Build a narrowly scoped feature or integration using existing repository structures. Define observable acceptance criteria and non-goals, complete the working path across relevant layers, and stop when focused checks confirm the requested outcome.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
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.
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Native Core's architecture-first simplicity remains mandatory. Turn feature into complete narrow outcome fitting system.
Exercise path. Run focused proof. Stop when acceptance passes. Report only material omissions and trigger.
아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
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.
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.
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.
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.
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.
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.