Knowledge Agent
Turn claude-mem observation history into named, filterable corpora you can prime and question like a custom project brain.
Install
npx skills add https://github.com/thedotmack/claude-mem --skill knowledge-agentWhat is this skill?
- Three-step workflow: build_corpus, prime_corpus, then conversational queries against the primed session
- Corpus filters: project, types (decision, bugfix, feature, refactor, discovery, change), concepts, files, semantic query
- Named custom brains—for example hooks expertise, monthly decisions, or worker-service bugfixes
- Semantic search and prefix file matching to slice observation history without exporting raw logs
Adoption & trust: 1.9k installs on skills.sh; 81.2k GitHub stars; 3/3 security scanners passed (skills.sh audits).
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Journey fit
Primary fit
Knowledge agents are built on agent memory tooling—the canonical shelf is Build → agent-tooling where solo builders wire conversational expertise on top of stored observations. The workflow (build_corpus → prime_corpus → query) extends claude-mem itself, not a one-off deploy or marketing task.
Common Questions / FAQ
Is Knowledge Agent safe to install?
skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.
SKILL.md
READMESKILL.md - Knowledge Agent
# 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.