# Kb Research

> Kb Research is an Agent Skill for research published in the MODSetter/SurfSense repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Structured approach to finding and synthesizing information from the user's knowledge base

- Skill name: `kb-research`
- Category: [Research](https://www.openparable.com/skill/research/1)
- Source: https://github.com/MODSetter/SurfSense/blob/HEAD/surfsense_backend/app/agents/chat/multi_agent_chat/main_agent/skills/builtin/kb-research/SKILL.md
- Repository: MODSetter/SurfSense (15,263 GitHub stars when collected)

## SKILL.md

# Knowledge-base research

## When to use this skill
- The user asks "find/look up/research" something specifically inside their knowledge base.
- The user references documents, notes, repos, or connector data they expect to exist already.
- A multi-document synthesis is required (e.g., "summarize what we've discussed about X across all my notes").

## Plan
1. Decompose the user's question into 2-4 specific, citation-worthy sub-questions.
2. For each sub-question, run **one** targeted KB search (focused on terms the user would have written, not synonyms). Open the most relevant 2-3 documents fully via `read_file` if their excerpts are too short.
3. Use `grep` to find supporting passages in long files instead of re-reading them end to end.
4. Cite every claim with the `[n]` label shown on the passage you used (search results and `read_file` output both carry them); never write a chunk id, URL, or title yourself.

## What good output looks like
- Short paragraphs with inline citations.
- Quoted phrases when wording matters.
- An explicit "Not found in your knowledge base" callout when a sub-question has no support — never fabricate.

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Source: https://www.openparable.com/skill/research/kb-research (OpenParable). Cite the canonical URL above.