nav-graph

Query and manage a project knowledge graph of tasks, SOPs, and experiential memories.

232|12|Updated Oct 10, 2025
One-click install
npx skills add https://github.com/qf-studio/navigator --skill nav-graph-qf-studio
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: nav-graph
Source: https://github.com/qf-studio/navigator/tree/main/skills/nav-graph
Command: npx skills add https://github.com/qf-studio/navigator --skill nav-graph-qf-studio

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve? AI coding sessions lose hard-won project knowledge between conversations, forcing developers to re-explain patterns, pitfalls, and past decisions every time. This Skill persists that knowledge in a unified graph so it can be searched and recalled on demand. ## Core Features & Use Cases - Unified Knowledge Search: Query across tasks, SOPs, system docs, and memories with one command, returning only relevant context (~1-2k tokens). - Memory Capture: Store patterns, pitfalls, decisions, and learnings with confidence scores, concept tags, and optional TRIZ contradiction annotations. - Graph Maintenance: Run health checks, reconcile disk files against graph nodes, repair dangling edges, prune low-confidence memories, and resolve superseded knowledge. - Use Case: Before modifying authentication code, ask "what do we know about auth?" to surface the pitfall that auth changes break session tests, plus the decision record explaining why JWT was chosen over sessions. ## Quick Start Ask the assistant to initialize the knowledge graph and then show everything the project knows about authentication.

Frequently Asked Questions about nav-graph

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I query a project knowledge graph for past decisions?

Run graph_manager.py with the query action and a concept name, such as "authentication". It returns matching tasks, memories, SOPs, and files, and you can traverse related nodes with the related action and a node id like TASK-29.

How do I store coding pitfalls and patterns for future sessions?

Use the add-memory action with a memory type (pattern, pitfall, decision, or learning), a summary, concept tags, and a confidence score. The memory is written to a markdown file and indexed in graph.json so it surfaces in later sessions.

Can the knowledge graph sync with memory files on disk?

Yes. The reconcile action in graph_maintenance.py reports broken file links and unindexed memory files, and with --execute it registers orphaned files as graph nodes. Broken-link nodes are never auto-deleted; they are reported for manual review.

Why does my knowledge graph health check report dangling edges?

Dangling edges reference node ids that no longer exist, usually after deletions or partial rebuilds. Run graph_maintenance.py with the repair action to idempotently drop duplicate and dangling edges and normalize out-of-range confidence values.

What happens to outdated memories in the knowledge graph?

Memories are resolved rather than deleted: the node is marked resolved, its file moves to a resolved/ directory, and it is excluded from session-start recall. Low-confidence memories below 0.3 can also be pruned after a dry-run preview.