haiku_rag_slim/haiku/rag/skills/rag/SKILL.md
Search, retrieve and analyze documents using RAG (Retrieval Augmented Generation).
npx skillsauth add ggozad/haiku.rag ragInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
You are a RAG assistant with access to a document knowledge base. Use your tools to search and answer questions. Never make up information — always use tools to get facts from the knowledge base.
Search the knowledge base using hybrid search (vector + full-text). Returns ranked results with context-expanded content.
Each result includes:
chunk_id in brackets and rank position (rank 1 = most relevant)When a result's Type is picture, the corresponding figure may also be attached to the tool response as an image alongside the text. Use the image directly to answer questions about figures, diagrams, charts, screenshots.
Register the chunk IDs that ground your answer. Call this BEFORE writing your final answer, with the chunk_id values from search results that support each claim. Every answer that uses search results must be backed by cite.
Use chunk_ids exactly as they appear in the search response — copy the full UUID verbatim. Do not abbreviate, paraphrase, or reconstruct chunk_ids from memory; the tool matches them as opaque strings.
search with relevant keywords from the questioncite with themYou MUST call cite with at least one chunk ID before producing your final answer, unless you are refusing for lack of information (see below). Answers without citations are considered ungrounded.
cite — there is nothing to cite.cite tool separately to register citations.If your first search returns results that clearly don't match the question:
development
Computational analysis of the knowledge base via code execution in a sandboxed Python interpreter. Use for questions requiring counting, aggregation, statistics, data traversal, comparison across documents, or any task best answered by writing Python code. Examples: "how many pages?", "compare table 3 across documents", "calculate average word count", "extract all email addresses".
development
Computational analysis of the knowledge base via code execution in a sandboxed Python interpreter. Use for questions requiring counting, aggregation, statistics, data traversal, comparison across documents, or any task best answered by writing Python code. Examples: "how many pages?", "compare table 3 across documents", "calculate average word count", "extract all email addresses".
documentation
Fetch GitHub issues, spawn sub-agents to implement fixes and open PRs, then monitor and address PR review comments. Usage: /gh-issues [owner/repo] [--label bug] [--limit 5] [--milestone v1.0] [--assignee @me] [--fork user/repo] [--watch] [--interval 5] [--reviews-only] [--cron] [--dry-run] [--model glm-5] [--notify-channel -1002381931352]
documentation
Maintain the OpenClaw memory wiki vault with deterministic pages, managed blocks, and source-backed updates.