egorfedorov

cco-overhead

Audit the fixed context overhead every session starts with — system prompt, MCP tools, agents, CLAUDE.md, memory — measured from real transcript usage

egorfedorov 102 10 Updated 4w ago
GitHub

Install

npx skillscat add egorfedorov/claude-context-optimizer/cco-overhead

Install via the SkillsCat registry.

SKILL.md

Session Baseline Overhead Audit

Measure how many tokens every session of this project pays BEFORE any work
happens — and where to cut.

Run:

node ${CLAUDE_PLUGIN_ROOT}/src/overhead.js

The report shows:

  1. Baseline — exact context size at the first assistant response (from the
    session transcript's API usage counts), latest and averaged over recent
    sessions, as a % of the working budget.
  2. Cost per session — what that baseline costs to write into the prompt
    cache each session.
  3. Itemization — the locally measurable parts (project + global CLAUDE.md,
    memory index, agent definitions) and the unattributed remainder (system
    prompt, tool schemas, MCP servers).
  4. Recommendations — what to trim and how (e.g. /cco-claudemd, disabling
    unused MCP servers, pruning agent descriptions).

Then run the MCP usage audit — it turns 30 days of tracked tool calls into
per-server verdicts and the EXACT removal command for servers that were never
called:

node ${CLAUDE_PLUGIN_ROOT}/src/overhead.js mcp

Only if the report actually prints claude mcp remove ... commands, OFFER to
run them for the user (each removal repays in every future session; claude mcp add restores any time). Only run them after the user agrees.

If a server is listed as ? not observed, the tracker has no MCP data yet —
that is not a verdict. Never suggest removing those servers, and never
construct a claude mcp remove command the report did not print.

Present the output to the user as-is (it is already formatted). If the report
says no transcripts were found, explain that the audit needs at least one
completed exchange in a session for this project.

Key framing for the user: baseline overhead is paid in EVERY session, so a
one-time trim repays itself continuously — it is usually the highest-leverage
optimization available.