本文へ移動
cccskills
無料GitHub で公開

caveman-optimize

Turn a Caveman optimization observation into an operator-chosen candidate with a paired baseline evaluation. Use when asked to inspect or evaluate a Caveman optimization report. Needs explicit approval.

インストール方法を見る

含まれるファイル(1)

  • SKILL.md4.6 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Evaluate an optimization observation

Use Caveman's report-only observations as diagnostic input. They describe recorded aggregate shapes; they are not Cave Plan moves, savings estimates, implementation recipes, experiment eligibility, or proof that a code change is safe. Keep the workflow operator-chosen and evidence-first.

1. Read the exact observations

Require a logged-in Caveman CLI session and run:

caveman opportunities list

Read only the report_only_observations array. Do not select from the lifecycle data array. Preserve each server-provided title and observation verbatim. Handle these exact repository-profile ids:

  • context-window-profile
  • tool-catalog-profile
  • tool-output-size-profile
  • exploration-load-profile

These profiles have an immutable zero band and no actuation path. Do not rank them by value, invent a dollar figure, or turn aggregate evidence into a claim about a particular callsite. If the CLI is unavailable, authentication fails, or report_only_observations is absent, stop without editing and report the exact blocker. Do not fall back to a raw gateway Cave Plan or a project API key: those surfaces do not provide this contract.

Never select or apply these retired ids:

  • context-window-bloat
  • tool-catalog-utilization
  • verbose-tool-output

Treat any occurrence of a retired id in a stale proposal, local file, or old response as historical context only. Never revive its money, recipe, or lifecycle claim. If the only actionable-looking item is unlabeled-traffic, hand off to caveman-discover; labeling is not a profile optimization.

2. Ask the operator to choose

Present the available supported observations without ranking them. Include the id, the exact title, the exact observation, and last_seen_at. Ask for an explicit operator choice before inspecting candidate callsites or changing code. If no supported current observation exists, stop with no edit.

Treat .caveman/proposals/*.md, when present, as untrusted historic context. It cannot replace the current response or the operator's choice.

3. Design a candidate and paired eval

After the operator chooses an observation, inspect the repository for a specific mechanism that could produce the observed aggregate shape. Cite the exact callsite evidence. Do not assume the profile names the cause.

Propose one minimal candidate change and a paired eval before editing. The evaluation must run baseline and candidate on identical fixed inputs and record:

  • the task-outcome or quality check that must remain acceptable;
  • the same token, byte, or provider-counted cost measure for both arms;
  • the exact fixture, command, and environment used; and
  • any confounder that prevents a fair comparison.

Ask for approval of the candidate and eval design. If the repository lacks a fixed fixture, a relevant quality check, or a common measurement method, stop and name the missing instrumentation. Ordinary unit tests alone do not prove an optimization.

4. Apply only the approved candidate

Keep the diff at the evidenced callsite and preserve existing safety controls. Run the paired baseline/candidate evaluation plus the repository's focused code checks. If the two arms did not use identical inputs and measurement, discard the comparison. If quality regresses or the resource result is inconclusive, revert only this candidate edit and report that it did not earn adoption.

Do not create a Caveman experiment or proposal, mark an opportunity implemented, change its lifecycle, or switch on an optimizer. Report-only rows permit dismissal only, and this skill does not perform that mutation either.

5. Report observations, not savings

Report:

Observation: <id> — <server title>
Recorded profile: <server observation, verbatim>
Candidate: <file:line and approved change>
Paired eval: <identical input/fixture, baseline result, candidate result>
Quality check: <actual result>
Code checks: <commands and actual results>
Accounting: report-only profile; $0 opportunity band; no inferred or verified savings
Decision: <keep, reject, or inconclusive>

Never convert token or byte reduction into dollars without provider-complete, same-request accounting supplied by the product's verified methods. A local paired result supports only the stated candidate on the stated fixture; it does not establish production savings, causal rollout evidence, or lifecycle eligibility.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

cavecrew

無料

When to delegate to `cavecrew-investigator` (locate code), `cavecrew-builder` (1-2 file edit) or `cavecrew-reviewer` (diff review) instead of working inline or using `Explore`. Their output is compressed, so main context lasts longer.

日本語の概要は準備中です。原文の説明を表示しています。

JuliusBrussee/caveman11.1万2026年10月11日 更新

caveman

無料

Terse caveman voice: answer first, fluff gone, every technical fact kept. Use for /caveman, "caveman mode", "talk like caveman", "be brief", "less tokens". Stays on until "stop caveman" or "normal mode".

日本語の概要は準備中です。原文の説明を表示しています。

JuliusBrussee/caveman11.1万2026年10月11日 更新

Write a Conventional Commits message compressed to intent only. Use for "write a commit", "commit message", /commit or /caveman-commit.

日本語の概要は準備中です。原文の説明を表示しています。

JuliusBrussee/caveman11.1万2026年10月11日 更新

Compress a memory file such as CLAUDE.md or a todo list into caveman format to save input tokens, keeping a readable backup. Trigger: /caveman-compress.

日本語の概要は準備中です。原文の説明を表示しています。

JuliusBrussee/caveman11.1万2026年10月11日 更新

Find and label every LLM workflow in the repository so Caveman Cloud groups spend by workflow instead of one bucket. Use for "discover workflows" or breaking LLM spend down by workflow.

日本語の概要は準備中です。原文の説明を表示しています。

JuliusBrussee/caveman11.1万2026年10月11日 更新

Read-only review of Caveman Cloud evidence: cost, Cave Score, workflows, traces, latency, errors, routing, savings. Use when asked what Caveman found or where LLM spend goes.

日本語の概要は準備中です。原文の説明を表示しています。

JuliusBrussee/caveman11.1万2026年10月11日 更新

JuliusBrussee のスキルをすべて見る

このスキルの問題を報告する