先調查再行動 是做什麼的？
在修改產品程式碼之前，先收集證據。
- 將觀察到的症狀與推斷的原因分開。
- 追蹤輸入、狀態轉換、所有權邊界與失敗輸出。
- 根據證據與低成本的反駁價值對假設進行排序。
- 在有一個可信機制能夠解釋所有證據之前，不要編輯程式碼。
- 當證據足以命名原因或明確的阻礙時，停止探索。
報告原因與證明。除非任務授權實作，否則不進行修復。
在診斷不明確的失敗原因時，先收集證據再修改程式碼。適用於未知原因、間歇性行為、效能回歸或需要證據排序假設的調查。
「先調查再行動」技能旨在指導 AI 代理在遇到不明確的錯誤或異常行為時，採取一套嚴謹的診斷流程，避免未經充分分析就貿然修改程式碼。此技能強制代理區分觀察到的症狀與推斷出的原因，並系統性地追蹤輸入、狀態轉換、所有權邊界與失敗輸出。代理需根據證據與低成本的反駁價值對假設進行排序，直到找到一個足以解釋所有證據的可信機制為止。此技能支援 Claude Code、Cursor 等命令列或編輯器整合的 AI 代理，特別適合處理間歇性錯誤、效能回歸或需要深入調查的複雜問題。使用此技能時，代理只會報告根本原因與證明，除非任務明確授權，否則不會進行修復。
npx skills add https://github.com/JuliusBrussee/caveman/tree/main/skills/investigate-firstInstalled? Explore more 程式設計與開發 skills: steipete/bluebubbles, steipete/eightctl, steipete/blucli · View all 6 →
在修改產品程式碼之前，先收集證據。
報告原因與證明。除非任務授權實作，否則不進行修復。
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.
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.
Implement reversible compatibility-safe transitions. Use for schema, data, API, protocol, configuration, or dependency migrations requiring rollback and preservation proof.
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.
Send and manage iMessages via BlueBubbles, including attachments, tapbacks, edits, replies, and groups.
Control Eight Sleep pods (status, temperature, alarms, schedules).
BluOS CLI (blu) for discovery, playback, grouping, and volume.
Create, search, and manage Bear notes via grizzly CLI.
Capture frames or clips from RTSP/ONVIF cameras.
Search GIF providers with CLI/TUI, download results, and extract stills/sheets.