Caveman Optimize

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:  — 
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.