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knowledge-agent

knowledge-agent

github.com/thedotmackAgent workflows104.0k5.8k installsUpdated today
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About knowledge-agent

knowledge-agent builds focused knowledge bases from claude-mem observations and lets users query them in persistent conversational sessions. It is intended for people who want to retrieve answers from selected project history.

Key features

  1. 01Build corpora with observation filters
  2. 02Prime corpora into AI sessions
  3. 03Ask questions with follow-up context
  4. 04List corpus statistics and status
  5. 05Rebuild or reprime corpora

Use cases

  1. 01Query decisions from a selected date range
  2. 02Ask about bugs or features in a project
  3. 03Maintain a focused session for recurring questions

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/thedotmack/claude-mem --skill knowledge-agent

After installing, type/knowledge-agentin Claude Code to confirm it loaded.

Details

From the project README

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.

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