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#ai
knowledge-agent
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.
DeepseekModel
Curated skill
Quality Excellent · 90
v1.0.0
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https://deepseekmodel.com/api/download.php?id=thedotmack-claude-mem-plugin-skills-knowledge-agent-skill-md&format=skill
Download .skill
Standard format with system_prompt and model_config, ready for any agent framework
The actual content of the system_prompt field in the .skill file.
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. 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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The downloaded .skill package contains the following fields.
| Field | Description |
|---|---|
| format | Format tag (skill/v1) |
| skill_id | Unique skill ID |
| name | Skill name |
| version | Version |
| description | Description |
| category | Categories (array) |
| trigger_words | Trigger words |
| tags | Tags |
| source | Source |
| source_url | Source URL (this page) |
| exported_at | Exported at (set per download) |
| system_prompt | System prompt body |
| model_config | Model config: provider / model / temperature / max_tokens / top_p |
| examples | Examples |
| install_guide | Import guide for Coze / Dify / Claude / custom frameworks |
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