Kb Retriever

{
  "name": "kb-retriever",
  "version": "1.0.0",
  "category": "Retrieval / Local Knowledge Base",
  "description": "Local knowledge-base retriever with progressive search. Navigates layered data_structure.md indexes, enforces learn-before-process for PDF and Excel, bounds retrieval to at most five rounds, and answers with sources.",
  "homepage": "https://github.com/ConardLi/garden-skills/tree/main/skills/kb-retriever",
  "compat": [
    "claude-code",
    "claude-ai",
    "cursor",
    "codex-cli",
    "gemini-cli",
    "opencode"
  ]
}


# Kb Retriever Skill — Local Knowledge-Base Retriever

> A skill for AI agents to efficiently answer questions over a **local, multi-format knowledge directory** using hierarchical index navigation and progressive retrieval — without ever loading whole files into context.

[中文文档](./README.zh-CN.md) · [Back to collection root](../../README.md)

![Kb Retriever Skill](https://cdn.jsdelivr.net/gh/ConardLi/assets@main/imgs/kb-retriever-skill.webp)

## What it does

Point the agent at a local directory full of mixed-format files (Markdown, PDF, Excel, …) and ask questions in natural language. The skill:

1. **Walks a hierarchical index** of `data_structure.md` files to figure out *which* files are likely to contain the answer.
2. **Forces a learn-before-process step** when it hits a PDF or Excel — it must read the corresponding `references/*.md` first and use the recommended tool, instead of blindly reading the whole file.
3. **Retrieves progressively** with `grep` + small windowed reads (offset/limit) instead of dumping entire files into context.
4. **Iterates up to 5 rounds**, narrowing keywords each round until it has enough evidence to answer.

---

## Core features

- ✅ **Multi-format**: Markdown / text, PDF, Excel — extensible per file type.
- ✅ **Hierarchical index**: each directory carries its own `data_structure.md`, forming an index tree the agent navigates.
- ✅ **Progressive retrieval**: grep-first, windowed reads, never whole-file loads — keeps token usage low even on large corpora.
- ✅ **Mandatory learning step**: PDF/Excel processing is gated on reading the right `references/*.md` first.
- ✅ **Bounded iteration**: at most 5 retrieval rounds, with explicit termination conditions.

---

## Skill structure

```
skills/kb-retriever/
├── SKILL.md                            Main skill (frontmatter name: kb-retriever)
├── README.md  /  README.zh-CN.md       This document
├── references/
│   ├── pdf_reading.md                  How to handle PDFs (pdftotext / pdfplumber / pypdf)
│   ├── excel_reading.md                How to read Excel with pandas (nrows, dtype, etc.)
│   └── excel_analysis.md               How to filter / aggregate / derive metrics on Excel
└── scripts/
    └── convert_pdf_to_images.py        Convert PDF pages to images when text extraction fails
```

---

## Setting up your knowledge base

This skill **does not ship a knowledge base** — you bring your own. Two ways to wire it up:

### Default location

Put a `knowledge/` directory at the root of the workspace where you invoke the agent:

```
your-project/
├── .claude/skills/  or  .agents/skills/
│   └── kb-retriever/             ← this skill folder
└── knowledge/                    ← ← ← your knowledge base
    ├── data_structure.md         (root-level index, see template below)
    ├── <domain-1>/
    │   ├── data_structure.md
    │   └── ...
    └── <domain-2>/
        └── ...
```

### Custom location

Tell the agent which path to use in your question, e.g. *"answer from `./docs`"* or *"my knowledge base is at `/data/kb`"*. The skill will use that path instead.

If the default `knowledge/` does not exist and the user hasn't specified a path, the skill will ask rather than guess.

### `data_structure.md` template

Each indexed directory should carry one of these:

```markdown
# [Directory name]

## Purpose
What this directory is for and when it should be searched.

## Files
- file1.pdf — what it contains, time / version