遷移
在編輯前，先對映當前讀取者、寫入者、資料形狀、相容性窗口與所有權。
- 定義前進路徑與回滾路徑。
- 保留現有資料；破壞性步驟必須明確宣告並單獨授權。
- 在部署可能重疊的情況下，確保混合版本操作的安全。
- 依序執行展開、遷移、驗證，再於適用時進行收縮。
- 讓重試具有冪等性，並使部分失敗可被觀察。
- 在必要的轉換階段驗證新舊路徑。
在請求的階段通過後停止；切勿隱含地執行後續的破壞性收縮。
實作可逆且相容安全的轉換。適用於需要回滾與保留驗證的 schema、資料、API、協定、設定或相依性遷移。
此技能提供一個結構化框架，用於在允許回滾與向後相容性的前提下，安全且可驗證地執行各種遷移（如 schema、資料、API、協定、設定或相依性）。其核心原則包括：先對當前讀寫者、資料形狀、相容性窗口與所有權進行對映，再進行編輯；明確定義前進路徑與回滾路徑；保留現有資料，且任何破壞性步驟都必須明確宣告並單獨授權。為支援分階段部署（逐步上線），該技能確保混合版本操作的安全，並遵循「展開、遷移、驗證、收縮」的順序，其中收縮（破壞性）階段絕不會隱含執行，必須在明確請求後才進行。此外，它強調重試的冪等性，並讓部分失敗可被觀察。支援的平台包括 Claude Code、Cursor、Codex 等 AI 代理工具。
npx skills add https://github.com/JuliusBrussee/caveman/tree/main/skills/migration在編輯前，先對映當前讀取者、寫入者、資料形狀、相容性窗口與所有權。
在請求的階段通過後停止；切勿隱含地執行後續的破壞性收縮。
This repo contains 12 individual skills — each has its own dedicated page.
Find and label every LLM workflow in the repository so Caveman Cloud groups spend by workflow instead of one bucket. Use for "discover workflows" or breaking LLM spend down by workflow.
Read-only review of Caveman Cloud evidence: cost, Cave Score, workflows, traces, latency, errors, routing, savings. Use when asked what Caveman found or where LLM spend goes.
Read-only repository explorer for cold-start orientation, broad cross-file localization, or when a direct search failed. Skip it when the exact file or symbol is already named. Returns path:line citations only; its reads stay out of main context.
Act on a Caveman learn report - review the ranked token sinks, apply cost-lowering fixes with per-edit consent, and report what those fixes returned. Use when asked to lower an agent's token cost, what caveman has saved, to trim a heavy CLAUDE.md, or to offload re-pasted context into cavemem.
Inspect Caveman Cloud's experiment lifecycle and block unsafe execution. Use when asked to start, approve, cancel, promote or roll back a Caveman experiment.
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.
Wire a repository through the Caveman Cloud gateway so every LLM request is measured, with no behavior change. Use for "set up caveman" or adding LLM spend observability.
Diagnose ambiguous failures before editing. Use for unknown causes, intermittent behavior, performance regressions, or investigations needing evidence-ranked hypotheses.
Build feature work with high overbuilding risk. Use for new behavior, product slices, or integrations where repository reuse, strict scope, and an explicit stop condition matter.
Restructure code while preserving behavior. Use for extraction, consolidation, ownership moves, or cleanup where verification must bracket structural edits.
Fix bugs and small behavior changes at the narrowest responsible layer. Use when regression proof, preserved surrounding behavior, and task-relevant tests matter.
Prove existing work meets acceptance conditions without expanding scope. Use for validation-only tasks, completion checks, focused gate runs, and last-mile proof.
ElevenLabs text-to-speech with mac-style say UX.
Oracle CLI second-model review/debug/refactor/design with selected files, dry-run token checks, API or browser engine.
Capture and automate macOS UI with the Peekaboo CLI.
Generate multiple radically different interface designs for a module using parallel sub-agents. Use when user wants to design an API, explore interface options, compare module shapes, or mentions "design it twice".
Build a throwaway prototype to flesh out a design — a runnable terminal app for state/business-logic questions, or several radically different UI variations toggleable from one route.
Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".