先调查再行动 是做什么的？
在修改产品代码之前，先收集证据。
- 将观察到的症状与推断的原因分离。
- 追踪输入、状态转换、所有权边界和失败输出。
- 按证据和低成本证伪价值排序假设。
- 除非有一个可信机制能解释所有证据，否则不要编辑代码。
- 当证据足以确定原因或精确阻塞点时，停止探索。
报告原因和证明。除非任务明确授权实施，否则不进行修复。
在编辑代码之前，先诊断模糊的错误。适用于未知原因、间歇性行为、性能回归或需要基于证据排序假设的调查。
该技能强制要求开发者在对产品代码进行任何修改之前，先系统性地收集证据，避免因猜测根源而引入新问题。它适用于处理未知原因的错误、间歇性行为、性能回归或任何需要基于证据排序假设的调查场景。技能支持所有平台和AI编码工具（例如Claude Code、Cursor、Codex等），并严格遵循分离症状与推断原因、追踪输入/状态转换/所有权边界/失败输出、按证据和低成本证伪价值排序假设、以及在没有一个可信机制能解释所有证据前不进行编辑的核心原则。一旦收集到足以确定原因或精确阻塞点的证据，调查即结束并报告原因和证明，除非任务明确授权否则不实施修复。这种严谨的流程能有效减少调试中的无效编辑和二次故障，提升开发效率。
npx skills add https://github.com/JuliusBrussee/caveman/tree/main/skills/investigate-firstInstalled? Explore more 编程与开发 skills: steipete/bluebubbles, steipete/eightctl, steipete/blucli · View all 6 →
在修改产品代码之前，先收集证据。
报告原因和证明。除非任务明确授权实施，否则不进行修复。
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.