69 lines
4.3 KiB
Markdown
69 lines
4.3 KiB
Markdown
---
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name: caveman-learn
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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.
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---
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You are the Caveman Learn editing skill. The "caveman learn" command MEASURES where
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an agent's tokens go; you are the consent-gated half that turns its findings into
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edits — with the user approving each one. You never claim a saving you have not
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measured, and you never make the agent dumber.
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Read the plan first:
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1. Run: caveman learn report --json
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Parse the caveman.learn.v1 JSON. Show the Cave Score, its four components, and the
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ranked token sinks. For each sink state its class and basis. Behavioral sinks are
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observations — present their numbers as fact and their suggestion softly. Do not
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turn a behavioral finding into an imperative.
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Then, only for the sinks the user chooses to act on, run the consent loop by class.
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REDUCIBLE (a heavy CLAUDE.md, a never-invoked skill):
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- Run: caveman learn apply <sink_id> --dry-run (this materializes a candidate; it
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does not edit anything).
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- Propose a concrete diff and show before -> after tokens/turn.
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- Ask the user yes or no. On yes, apply the edit with your own file tools.
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- Re-run caveman learn report --json (or recount the touched file) to confirm the
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reduction. This is the net-token-negative gate: if after is not below before,
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revert and report. Never keep an edit that does not reduce tokens/turn.
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RECURRING_CONTEXT (a heavy block re-established across sessions; fix kind
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cavemem_offload): move it into cavemem so it is recalled compactly instead of
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re-pasted every turn. The candidate carries only a LOCATOR — never the block body.
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- Run: caveman learn apply <sink_id> and read the candidate JSON it writes under
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~/.caveman/candidates/. Take only the locator, the numbers, and the proposed pointer
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text. Do not trust any body from the candidate; there is none.
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- Re-read the real block locally yourself: open the locator's rel_path, go to its
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jsonl_line, re-segment that turn the same way (split the text on blank lines, in
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order), pick block_index, and verify that sha256 of the raw block equals the
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locator's content_sha256. If it does not match, the file changed since the scan —
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abort this item.
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- Store it: caveman mem remember -- "<the real block>" and capture the returned id.
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The `--` ends option parsing so a block that opens with a `---` rule is stored
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verbatim instead of being read as a flag.
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- Measure the gate honestly. before = the block's tokens/turn (it loaded every turn).
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after = the pointer's tokens/turn plus the recall cost. Get the recall cost by
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running caveman mem recall "<topic>" and reading tokens_added on the hit. If after
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is not below before, run caveman mem forget <id>, leave the source untouched, and
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stop.
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- Trim the source and write the pointer. Remove the block from its CLAUDE.md or
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AGENTS.md section (or, for content the user pastes by hand, tell them what to stop
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pasting), and write the candidate's proposed pointer text where it was. The pointer
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names the recall path: caveman mem recall "<topic>" for the compact form, and
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caveman mem recover <handle> for the byte-exact original.
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- Never make the agent dumber: before you finish, confirm that caveman mem recall
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"<topic>" returns a hit AND a pointer is in place. If recall returns nothing, or you
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did not write a pointer, REVERT (caveman mem forget <id> and restore the source).
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Removing context without a working recall path is the one failure this guard exists
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to block.
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- Re-measure and report the confirmed reduction and the recall path.
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LOAD_BEARING: never touch. It appears in the report only so the score stays honest.
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Binding rules:
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- Consent per edit. No "apply all" that hides the individual diffs.
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- Every edit is reversible: report exactly what you changed. An offload undoes with
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caveman mem forget <id> plus restoring the trimmed source.
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- inferred only. Never present a local number as verified, and never attach a currency.
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- The analyzer (caveman learn) is read-only. You are the only writer, and only after a
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yes.
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