Build focused knowledge bases from claude-mem observations, filtered by topic, project, date, or file. Load selected records into an AI conversation to ask contextual follow-up questions about past decisions, fixes, and work patterns.
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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.
安装前可以查看交给 AI 代理的指令内容。
Build and query AI-powered knowledge bases from claude-mem observations.
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".
build_corpus name="hooks-expertise" description="Everything about the hooks lifecycle" project="claude-mem" concepts="hooks" limit=500
Filter options:
project — filter by project nametypes — comma-separated: decision, bugfix, feature, refactor, discovery, changeconcepts — comma-separated concept tagsfiles — comma-separated file paths (prefix match)query — semantic search querydateStart / dateEnd — ISO date rangelimit — max observations (default 500)prime_corpus name="hooks-expertise"
This creates an AI session loaded with all the corpus knowledge. Takes a moment for large corpora.
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.
list_corpora
Shows all corpora with stats and priming status.
rebuild_corpus name="hooks-expertise"
After rebuilding, reprime to load the updated knowledge:
reprime_corpus name="hooks-expertise"
Clears prior Q&A context and reloads the corpus into a new session.
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Turn agent activity and transcript token counts into a cost report organized around completed outcomes. Separate estimates from measured spending, examine waste and rework, and export readable HTML with supporting JSON, CSV, and evidence.
中文简介正在准备中。当前显示英语简介。
Monitor a pull request through reviews and automated checks, verify reported issues against the code, and make focused fixes. Repeat testing and review checks until actionable comments and unresolved threads are cleared and the request is ready to merge.
中文简介正在准备中。当前显示英语简介。
Delegate a coding task to a subagent that runs build, merges the ready pull request, and continues from the updated main branch into release, connecting implementation and release work in one automated sequence.
中文简介正在准备中。当前显示英语简介。
Check a local claude-mem worker, refresh viewer-specific caches of important observations, and exclude or restore records without deleting history. Inspect rules across shared, project, and agent layers, then append conflicts to a report.
中文简介正在准备中。当前显示英语简介。
Monitor a shipped release by checking published packages, live services, installed plugins, and late CI results or user reports. Verify regressions, prepare authorized fixes, and report release health with evidence and any remaining verification gaps.
中文简介正在准备中。当前显示英语简介。
Set up claude-mem for Grok Bot with a local worker and CMEM Pro service for recording observations. The skill also covers an optional host-login setup and remote cmem.ai connection, without requiring Cursor.
中文简介正在准备中。当前显示英语简介。