Close the loop on caveman learn. The command measures where your agent's tokens go; this skill reviews that plan with you and applies the fixes — one approved edit at a time.

Install

caveman skills install caveman-learn            # this repo's .claude/skills
caveman skills install caveman-learn --user      # all repos (~/.claude/skills)
caveman skills install caveman-learn --agent codex

What it does

Runs caveman learn report --json and shows your Cave Score + ranked token sinks.
For each sink you pick, proposes a fix and asks yes/no:
- reducible (heavy CLAUDE.md, never-invoked skill) → a concrete trim, applied only if it measurably lowers tokens/turn.
- recurring_context (context you re-establish every session) → offload it to cavemem (cavemem_offload): stored raw, compacted at recall on demand, with a cheap pointer left behind. Applied only when it beats re-pasting, and only after a confirming recall proves the content still comes back.
- load_bearing → never touched.

After an approved edit passes its re-measure gate, caveman learn applied <sink_id> records the sink, fix kind, application time, and before-value in Caveman's own outcome store. Later scans compare post-fix sessions and report improved, unchanged, regressed, or insufficient_data. This ledger does not edit user or repository config.

Honesty

Everything is inferred — no currency, no "verified". Every edit is consent-gated and reversible, and an offload that would leave the agent unable to recall the content is rejected. The analyzer never edits your files; this skill does, only with your yes.