How do I install the Tokenwise skill?

Run npx skillmds@latest add antigravity/tokenwise in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Tokenwise safe to use?

SkillMD's automated safety review verdict for this skill is CAUTION. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: makes network calls, reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Tokenwise?

This skill is tagged as working with Claude Code, Cursor, OpenAI Codex, Gemini, Antigravity. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Tokenwise free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.

Who published Tokenwise?

Antigravity Skills (@antigravity) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Tokenwise by antigravity — Agent Skill

Name: Tokenwise
Rating: 4.2 (5 reviews)
Author: Antigravity Skills

File contents

TokenWise — Measurement-Driven Model Router

Overview

A Claude Code skill that auto-routes subtasks to the cheapest model that can handle them (Haiku for grunt work, Sonnet for scoped reasoning, Opus only for synthesis), then logs every routed task to a local NDJSON with real token + cost numbers. Includes an A/B test subcommand that runs the same task across multiple tiers and scores quality, so the routing decisions are verified against the user's real workload — not estimated.

Anthropic's own bug tracker (Issue #27665) reports 93.8% of Max-subscriber Claude Code tokens flow to Opus. Existing routers (claude-router, wshobson, VoltAgent) either pin models statically or route by vibes-based heuristics with no measurement. TokenWise fills the measurement gap.

When to use

Cutting Claude Code token spend without sacrificing output quality
Validating whether Haiku/Sonnet is "good enough" for a specific task class before trusting auto-routing
Auditing where Opus tokens are actually being burned
Logging per-session cost data for finance or chargeback

Subcommands

/tokenwise:install — guided installer with diff preview, automatic backups, and --dry-run mode
/tokenwise:report — per-session token + cost summary vs all-Opus baseline
/tokenwise:summary [--week|--month|--all] — historical aggregate with trend
/tokenwise:ab "<task>" — A/B test the same task at multiple tiers, generates a markdown comparison
/tokenwise:undo — restore CLAUDE.md / settings.json from backup

Routing taxonomy

Tier	Model	Task class
Mechanical	Haiku 4.5	file reads, grep, format, rename, simple edits, doc lookups
Scoped reasoning	Sonnet 4.6	single-file refactor, scoped research, test writing
Synthesis	Opus 4.7	architecture decisions, multi-file refactor, security review

Safety caps:

Haiku never spawns further subagents
Max spawn depth = 2
Subagents that need a smarter model return to parent — they never escalate on their own
Tasks under 100 chars with no file context run inline (subagent overhead > savings)
Subagent context >30k tokens bumps a tier

Privacy

Zero telemetry. All logs in .tokenwise/log.ndjson local to the project. Task descriptions truncated to 80 chars and stripped of file contents before logging. No analytics endpoint exists in the source.

Install

In any Claude Code session:

/plugin marketplace add CodeShuX/tokenwise
/plugin install tokenwise@tokenwise

Then run /tokenwise:install and follow the guided prompts.

Limitations

Token counts approximate to ±2% vs Anthropic billing
A/B test mode costs extra tokens (one task × N tiers) — intentional one-time validation
Anthropic-only by design (use LiteLLM or OpenRouter for cross-vendor)
Subagent model: param has known silent-fail bugs on some Claude Code builds — skill probes for this at install and refuses to configure if routing is broken

Source

Repo: https://github.com/CodeShuX/tokenwise
License: MIT
Author: CodeShuX

---name: tokenwisedescription: Auto-routes Claude Code subtasks to the cheapest capable model (Haiku/Sonnet/Opus), logs token costs, and A/B tests tiers to validate savings against real workloads.license: MIT---# TokenWise — Measurement-Driven Model Router## OverviewA Claude Code skill that auto-routes subtasks to the cheapest model that can handle them (Haiku for grunt work, Sonnet for scoped reasoning, Opus only for synthesis), then logs every routed task to a local NDJSON with real token + cost numbers. Includes an A/B test subcommand that runs the same task across multiple tiers and scores quality, so the routing decisions are verified against the user's real workload — not estimated.Anthropic's own bug tracker (Issue #27665) reports 93.8% of Max-subscriber Claude Code tokens flow to Opus. Existing routers (claude-router, wshobson, VoltAgent) either pin models statically or route by vibes-based heuristics with no measurement. TokenWise fills the measurement gap.## When to use- Cutting Claude Code token spend without sacrificing output quality- Validating whether Haiku/Sonnet is "good enough" for a specific task class before trusting auto-routing- Auditing where Opus tokens are actually being burned- Logging per-session cost data for finance or chargeback## Subcommands- `/tokenwise:install` — guided installer with diff preview, automatic backups, and `--dry-run` mode- `/tokenwise:report` — per-session token + cost summary vs all-Opus baseline- `/tokenwise:summary [--week|--month|--all]` — historical aggregate with trend- `/tokenwise:ab "<task>"` — A/B test the same task at multiple tiers, generates a markdown comparison- `/tokenwise:undo` — restore CLAUDE.md / settings.json from backup## Routing taxonomy| Tier | Model | Task class ||---|---|---|| Mechanical | Haiku 4.5 | file reads, grep, format, rename, simple edits, doc lookups || Scoped reasoning | Sonnet 4.6 | single-file refactor, scoped research, test writing || Synthesis | Opus 4.7 | architecture decisions, multi-file refactor, security review |Safety caps:- Haiku never spawns further subagents- Max spawn depth = 2- Subagents that need a smarter model return to parent — they never escalate on their own- Tasks under 100 chars with no file context run inline (subagent overhead > savings)- Subagent context >30k tokens bumps a tier## PrivacyZero telemetry. All logs in `.tokenwise/log.ndjson` local to the project. Task descriptions truncated to 80 chars and stripped of file contents before logging. No analytics endpoint exists in the source.## InstallIn any Claude Code session:```/plugin marketplace add CodeShuX/tokenwise/plugin install tokenwise@tokenwise```Then run `/tokenwise:install` and follow the guided prompts.## Limitations- Token counts approximate to ±2% vs Anthropic billing- A/B test mode costs extra tokens (one task × N tiers) — intentional one-time validation- Anthropic-only by design (use LiteLLM or OpenRouter for cross-vendor)- Subagent `model:` param has known silent-fail bugs on some Claude Code builds — skill probes for this at install and refuses to configure if routing is broken## Source- Repo: https://github.com/CodeShuX/tokenwise- License: MIT- Author: CodeShuX

Frequently asked questions

Tokenwise