Delegate code lookup, small edits, and diff reviews to three specialized subagents. Their compact, structured results help conserve the main agent's context, with guidance on when to delegate and when to keep work in the main conversation.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
- Locating definitions and callers
- Making a focused edit across one or two files
- Reviewing a diff for bugs
JuliusBrussee/caveman☆ 11.4만2026년 10월 18일 업데이트
Make agent replies shorter and easier to scan by putting answers first and removing filler. Preserves technical details, exact commands, numbers, and essential conditions while keeping the user's chosen language.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
- Reading concise replies in a terminal
- Getting conclusions before explanations
- Reducing wording while keeping technical details
JuliusBrussee/caveman☆ 11.4만2026년 10월 18일 업데이트
Create short commit messages using Conventional Commits, a format that labels the type of change. Emphasize intent, keep the subject concise, and include explanatory text when breaking changes, security fixes, data migrations, or reversions require context.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
- Writing a commit message for code changes
- Standardizing commit message format
- Documenting breaking changes and migration steps
JuliusBrussee/caveman☆ 11.4만2026년 10월 18일 업데이트
Compress memory files and task lists into shorter wording to reduce input tokens. Keeps code and document structure intact, saves a readable backup separately, and sends the text to the configured model provider.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
- Shortening CLAUDE.md to reduce input tokens
- Compressing task lists and preference files
- Keeping readable backups of compressed files
JuliusBrussee/caveman☆ 11.4만2026년 10월 18일 업데이트
Find LLM workflows in a codebase and label them by the job they perform, so Caveman Cloud can separate spending by workflow. Present a labeling proposal for approval, apply focused code changes, and check an affected execution path.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
- Breaking down LLM spending by job
- Inventorying LLM use in APIs, scheduled jobs, and scripts
- Separating evaluation spending from other workflows
JuliusBrussee/caveman☆ 11.4만2026년 10월 18일 업데이트
Review Caveman Cloud spending, latency, errors, and potential improvements using current reports and traces, or individual execution records. Produce findings tied to time windows and trace IDs, while keeping measured costs, estimated opportunities, and verified savings separate.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
- Understanding which workflows drive LLM spending
- Investigating slow responses and errors
- Reviewing measured costs and verified savings separately
JuliusBrussee/caveman☆ 11.4만2026년 10월 18일 업데이트