knowledge-agent
Build and query AI-powered knowledge bases from claude-mem observations.
Skill by longhang2004
Chalk Labs
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
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About
Use cases: agent-building
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What's inside
- Skill name
knowledge-agent- Description
- 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.
- License
- MIT
- Instructions
- 77 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# 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.
From longhang2004/Tenvyr, MIT.
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- Last commit (this folder)
- Jul 28, 2026 (3 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/longhang2004/Tenvyr
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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knowledge-agent skill. Agent Mart. https://agentmart.dev/skills/longhang2004-knowledge-agent (last updated 2026-10-24)