原因不明のエラー、断続的な挙動、パフォーマンス低下、または証拠に基づいた仮説のランク付けが必要な調査に使用します。曖昧な障害を診断してから編集を行います。
このスキルは、エラーや障害の原因がはっきりしない場合に、コードを直接編集する前に徹底的な調査を優先するためのガイドラインです。観測された症状と推定原因を明確に区別し、入力値、状態遷移、所有権の境界、障害出力などをトレースすることで、証拠に基づいた仮説を立てます。仮説は証拠の強さと反証のしやすさでランク付けされ、一貫性のあるメカニズムが特定されるまで編集は行いません。主にClaude CodeやCursorなどのAIエージェント向けに設計されており、開発者が不確かな変更を行うリスクを減らし、正確な原因特定と報告に集中できるようにします。このスキルはマークダウン形式で提供され、タスクが実装を許可するまでは修正を行わないという規律を徹底します。
npx skills add https://github.com/JuliusBrussee/caveman/tree/main/skills/investigate-firstInstalled? Explore more コーディング＆開発 skills: steipete/bluebubbles, steipete/eightctl, steipete/blucli · View all 6 →
製品コードを変更する前に証拠を収集します。
原因と証拠を報告します。タスクが実装を承認しない限り修正は行いません。
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.