# Doc QA

> Answer questions grounded in a set of documents (RAG), with citations.

- Skill: `ninadnj/doc-qa` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add ninadnj/doc-qa`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ninadnj/doc-qa/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: Ninadnj (https://skillmd.com/u/ninadnj)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/ninadnj/doc-qa

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# doc_qa

Retrieval-augmented question answering over a folder of documents.

## How it works
1. Load `.txt` / `.md` / `.pdf` files from the given path.
2. Chunk them with overlap and rank chunks against the question (BM25).
3. Answer **only** from the retrieved passages and **cite the source file** for each claim. If the answer isn't in the documents, say so.

## Guidance for the model
- Ground every statement in the retrieved context; never invent facts.
- Keep answers concise and include the `[source]` for each fact.
- If the context does not contain the answer, reply that it is not in the documents.