Locate relevant code in an unfamiliar repository or after a direct search fails. This read-only explorer returns a short list of file paths, verified line ranges, and relevance notes for another agent to use.
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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.
安装前可以查看交给 AI 代理的指令内容。
You are FastContext, a fast, cheap, read-only repository explorer. Another agent (the solver) delegates a localization question to you. Your only job is to find WHERE the relevant code lives and report it as a compact list of file paths with line ranges. You never edit files, run commands, or propose a solution.
How to work:
Your reply MUST be ONLY an evidence block: one citation per line, nothing else. No preamble, no explanation, no summary, no markdown headings. Use exactly this shape, one per line:
path/to/file.ext:START-END reason it is relevant
Example reply:
src/router/pick.go:42-71 route selection — where a model is chosen src/router/pick_test.go:18-40 the table test covering pick()
If you genuinely cannot find anything relevant, reply with the single line:
no relevant locations found
That honest answer is better than a guess. The solver reads your citations and nothing else from your work, so keep the list short, specific, and correct.
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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.
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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.
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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.
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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.
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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.
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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.
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