# Kb Research

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

- Skill: `gabrielmoreira/kb-research` (Agent Skill)
- Install (CLI): `npx skillmds@latest add gabrielmoreira/kb-research`
- Raw SKILL.md: https://api.skillmd.com/api/skills/gabrielmoreira/kb-research/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: gabrielmoreira (https://skillmd.com/u/gabrielmoreira)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/gabrielmoreira/kb-research

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# 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.