将代码定位、一至两个文件的修改和差异审查分配给专门的辅助智能体,以简短的结构化格式接收结果,减少主智能体的上下文占用。
- 查找定义和调用位置
- 限定在一至两个文件内修改
- 审查代码差异中的缺陷
从Caveman观测报告出发,针对用户选择的对象提出最小代码改动。经批准后,以相同输入比较改动前后的质量和资源用量,为是否采用提供依据。
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.
caveman-optimize以Caveman观测报告为起点,制定具体的代码改动候选方案和比较评估计划。针对用户选定的观测,检查代码中的证据,提出最小改动。经批准后,向改动前后的版本提供相同的固定输入,检查质量,并以统一方法测量词元数、字节数等指标。
适合希望将资源用量观测转化为可验证改进方案的开发者。如果质量下降或结果不明确,会撤销该候选改动,让用户根据实际测量结果决定是否采用。
需要已登录的Caveman CLI。技能会明确要求用户选择对象,并批准候选方案和评估设计。缺少固定输入、质量检查或统一测量方法时会停止。不会仅凭观测断言节省金额或生产环境中的效果,也不会创建Caveman实验或更改管理状态。
本介绍由 AI(OpenAI Codex)根据公开的 SKILL.md 撰写。准确的规格请查看下方原文。
AI 仅根据 SKILL.md 的内容,从 4 个方面进行了评估。星标数和热度不计入评估。
总分按每项 A=25 分、B=15 分、C=5 分相加。 由 AI(OpenAI Codex)于 2026年10月20日 评估。仅供参考,安装前请同时查看原文。
安装前可以查看交给 AI 代理的指令内容。
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.
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-profiletool-catalog-profiletool-output-size-profileexploration-load-profileThese 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-bloattool-catalog-utilizationverbose-tool-outputTreat 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.
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.
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:
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.
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.
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.
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将代码定位、一至两个文件的修改和差异审查分配给专门的辅助智能体,以简短的结构化格式接收结果,减少主智能体的上下文占用。
将智能体回复整理为先给结论的简短文字,省去问候和冗长铺垫,同时保留代码、数值、否定与例外条件等必要信息,兼顾可读性。
按Conventional Commits格式生成简短的提交消息,说明代码变更意图,并为破坏兼容性的变更、迁移等情况补充正文背景。
将CLAUDE.md、任务列表等文件中的自然语言压缩为简短表述,减少输入令牌。保留代码与文档结构,并在单独位置备份便于阅读的原文。
按具体工作梳理代码中使用大语言模型的流程,先提出用途标签方案,获批后再添加标签,便于在 Caveman Cloud 中按流程分别查看费用。
以只读方式检查Caveman Cloud的费用、响应时间和错误,对照汇总数据与单次执行记录,依据证据整理支出构成及可改进之处。