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From knowledge-graph
Knowledge graph maintenance and self-reflection rules. ALWAYS ACTIVE: SELF-REFLECTION TRIGGERS — when these patterns occur, STOP and engage memory: SPINNING WHEELS: Few attempts at same action without progress. → Ask: What am I assuming? Have I seen this before? kg_search or kg_sync. → Capture: meta-learning (user level), specific approach (project level). USER CORRECTION: "No", "that's wrong", "focus", "step back". → STOP. Understand what user wants. Identify the signal you missed. → Capture: the pattern at user level so you recognize it next time. CONFUSION ABOUT KNOWN STATE: "Where is this data?" about something you should know. → Trace data flow explicitly. Don't guess. → Capture: organization (project), your pattern (user). UNEXPECTED RESULT: Tool output doesn't match expectation. → Understand WHY before working around it. → Capture: wrong mental model (user) or undocumented behavior (project). DEJA VU: "I feel like I've solved this before." → Check graph: kg_search. If found: use it. If missing: capture now. SESSION LIFECYCLE: - Start: kg_read(cwd) + scan for relevance (see kg-core) - During: Have you captured anything? If not, why not? Sync periodically. - After completing non-trivial task: What relationships are worth recording? - End/wrap-up: Flush pending insights. What took longer than expected? What helps next session? GRAPH HEALTH AWARENESS: - After kg_read, notice health line. High orphan % = connection opportunities. - After creating a node, connect it with kg_put_edge — one edge makes a node far more valuable. - Nodes without edges risk archival and add cost without compression benefit. MEMORY UPDATE DISCIPLINE: When a memorized approach fails or is corrected: 1. Update the existing node with correct information (don't leave stale data) 2. Scope appropriately — don't narrow to just the current instance if the pattern is general 3. Delete or merge duplicate/outdated nodes
npx claudepluginhub mironmax/claudecode-pluginsHow this skill is triggered — by the user, by Claude, or both
Slash command
/knowledge-graph:kg-maintainThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
A healthy graph is a mesh of connections, not isolated facts. Most nodes participate
Queries and manages a project knowledge graph across tasks, SOPs, memories, and concepts. Useful for recalling patterns, pitfalls, decisions, or related knowledge.
Manages persistent knowledge graph for specs by caching agent discoveries, codebase analysis, patterns, components, and APIs. Use to remember findings across sessions, validate task dependencies, and query prior work.
Manages persistent knowledge tree with namespaces for proactive recall, search, and organization across conversations. Activates on memory requests or technical topics.
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A healthy graph is a mesh of connections, not isolated facts. Most nodes participate in at least one edge. Health stats show this at a glance:
When auditing the graph with kg_read:
Claude Code persists tool results over ~50K chars to disk — model sees only a 2KB preview. kg_read output is the full graph as text. If kg_read shows a size warning (>40K chars):
When project folder renamed, graph slug changes. Server handles via alias detection. If project graph is unexpectedly empty, check ~/.knowledge-graph/projects/ for old name.
Server can be safely restarted (kg-memory restart). Validates PIDs, uses setsid, drains connections, write-through persistence means no data loss.
When project graph is empty: