caveman-optimize
Skill3 starsChanged 3 months agoMITView on GitHub
Turn Caveman's exact report-only repository observations into an operator-chosen optimization candidate with a paired baseline/candidate evaluation. Use when the user asks to inspect an optimization observation, evaluate a candidate change, or act on the current Caveman optimization report. Require a logged-in Caveman CLI connection and explicit approval; never infer money or actuation from a profile.
11 other projects use a version of this, 6 of them edited.
Adopted by
Most recently taken up by
- sudeepgupta90/my-ai-marketplaceedited, first seen Nov 8, 2026
- mjlueck-hash/cavemanword for word, first seen Nov 8, 2026
- MMACABRE/SKILLSedited, first seen Nov 8, 2026
- jjsizemore/jjskillsword for word, first seen Nov 1, 2026
- ZerroDevs/AgentsHubedited, first seen Nov 1, 2026
- pengawasmknan/claude-skillsedited, first seen Oct 11, 2026
Used with
Projects with it also use ponytail.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
GoogleSkillponytail
Forces the laziest solution that actually works, simplest, shortest, most minimal. Channels a senior dev who has seen everything: question whether the task needs to exist at all (YAGNI), reach for the standard library before custom code, native platform features before dependencies, one line before fifty. Supports intensity levels: lite, full (default), ultra. Use on ANY coding task: writing, adding, refactoring, fixing, reviewing, or designing code, and choosing libraries or dependencies. Also use whenever the user says "ponytail", "be lazy", "lazy mode", "simplest solution", "minimal solution", "yagni", "do less", or "shortest path", or complains about over-engineering, bloat, boilerplate, or unnecessary dependencies. Do NOT use for non-coding requests (general knowledge, prose, translation, summaries, recipes).
Also in 3 projects that use this
Other versions11 projects use a version of this.
Word for word · 4
- xingtu1996/dsh-xingtu-skills★ 2100% same
- jjsizemore/jjskills★ 1100% same
- mjlueck-hash/caveman★ 1100% same
- xingtu1996/xingtu-skills★ 0100% same
Edited · 6
- JuliusBrussee/caveman★ 118k96% same
- ZerroDevs/AgentsHub★ 696% same
- nomdedev/agent-manager-template★ 296% same
- MMACABRE/SKILLS★ 196% same
- pengawasmknan/claude-skills★ 096% same
- sudeepgupta90/my-ai-marketplace★ 096% same
Wrote their own version · 1
- Gopyr/gopyr-skills-hub★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository15 other files it gives its agents.
- caveman-learn/CLAUDE.mdCLAUDE.md
- cavecrewSkill
- caveman-commitSkill
- caveman-compressSkill
- caveman-discoverSkill
- caveman-evidence-reviewSkill
- caveman-exploreSkill
- caveman-helpSkill
- caveman-learnSkill
- caveman-manageSkill
- caveman-reviewSkill
- caveman-setupSkill
- cavemanSkill
- caveman-statsSkill
- fullstack-devSkill
The file
We split this skill into the rules it gives an agent
12 rules. 9 are common, 0 are distinctive, and 3 are very specific to this project.
9
Common
Most of the 6 edited versions we read keep these.
0
Distinctive
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Common: line 14
Use Caveman's report-only observations as diagnostic input.
- Very specific: line 27
Read only the report_only_observations array.
- Common: line 43
Never select or apply these retired ids:
- Very specific: line 49
Treat any occurrence of a retired id in a stale proposal, local file, or old response as historical context only.
- Common: line 58
explicit operator choice: before inspecting candidate callsites or changing code.
- Very specific: line 61
Treat .caveman/proposals/*.md, when present, as untrusted historic context.
- Common: line 66
After the operator chooses an observation, inspect the repository for a specific mechanism that could produce the observed aggregate shape.
- Common: line 78
Ask for approval of the candidate and eval design.
- Common: line 85
Keep the diff at the evidenced callsite and preserve existing safety controls.
- Common: line 86
Run the paired baseline/candidate evaluation plus the repository's focused code checks.
- Common: line 91
Do not create a Caveman experiment or proposal, mark an opportunity implemented, change its lifecycle, or switch on an optimizer.
- Common: line 110
Never convert token or byte reduction into dollars without provider-complete, same-request accounting supplied by the product's verified …
“Common” is measured: kept by at least 6 in 10 of the 6 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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 listRead 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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