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=500Filter 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_corporaShows 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.