown output.
Why use it?
It removes the need to inspect raw log entries to understand usage, spending, task types, model distribution, and savings.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/codeshux/tokenwise/summary
Any agent
npx skills add CodeShuX/tokenwise --skill summary
Clone the repo
git clone --depth 1 https://github.com/CodeShuX/tokenwise

Made for: Claude Code, Codex.

Per session 59 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 724 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 91% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00059 $0.00724
Opus 5 $0.00030 $0.00362
Sonnet 5 $0.00012 $0.00145
Haiku 4.5 $0.00006 $0.00072

Measured yesterday against content hash 593be973f66e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

summary scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

91% identical to CodeShuX__tokenwise — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/summary/SKILL.md · 74 lines

How it starts

The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/tokenwise:summary — Multi-session trend report

Aggregate .tokenwise/log.ndjson over a time window.

Parse $ARGUMENTS

  • --week (default) — last 7 days
  • --month — last 30 days
  • --all — entire log
  • --days <N> — last N days
  • --out <path> — write the report to a markdown file instead of stdout
  • --json — output JSON instead of text

Steps

  1. Read ./.tokenwise/log.ndjson (or the path the user provides)

  2. Filter entries to the time window:

    • Compute cutoff: now - <days>*86400
    • Keep entries where ts > cutoff
  3. Aggregate:

    • Total: sessions (unique session_id), tasks, total cost, baseline cost, savings
    • Per model: task count, cost, % of total
    • Per task_class (mechanical|execution|review|planning): task count, avg cost, dominant model
    • Trend (only for --week or --days <≤14>): per-day cost + savings bar chart in plain text
  4. Print:

TokenWise Summary — last <N> days
==================================

Sessions:        <count>
Tasks routed:    <count>
Total spent:     $<total>
Baseline:        $<baseline>
Savings:         $<savings> (<pct>%)

Per model:
  Haiku    <count> tasks  $<cost>  (<pct>%)
  Sonnet   <count> tasks  $<cost>  (<pct>%)
  Opus     <count> tasks  $<cost>  (<pct>%)
  Fable    <count> tasks  $<cost>  (<pct>%)

Top task classes:
  <class>          <count> tasks   avg cost $<avg>   model: <dominant>
  ...

Daily trend (cost):
  Mon  ███████░░░ $4.21
  Tue  ███░░░░░░░ $1.82
  Wed  ████████░░ $5.04
  ...
  1. If --out <path> was provided, write the markdown version of the report to that path. Use a proper markdown table for the trend section.

  2. If --json, dump the aggregated data structure as pretty-printed JSON.

Notes on small log files

  • If <2 sessions in the window: print the report but add Note: too few sessions for meaningful trend data.
  • If log file is empty/missing: print the same "No TokenWise log found" message that /tokenwise:report uses.
  • Omit any "Per model" row whose count is 0 for the window — Fable's row will legitimately be absent for most users most of the time.

Read the full file on GitHub · 74 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 74 lines · 0 tokens per session scan A 593be973f66e

Subscribe to this mod's changes

summary is a skill published in the GitHub repository CodeShuX/tokenwise (3 stars, last pushed 6d ago), licensed MIT. It adds 59 tokens to every session and 724 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to CodeShuX__tokenwise, differing in 4 lines, and is treated as a copy.

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