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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.
Facilitate a read-only standup across git worktrees, branches, or PRs to compare changes and produce one consolidation plan.
Token-optimized structural code search using tree-sitter AST parsing. Use instead of reading full files when you need to understand code structure, find functions, or explore a codebase efficiently.
Create a detailed, phased implementation plan with documentation discovery. Use when asked to plan a feature, task, or multi-step implementation — especially before executing with do.
Cluster a GitHub issue backlog by root cause into a small set of plan-master issues, redirect children with a standardized comment, and bundle architectural-fix PRs that close clusters atomically. Use when an issue tracker has accumulated dozens of reports that share underlying defects, when asked to triage / consolidate / cluster / dedupe issues, when asked to build a plan series or roadmap from open issues, or when routing a new incoming bug into an existing plan.
Generate a serial week-by-week narrative digest of a project's full claude-mem timeline. Splits the timeline into per-ISO-week files, then runs one consecutive subagent per week — each receiving the prior week's carry-forward block — to produce one chapter per ISO week of data. Use when asked for "weekly digests", "week-by-week story", "serial timeline", or "narrative chapters" of a project's history.
Audit a design against Dieter Rams' ten "Good design is..." principles, then hand off a /make-plan prompt for one of three outcomes — new design, refine design, or redesign. Use when the user says "audit this design", "design review", "check this UI against Rams", "is this UI good", "critique this design", "design audit", or asks for a critique that should lead to a plan.
| 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. |
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.