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From knowledge-graph
Knowledge graph persistent memory system. CRITICAL behavioral rules: SESSION AWARENESS: Before starting ANY task, check if kg_read has been called this session. If graph is not loaded (no node/edge data visible in recent context), IMMEDIATELY run: kg_read(cwd="<project root>") This applies to new sessions, resumed sessions, and sessions continuing after context compaction. If kg_read fails (connection refused), tell user: "Memory server not running. Start with kg-memory start?" AFTER LOADING: Review ALL user-level nodes — they contain working style rules, pitfall patterns, confirmed preferences. Treat as defaults. Review project-level nodes for architecture and decisions. Scan archived node IDs — if ANY feel related to upcoming work, read them in full with kg_read(cwd, id). Err on reading too many. A wasted read = 1 tool call. Missing context = failed task. SELF-AWARENESS CHECK: If you encounter a problem class that "feels familiar" (permissions, caching, deployment, file ownership, etc.) — STOP and kg_search before attempting a solution. Your memory likely has the answer. Never guess when you can check. TWO STORAGE LEVELS: - user: cross-project wisdom (preferences, principles, meta-learnings, user profile) - project: codebase-specific (architecture, decisions, dependencies, patterns) TWO ENTRY TYPES: - node: named concept/pattern/insight (id + gist + optional notes/touches) - edge: relationship between nodes/files/concepts (from + to + rel + optional notes) CORE PRINCIPLE: Compress meaning. Maximum insight per symbol. Prefer edges over new nodes. If something can be expressed as a relationship between existing things, use an edge. API (8 tools): kg_read(cwd, id?, level?) → init session + full graph, or single node with full content kg_search(query, session_id?) → full-text search across both levels kg_put_node(session_id, level, id, gist, notes?, touches?) → create/update node kg_put_edge(session_id, level, from, to, rel, notes?) → create/update edge kg_delete_node(session_id, id) → delete node (auto-resolves level) kg_delete_edge(session_id, from, to, rel) → delete edge (auto-resolves level) kg_sync(session_id) → changes from other sessions kg_progress(session_id, task_id, state?, level?) → get/set task progress
npx claudepluginhub mironmax/claudecode-pluginsHow this skill is triggered — by the user, by Claude, or both
Slash command
/knowledge-graph:kg-coreThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Every session, immediately — before any task work:
Queries and manages a project knowledge graph across tasks, SOPs, memories, and concepts. Useful for recalling patterns, pitfalls, decisions, or related knowledge.
Searches knowledge graph, loads session context, views history, checks status, and visualizes memory for recalling past decisions in coding sessions.
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.
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Every session, immediately — before any task work:
kg_read(cwd="<project root>") # Returns full graph + session_id
The returned session_id is used for all subsequent tool calls (writes, deletes, sync).
If resuming a session (context suggests prior conversation), try kg_sync(session_id) first.
If that fails (unknown session), run the full startup sequence.
All sessions share the same server. kg_sync(session_id) pulls changes from other sessions.
Call sync: before decisions depending on shared knowledge, when another session may have been active,
every 30+ min in long sessions, after spawning subagents that write to the graph.
| Skill | Purpose |
|---|---|
/skill kg-scout | Mine conversation history for patterns worth preserving |
/skill kg-extract | Map codebase architecture into the knowledge graph |
System archives lowest-scored nodes when graph exceeds token limit. Score = 0.25recency + 0.50connectedness + 0.25*richness (weighted sum of percentiles). Nodes protected for 3 days after update. Archived nodes remain on disk; edges stay visible as memory traces.