6c8d178f5d
- 全局重命名 claude-mem → codebuddy-mem - AI 后端改为 DeepSeek V4 直连 - 适配 CodeBuddy Code 作为 MCP 客户端 - 修复 GS 函数 timeoutMs bug - 新增 README / CHANGELOG / UPSTREAM / install.sh - 协议：AGPL-3.0
81 lines
2.4 KiB
Markdown
81 lines
2.4 KiB
Markdown
---
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name: knowledge-agent
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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.
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---
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# Knowledge Agent
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Build and query AI-powered knowledge bases from claude-mem observations.
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## What Are Knowledge Agents?
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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.
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Think of them as custom "brains": "everything about hooks", "all decisions from the last month", "all bugfixes for the worker service".
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## Workflow
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### Step 1: Build a corpus
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```text
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build_corpus name="hooks-expertise" description="Everything about the hooks lifecycle" project="claude-mem" concepts="hooks" limit=500
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```
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Filter options:
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- `project` — filter by project name
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- `types` — comma-separated: decision, bugfix, feature, refactor, discovery, change
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- `concepts` — comma-separated concept tags
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- `files` — comma-separated file paths (prefix match)
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- `query` — semantic search query
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- `dateStart` / `dateEnd` — ISO date range
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- `limit` — max observations (default 500)
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### Step 2: Prime the corpus
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```text
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prime_corpus name="hooks-expertise"
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```
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This creates an AI session loaded with all the corpus knowledge. Takes a moment for large corpora.
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### Step 3: Query
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```text
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query_corpus name="hooks-expertise" question="What are the 5 lifecycle hooks and when does each fire?"
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```
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The knowledge agent answers from its corpus. Follow-up questions maintain context.
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### Step 4: List corpora
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```text
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list_corpora
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```
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Shows all corpora with stats and priming status.
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## Tips
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- **Focused corpora work best** — "hooks architecture" beats "everything ever"
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- **Prime once, query many times** — the session persists across queries
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- **Reprime for fresh context** — if the conversation drifts, reprime to reset
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- **Rebuild to update** — when new observations are added, rebuild then reprime
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## Maintenance
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### Rebuild a corpus (refresh with new observations)
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```text
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rebuild_corpus name="hooks-expertise"
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```
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After rebuilding, reprime to load the updated knowledge:
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### Reprime (fresh session)
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```text
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reprime_corpus name="hooks-expertise"
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```
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Clears prior Q&A context and reloads the corpus into a new session.
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