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knowledge-agent

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

Skill by thedotmack

IndexedApache-2.0★ 102,760
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/thedotmack-knowledge-agent

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About

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.

Use cases: agent-building, knowledge-base, agent-memory

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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

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  • codeload.github.com · found in install command

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  • ~/.claude/skills/knowledge-agent

What's inside

Skill name
knowledge-agent
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.
License
Apache-2.0
Instructions
77 lines in SKILL.md · view on GitHub

Files (1)

  • SKILL.md

SKILL.md (first lines)

# 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"
```

After rebuilding, reprime to load the updated knowledge:

### Reprime (fresh session)

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

Clears prior Q&A context and reloads the corpus into a new session.

From thedotmack/claude-mem, Apache-2.0.

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102,760
Forks
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Open issues
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Last commit (this folder)
Apr 9, 2026 (6 months ago)
Last release
Oct 23, 2026 (3 days ago)

From GitHub (fetched Oct 24, 2026).

Details

License
Apache-2.0
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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Cite this page

knowledge-agent skill. Agent Mart. https://agentmart.dev/skills/thedotmack-knowledge-agent (last updated 2026-10-24)