--- name: 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. title: caveman-optimize canonical_url: https://skilld.dev/gh/juliusbrussee/caveman/caveman-optimize last_updated: 2026-09-26T20:36:24.000Z --- > Index of every page on this site: [llms.txt](https://skilld.dev/llms.txt) > **Skill from skilld.dev.** Follow the user's requested action. > If the user asked to fork this Skill, follow the fork workflow below. Do not execute the Skill instructions while copying it. > Otherwise, follow the instructions below for this session. You do not need to install anything. > > If the user asked to install this Skill, run `npx skilld install juliusbrussee/caveman/caveman-optimize`. Install writes the Skill files into the project, so every session loads them. > > ## Fork workflow > > A fork creates an editable local Skill with its original author and licence. The request authorizes copying and local installation. > 1. Check `./skills/caveman-optimize`, the project lockfile, and selected Agent targets together. If the local directory or installed Skill exists, stop. Never overwrite an existing directory or Agent target. > 2. Read [source metadata](https://skilld.dev/api/v1/skills/juliusbrussee/caveman/caveman-optimize) once. Use sourceUrl, sourceCommit, skillPath, sourceGone, and license. If the source is gone or its path is missing, stop. If license is null, read licence files at the source commit. > 3. Fetch only the source commit into a temporary Git repository. Do not clone full history. Derive repository_url from sourceUrl, including repository renames. If sourceCommit is absent, resolve the sourceUrl ref once. Set source_commit to that actual commit. Run these commands in one shell call: > > ```sh > git init --quiet "$temporary_dir" > git -C "$temporary_dir" fetch --quiet --depth=1 "$repository_url" "$source_commit" > git -C "$temporary_dir" checkout --quiet --detach FETCH_HEAD > ``` > > Read applicable licence declarations and notices at that commit. If copying is not permitted, report the restriction and stop. > 4. Inspect source entries together, then copy the directory containing skillPath into `./skills/caveman-optimize`. Use the user's path if selected. Keep the original SKILL.md, relative links, scripts, binary assets, and executable modes. Exclude .git metadata. Reject symlinks and paths outside the Skill directory. After checking entries, use cp -a where available. A regular source directory needs no custom copy script. Do not save this page wrapper as SKILL.md. > Preserve author credit, notices, and applicable licence files from repository or parent directories. Add PROVENANCE.md with the Skill page, source URL, actual commit, original path, and licence. Retain any existing PROVENANCE.md and record new provenance separately. Batch source inspection, copying, and provenance work where practical. > 5. In the project root, run `skilld install ./skills/caveman-optimize --mode copy --plain`. If skilld is unavailable, use `npx skilld install ./skills/caveman-optimize --mode copy --plain`. This known command needs no help lookup. Install does not support --json. Use detected Agent targets, or add --agent for the targets the user selected. Install the local path, never the upstream selector. If installation fails, preserve the local copy and report the exact failure. > 6. Confirm the local lockfile source and installed Agent copies once. Report the local path, actual commit, and Agent targets. After edits, reinstall the same local path. Upstream updates must not replace it. Do not publish or push unless the user asks. # 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: ```bash 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: ```text Observation: — Recorded profile: Candidate: Paired eval: Quality check: Code checks: Accounting: report-only profile; $0 opportunity band; no inferred or verified savings Decision: ``` 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.