# Build Knowledge Bases

Builds focused knowledge bases from observation history and answers questions on them.

- Repository: https://github.com/thedotmack/claude-mem (109099 stars)
- Category: Data and ML
- License: Apache-2.0
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add thedotmack/claude-mem --skill knowledge-agent -g -a claude-code -y`
Codex: `npx skills add thedotmack/claude-mem --skill knowledge-agent -g -a codex -y`
Cursor: `npx skills add thedotmack/claude-mem --skill knowledge-agent -g -a cursor -y`
Gemini CLI: `npx skills add thedotmack/claude-mem --skill knowledge-agent -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add thedotmack/claude-mem`

## Description

Build and query AI-powered knowledge bases from claude-mem observations. Use when users want to create focused "brains" from their observation history, ask questions about past work patterns, or compile expertise on specific topics.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Knowledge Agent

Build and query AI-powered knowledge bases from claude-mem observations.

## What Are Knowledge Agents?

Knowledge agents are filtered corpora of observations compiled into a conversational AI session. Build a corpus from your observation history, prime it (loads the knowledge into an AI session), then ask it questions conversationally.

Think of them as custom "brains": "everything about hooks", "all decisions from the last month", "all bugfixes for the worker service".

## Workflow

### Step 1: Build a corpus

```text
build_corpus name="hooks-expertise" description="Everything about the hooks lifecycle" project="claude-mem" concepts="hooks" limit=500
```

Filter options:
- `project`, filter by project name
- `types`, comma-separated: decision, bugfix, feature, refactor, discovery, change
- `concepts`, comma-separated concept tags
- `files`, comma-separated file paths (prefix match)
- `query`, semantic search query
- `dateStart` / `dateEnd`, ISO date range
- `limit`, max observations (default 500)

### Step 2: Prime the corpus

```text
prime_corpus name="hooks-expertise"
```

This creates an AI session loaded with all the corpus knowledge. Takes a moment for large corpora.

### Step 3: Query

```text
query_corpus name="hooks-expertise" question="What are the 5 lifecycle hooks and when does each fire?"
```

The knowledge agent answers from its corpus. Follow-up questions maintain context.

### Step 4: List corpora

```text
list_corpora
```

Shows all corpora with stats and priming status.

## Tips

- **Focused corpora work best**, "hooks architecture" beats "everything ever"
- **Prime once, query many times**, the session persists across queries
- **Reprime for fresh context**, if the conversation drifts, reprime to reset
- **Rebuild to update**, when new observations are added, rebuild then reprime

## Maintenance

### Rebuild a corpus (refresh with new observations)

```text
rebuild_corpus name="hooks-expertise"
```

The rest: https://github.com/thedotmack/claude-mem/blob/HEAD/plugin/skills/knowledge-agent/SKILL.md