caveman-optimize
Turn a Caveman optimization observation into an operator-chosen candidate with a paired baseline evaluation
Install / Use
npx skills add JuliusBrussee/caveman --skill caveman-optimizeInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
AutomationSupported Platforms
Tags
Our assessment of caveman-optimize
caveman-optimize scores 87/100 on our quality scale, 356th of 1,111 Automation skills we index (top 33%).
Its SKILL.md is 4.6 KB long, split into 6 sections with 2 code examples: a solid amount of guidance for an agent.
With 107,653 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated yesterday, so caveman-optimize is actively maintained.
- No license is declared. By default that means all rights are reserved: you can read it, but reusing or redistributing it is not clearly permitted. Ask the author before building on it commercially.
- Its trust signals score 88/100, with 1 caution from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
caveman-optimize compared with similar skills
All 4 of these similar skills score higher than caveman-optimize; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| caveman-optimize (this skill)by JuliusBrussee | 87 | 107.7k | 1d ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 85.4k | 9d ago | CLAUDE.md |
| rufloby ruvnet | 100 | 73.2k | today | CLAUDE.md |
| Scraplingby D4Vinci | 100 | 83.5k | today | MCP Server |
| algorithmic-artby anthropics | 100 | 177.9k | 2d ago | SKILL.md |
Frequently asked questions
- How do I install caveman-optimize?
- Run
npx skills add JuliusBrussee/caveman --skill caveman-optimize. The install tabs above show the steps for each supported agent. - Which AI agents does caveman-optimize work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is caveman-optimize safe to use?
- It declares no license and scores 88/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is caveman-optimize still maintained?
- The repository was last updated yesterday, so caveman-optimize is actively maintained.
Skill content
View source on GitHubname: caveman-optimize description: > 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.
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-profiletool-catalog-profiletool-output-size-profileexploration-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-bloattool-catalog-utilizationverbose-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.
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Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
