context_profilerlisted

# context_profiler — how much window is gone before you start? On a modest machine the usable window is shared between the model weights' RAM and the KV-cache, and every always-on token is paid twice (RAM + each turn). This measures the **prefix** an agent carries before its first message and frames it against real local windows, so you can shrink it. ```bash python -m skills.context_profiler.cli . # prefix tokens + % of 64k/128k/256k python -m skills.context_profiler.cli . --json ``` Components measured: - **directives** — CLAUDE.md / AGENTS.md instructions ([[metrics]] always-on). - **core_agent** — the shared `core-agent.md` rules, if present. - **tool_schemas** — the MCP tool definitions injected into the agent (the *hidden* cost: on this repo ~3.8k tok for 38 tools). - **skill_catalog** — the skills' descriptions IF the whole catalogue is injected. It then reports the % of 64k/128k/256k windows and a **reduction plan** with honest token savings: - **lazy tool loading** — expose ~5 core tools + a `find_tool(query)` that loads a schema on demand (the pattern this very harness uses via ToolSearch). - **on-demand skill search** — don't inject the catalogue; use [[skill_finder]] / [[context_budget]] to load only the relevant skills per task. On this repo: prefix ~7.9k tok (12% of a 64k window) → **~1.4k tok (2%)** once lazy tools + on-demand skills are applied. Exposed via [[llm_mcp]] as `context_profile`. Pure measurement, 0 cloud tokens.