# Knowledge Base Manager

A skill that walks you through designing a knowledge base for an AI system: pick RAG, graph or hybrid, then go from source audit to storage.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 438 installs · ★ 38
- Author: daffy0208
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/daffy0208/ai-dev-standards/tree/HEAD/skills/knowledge-base-manager/SKILL.md
- Updated: 2025-12-25
- HTML: https://affpapa.org/en/nevibecode/skills/knowledge-base-manager

## What it does

The skill runs six phases: knowledge audit and architecture, curation and ingestion, storage and retrieval setup, and the rest. First it sorts out what you actually have — text, entities with relationships, or both. That choice drives everything after.

A decision tree sits in the middle. Mostly unstructured text means a document-based KB on RAG, and the skill points you to `rag-implementer` plus `vector-database-mcp`. Structured entities with relationships mean a graph, which needs `knowledge-graph-builder` and `graph-database-mcp`. A mix means hybrid: both skills run, and this one handles the integration.

There is also a list of cases where a knowledge base is the wrong call — static docs are enough, nobody will maintain it, a simple FAQ under 50 items covers everything. Worth checking whether your team has the curation budget before starting. The storage examples name Pinecone, Weaviate, pgvector, Neo4j and ArangoDB; the pick is yours.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add daffy0208/ai-dev-standards --skill knowledge-base-manager -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/knowledge-base-manager/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/knowledge-base-manager/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add daffy0208/ai-dev-standards --skill knowledge-base-manager -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/knowledge-base-manager/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add daffy0208/ai-dev-standards --skill knowledge-base-manager -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/knowledge-base-manager/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install daffy0208/ai-dev-standards knowledge-base-manager
```

2. **Manually: put the files in place**
- File: `.github/skills/knowledge-base-manager/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add daffy0208/ai-dev-standards --skill knowledge-base-manager -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/daffy0208/ai-dev-standards --path skills/knowledge-base-manager
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/knowledge-base-manager/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add daffy0208/ai-dev-standards --skill knowledge-base-manager -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/daffy0208/ai-dev-standards/tree/HEAD/skills/knowledge-base-manager/SKILL.md

## When to use it
- choose between RAG, graph and hybrid for your data
- audit knowledge sources and rank them
- define a metadata schema for documents and an ontology for entities
- track provenance and versions across entries
- decide when a project does not need a knowledge base at all

## Example prompts
- help me design a knowledge base for our documentation
- we have both articles and related entities — which KB type fits
- audit our knowledge sources and propose a schema

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 16d6d157c5fe0609.

BEGIN THIRD-PARTY FILE 16d6d157c5fe0609
```text
---
name: Knowledge Base Manager
description: Design, build, and maintain comprehensive knowledge bases. Bridges document-based (RAG) and entity-based (graph) knowledge systems. Use when building knowledge-intensive applications, managing organizational knowledge, or creating intelligent information systems.
version: 1.0.0
---

# Knowledge Base Manager

Build and maintain high-quality knowledge bases for AI systems and human consumption.

## Core Principle

**Knowledge Base = Structured Information + Quality Curation + Accessibility**

A knowledge base is not just a data dump—it's curated, validated, versioned information designed to answer questions and enable reasoning.

---

## When to Use Knowledge Bases

### Use Knowledge Bases When:

- ✅ Need to answer factual questions consistently
- ✅ Information changes frequently and needs version control
- ✅ Multiple sources need to be unified and reconciled
- ✅ Provenance and citation tracking is critical
- ✅ Building AI systems that need grounded, verifiable information
- ✅ Organizational knowledge needs to be preserved and searchable
- ✅ Complex domain with interconnected concepts

### Don't Use Knowledge Bases When:

- ❌ Static documentation is sufficient (use docs + search)
- ❌ No one will maintain/update it (knowledge rot guaranteed)
- ❌ Simple FAQ covers all questions (<50 items)
- ❌ Information doesn't change (static site faster/cheaper)
- ❌ Team lacks resources for curation

---

## Knowledge Base Types: Decision Framework

### 1. Document-Based Knowledge Base (RAG)

**What it is:** Collection of documents, chunked and embedded for semantic search

**Best for:**

- Technical documentation
- Support articles, FAQs
- Policy documents
- Research papers
- Blog content
- User manuals

**Strengths:**

- Easy to add new documents
- Preserves full context
- Natural for text-heavy content

**Weaknesses:**

- Hard to query relationships ("Who works where?")
- Duplicate information across documents
- Difficult to keep facts consistent

**Use:** `rag-implementer` skill + `vector-database-mcp`

---

### 2. Entity-Based Knowledge Base (Knowledge Graph)

**What it is:** Network of entities (people, places, things) connected by relationships

**Best for:**

- Organizational charts
- Product catalogs with relationships
- Social networks
- Recommendation systems
- Fraud detection
- Supply chain tracking

**Strengths:**

- Excellent for "how are X and Y related?" queries
- Consistent facts (one source of truth)
- Powerful traversal ("friends of friends")

**Weaknesses:**

- Upfront modeling required (ontology design)
```
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