caveman-optimize

Ubah observasi repositori khusus-laporan yang tepat dari Caveman menjadi kandidat optimasi pilihan operator dengan evaluasi baseline/kandidat berpasangan. Gunakan saat pengguna meminta untuk memeriksa observasi optimasi, mengevaluasi perubahan kandidat, atau bertindak berdasarkan laporan optimasi Caveman saat ini. Membutuhkan koneksi Caveman CLI yang masuk dan persetujuan eksplisit; jangan pernah menyimpulkan uang atau aktuasi dari profil.

npx skills add https://github.com/juliusbrussee/caveman --skill 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: <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.

Lebih banyak skill dari juliusbrussee

caveman
juliusbrussee
Mode komunikasi ultra-kompresi. Mengurangi penggunaan token ~75% dengan berbicara seperti manusia gua sambil tetap menjaga akurasi teknis penuh. Mendukung level intensitas: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Gunakan saat pengguna mengatakan "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", atau memanggil /caveman. Juga aktif otomatis saat efisiensi token diminta.
communicationproductivity
caveman-commit
juliusbrussee
Pembangkit pesan commit ultra-kompresi. Mengurangi kebisingan dari pesan commit sambil mempertahankan maksud dan alasan. Format Conventional Commits. Subjek ≤50 karakter, isi hanya jika "mengapa" tidak jelas. Gunakan saat pengguna berkata "tulis commit", "pesan commit", "buat commit", "/commit", atau memanggil /caveman-commit. Aktif otomatis saat perubahan staging.
developmentcode-review
caveman-compress
juliusbrussee
Kompres file memori bahasa alami (CLAUDE.md, todos, preferensi) ke format caveman untuk menghemat token input. Mempertahankan semua substansi teknis, kode, URL, dan struktur. Versi terkompresi menimpa file asli. Cadangan yang dapat dibaca manusia disimpan sebagai FILE.original.md. Pemicu: /caveman-compress FILEPATH atau "compress memory file
developmentdocument
caveman-help
juliusbrussee
Kartu referensi cepat untuk semua mode, skill, dan perintah caveman. Tampilan sekali pakai, bukan mode permanen. Pemicu: /caveman-help, "caveman help", "what caveman commands", "how do I use caveman".
developmentdocumentproductivity
caveman-review
juliusbrussee
Komentar ulasan kode yang sangat ringkas. Mengurangi kebisingan dari umpan balik PR sambil mempertahankan sinyal yang dapat ditindaklanjuti. Setiap komentar adalah satu baris: lokasi, masalah, perbaikan. Gunakan saat pengguna mengatakan "review this PR", "code review", "review the diff", "/review", atau memanggil /caveman-review. Otomatis terpicu saat meninjau pull request.
developmentcode-review
caveman-stats
juliusbrussee
Tampilkan penggunaan token nyata dan perkiraan penghematan untuk sesi saat ini. Membaca langsung dari log sesi Claude Code — tanpa perkiraan AI. Dipicu oleh /caveman-stats. Output disuntikkan oleh hook mode-tracker; model itu sendiri tidak menghitung angka-angka tersebut.
developmentdata-analysis
cavecrew
juliusbrussee
Decision guide for delegating to caveman-style subagents. Tells the main thread WHEN to spawn `cavecrew-investigator` (locate code), `cavecrew-builder` (1-2 file edit), or `cavecrew-reviewer` (diff review) instead of doing the work inline or using vanilla `Explore`. Subagent output is caveman-compressed so the tool-result injected back into main context is ~60% smaller — main context lasts longer across long sessions. Trigger: "delegate to subagent", "use cavecrew", "spawn...
developmentcode-reviewapi
caveman-explore
juliusbrussee
Penjelajah repositori read-only. Gunakan SECARA PROAKTIF untuk eksplorasi cold-start, lokalisasi lintas-file yang luas, atau ketika pencarian langsung gagal dan Anda perlu menemukan di mana sesuatu berada. Lewati ketika isu sudah menyebutkan file atau simbol yang tepat, atau giliran sebelumnya sudah mengembalikan bukti file:line yang dapat digunakan. Mengembalikan hanya kutipan path:line yang ringkas; operasi read dan grep-nya tidak pernah masuk ke percakapan utama.