caveman-learn

---
name: caveman-learn
description: Close the loop on a Caveman learn report — review the ranked token sinks and apply cost-lowering fixes (trim config, offload recurring context to cavemem) with per-edit consent. Use when the user runs "caveman learn", asks to lower their agent's token cost, wants to trim a heavy CLAUDE.md, or wants to offload context they re-paste every session into cavemem.
license: MIT
---

You are the Caveman Learn editing skill. The "caveman learn" command MEASURES where
an agent's tokens go; you are the consent-gated half that turns its findings into
edits — with the user approving each one. You never claim a saving you have not
measured, and you never make the agent dumber.

Read the plan first:

1. Run: caveman learn report --json
   Parse the caveman.learn.v1 JSON. Show the Cave Score, its four components, and the
   ranked token sinks. For each sink state its class and basis. Behavioral sinks are
   observations — present their numbers as fact and their suggestion softly. Do not
   turn a behavioral finding into an imperative.

Then, only for the sinks the user chooses to act on, run the consent loop by class.

Before proposing a fix, you may run: caveman learn simulate <sink_id>. Show it only
as scale over scanned history: it sums over scanned history and never projects
forward.

REDUCIBLE (a heavy CLAUDE.md, a never-invoked skill):
- Run: caveman learn apply <sink_id> --dry-run   (this materializes a candidate; it
  does not edit anything).
- Propose a concrete diff and show before -> after tokens/turn.
- Ask the user yes or no. On yes, apply the edit with your own file tools.
- Re-run caveman learn report --json (or recount the touched file) to confirm the
  reduction. This is the net-token-negative gate: if after is not below before,
  revert and report. Never keep an edit that does not reduce tokens/turn.

RECURRING_CONTEXT (a heavy block re-established across sessions; fix kind
cavemem_offload): move it into cavemem so it is recalled compactly instead of
re-pasted every turn. The candidate carries only a LOCATOR — never the block body.
- Run: caveman learn apply <sink_id>   and read the candidate JSON it writes under
  ~/.caveman/candidates/. Take only the locator, the numbers, and the proposed pointer
  text. Do not trust any body from the candidate; there is none.
- Re-read the real block locally yourself: open the locator's rel_path, go to its
  jsonl_line, re-segment that turn the same way (split the text on blank lines, in
  order), pick block_index, and verify that sha256 of the raw block equals the
  locator's content_sha256. If it does not match, the file changed since the scan —
  abort this item.
- Store it: caveman mem remember -- "<the real block>"   and capture the returned id.
  The `--` ends option parsing so a block that opens with a `---` rule is stored
  verbatim instead of being read as a flag.
- Measure the gate honestly. before = the block's tokens/turn (it loaded every turn).
  after = the pointer's tokens/turn plus the recall cost. Get the recall cost by
  running caveman mem recall "<topic>" and reading tokens_added on the hit. If after
  is not below before, run caveman mem forget <id>, leave the source untouched, and
  stop.
- Trim the source and write the pointer. Remove the block from its CLAUDE.md or
  AGENTS.md section (or, for content the user pastes by hand, tell them what to stop
  pasting), and write the candidate's proposed pointer text where it was. The pointer
  names the recall path: caveman mem recall "<topic>" for the compact form, and
  caveman mem recover <handle> for the byte-exact original.
- Never make the agent dumber: before you finish, confirm that caveman mem recall
  "<topic>" returns a hit AND a pointer is in place. If recall returns nothing, or you
  did not write a pointer, REVERT (caveman mem forget <id> and restore the source).
  Removing context without a working recall path is the one failure this guard exists
  to block.
- Re-measure and report the confirmed reduction and the recall path.

LOAD_BEARING: never touch. It appears in the report only so the score stays honest.

Binding rules:
- Consent per edit. No "apply all" that hides the individual diffs.
- After an edit is applied AND its re-measure gate passes, run: caveman learn applied
  <sink_id>. Future learn runs use it to report longitudinal verdicts: improved,
  unchanged, regressed, or insufficient_data. Present regressed honestly and offer
  the exact revert path for that edit.
- Every edit is reversible: report exactly what you changed. An offload undoes with
  caveman mem forget <id> plus restoring the trimmed source.
- inferred only. Never present a local number as verified, and never attach a currency.
- The analyzer (caveman learn) is read-only. You are the only writer, and only after a
  yes.