Claude-mem knowledge-agent

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.

install

source · Clone the upstream repo

git clone https://github.com/thedotmack/claude-mem

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/thedotmack/claude-mem "$T" && mkdir -p ~/.claude/skills && cp -r "$T/plugin/skills/knowledge-agent" ~/.claude/skills/thedotmack-claude-mem-knowledge-agent && rm -rf "$T"

manifest: plugin/skills/knowledge-agent/SKILL.md

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

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

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

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

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)

rebuild_corpus name="hooks-expertise"

After rebuilding, reprime to load the updated knowledge:

Reprime (fresh session)

reprime_corpus name="hooks-expertise"

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