From claude-mem
Builds and queries AI-powered knowledge bases from claude-mem observations. Filters history into focused corpora on topics like hooks or bugfixes, primes into sessions, and enables conversational Q&A.
How this skill is triggered — by the user, by Claude, or both
Slash command
/claude-mem:knowledge-agentThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Build and query AI-powered knowledge bases from claude-mem observations.
Build and query AI-powered knowledge bases from claude-mem observations.
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".
build_corpus name="hooks-expertise" description="Everything about the hooks lifecycle" project="claude-mem" concepts="hooks" limit=500
Filter options:
project — filter by project nametypes — comma-separated: decision, bugfix, feature, refactor, discovery, changeconcepts — comma-separated concept tagsfiles — comma-separated file paths (prefix match)query — semantic search querydateStart / dateEnd — ISO date rangelimit — max observations (default 500)prime_corpus name="hooks-expertise"
This creates an AI session loaded with all the corpus knowledge. Takes a moment for large corpora.
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.
list_corpora
Shows all corpora with stats and priming status.
rebuild_corpus name="hooks-expertise"
After rebuilding, reprime to load the updated knowledge:
reprime_corpus name="hooks-expertise"
Clears prior Q&A context and reloads the corpus into a new session.
npx claudepluginhub mguttmann/claude-memBuilds and queries AI-powered knowledge bases from claude-mem observations, enabling focused Q&A on past work patterns, decisions, and bugfixes.
Implements 3-layer memory search workflow to recall past work, decisions, errors, and project history token-efficiently via layered functions.