Coach

Skill Jimmynycu/token-efficiency/plugins/token-efficiency-coach/skills/coach

A Claude Code plugin that shows exactly where your AI coding session wasted tokens — and how to fix it. Flags waste, never quality. Runs locally.

Install

npx -y skills add Jimmynycu/token-efficiency --skill coach

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Run the token-efficiency coach on a Claude Code session transcript: find wasted tokens (uncached context, bloat, oversized tool output, redundant reads, wrong-tier spend) and report the fixes with dollar estimates. Use when a session felt expensive/slow or the user wants to cut token spend.

SKILL.md

3.5 KB, as published. Nobody here has run it

Token-Efficiency Coach

Reads a Claude Code session transcript and coaches the user on token waste — the observable inefficiencies (uncached context, context bloat, oversized tool output, failed tool calls, output-heavy turns, wrong-tier routing, redundant reads) — with a per-finding fix and a dollar estimate. It flags waste; it never grades work quality or scores a person. The analyzer runs locally and reads only your own session log, so no prompt leaves your machine.

The report below is generated at command time by the shared engine (scripts/analyze.py), which auto-finds the latest session for this project when no path is given, or analyzes the path you pass as an argument.

Coach report

!python3 "${CLAUDE_PLUGIN_ROOT}/scripts/analyze.py" $ARGUMENTS

Your task

Relay the ranked report above to the user in your own words — lead with the headline (total cost, the single biggest recoverable amount) and then the findings in priority order. Keep dollar figures framed as upper-bound estimates, exactly as the engine does. Do not invent numbers the report did not produce.
Offer to apply the top fix. Pick the highest-severity finding and propose the concrete remedy it implies, for example:
- Low cache hit → restructure the session so stable context (system prompt, instructions, pinned files) sits at the FRONT and stays byte-identical so it caches.
- Context bloat / very long thread → compact the conversation or split the remaining work into a fresh session to shed dead context.
- Oversized tool output / redundant reads → read targeted line ranges, grep for the lines needed, and pipe noisy commands through head/tail instead of dumping whole files.
- Everything on the priciest model → route mechanical steps (renames, greps, boilerplate, file reads) to a cheaper tier and reserve the top model for real reasoning.
Offer to wire up the live statusline if the user does not already have one. The plugin ships scripts/statusline.py (live $cost · ctx tokens · cache-hit% · ⚠), but a plugin cannot register a statusLine itself — it must go in the user's ~/.claude/settings.json pointing at a stable copy of the script. If they say yes, walk them through the README's "Wire up the statusline" steps (copy the script to a stable path, add the statusLine block, accept the trust dialog).

If the report says no waste patterns tripped, congratulate the user on a clean session and skip the fix offer (the statusline offer may still apply).

Notes for maintainers

All detection logic lives in scripts/analyze.py (the single engine). This command adds zero analysis logic — it only injects the engine's output and relays it.
Waste patterns + coaching copy live in scripts/patterns.json; prices in scripts/prices.json. Edit those, not this file, to tune behavior.
${CLAUDE_PLUGIN_ROOT} is substituted in skill content for plugin skills, so the engine path above resolves to the installed plugin's scripts/analyze.py.