Nav Graph

Navigator Knowledge Graph Skill

Query and manage the unified project knowledge graph. Surfaces relevant knowledge from tasks, SOPs, system docs, and experiential memories.

Why This Exists

Navigator v6.0.0 introduces the Project Knowledge Graph:

• Unified search: Query across all knowledge types with one interface
• Experiential memory: Patterns, pitfalls, decisions, learnings persist
• Context-aware retrieval: Load only relevant knowledge (~1-2k tokens)
• Relationship traversal: Find related concepts and documents

When to Invoke

Query triggers:

• "What do we know about X?"
• "Show everything related to X"
• "Any pitfalls for X?"
• "What decisions about X?"
• "Find all knowledge about X"

Memory capture triggers:

• "Remember this pattern: ..."
• "Remember this pitfall: ..."
• "Remember we decided: ..."
• "Remember this learning: ..."

Graph management triggers:

• "Initialize knowledge graph"
• "Rebuild knowledge graph"
• "Show graph stats"

Graph Location

.agent/knowledge/graph.json (~1-2k tokens, loaded on query)

Execution Steps

Step 1: Determine Action

QUERY (searching knowledge):

User: "What do we know about authentication?"
→ Query graph by concept

CAPTURE (storing memory):

User: "Remember: auth changes often break session tests"
→ Create new memory node

INIT (building graph):

User: "Initialize knowledge graph"
→ Build graph from existing docs

STATS (viewing graph):

User: "Show graph stats"
→ Display graph statistics

Step 2: Load or Initialize Graph

Check if graph exists:

if [ -f ".agent/knowledge/graph.json" ]; then
  echo "Graph exists"
else
  echo "No graph found, will initialize"
fi

Initialize if not exists:

python skills/nav-graph/functions/graph_builder.py \
  --agent-dir .agent \
  --output .agent/knowledge/graph.json

Step 3A: Query Knowledge (If QUERY Action)

Extract concept from user input:

User: "What do we know about testing?"
→ Concept: testing

User: "Any pitfalls for auth?"
→ Concept: auth (normalized to authentication)

Run query:

python skills/nav-graph/functions/graph_manager.py \
  --action query \
  --concept "testing" \
  --graph-path .agent/knowledge/graph.json

Display results:

Knowledge Graph: "testing"

TASKS (3)
  - TASK-30: Task Verification Enhancement (completed)
  - TASK-17: Visual Regression Integration (completed)
  - TASK-11: Project Skills Generation (completed)

MEMORIES (2)
  - PITFALL: "Auth changes break session tests" (90%)
  - PATTERN: "Always run unit tests before integration" (85%)

SOPs (1)
  - visual-regression-setup

FILES (5)
  - skills/backend-test/*
  - skills/frontend-test/*

Load details: "Read TASK-30" or "Show testing memories"

Step 3B: Capture Memory (If CAPTURE Action)

Parse memory from user input:

User: "Remember this pitfall: auth changes often break session tests"
→ Type: pitfall
→ Summary: "auth changes often break session tests"
→ Concepts: [auth, testing]

User: "Remember we decided to use JWT over sessions for scaling"
→ Type: decision
→ Summary: "use JWT over sessions for scaling"
→ Concepts: [auth, architecture]

Determine memory type:

Create memory:

python skills/nav-graph/functions/graph_manager.py \
  --action add-memory \
  --memory-type pitfall \
  --summary "auth changes often break session tests" \
  --concepts "auth,testing" \
  --confidence 0.9 \
  --graph-path .agent/knowledge/graph.json

Optionally create detailed memory file:

# Pitfall: Auth Changes Break Session Tests

## Summary
Auth changes often break session tests due to...

## Context
Discovered during TASK-XX when...

## Recommended Approach
When modifying auth, always run...

## Related
- TASK-12: V3 Skills-Only
- SOP: autonomous-completion

Confirm capture:

Memory captured: mem-001

Type: Pitfall
Summary: "auth changes often break session tests"
Concepts: auth, testing
Confidence: 90%

This will be surfaced when working on auth or testing topics.

Step 3C: Initialize Graph (If INIT Action)

Build from existing docs:

python skills/nav-graph/functions/graph_builder.py \
  --agent-dir .agent \
  --output .agent/knowledge/graph.json

Display results:

Knowledge Graph Initialized

Scanned:
  - Tasks: 35
  - SOPs: 12
  - System docs: 3
  - Markers: 8

Extracted:
  - Concepts: 15
  - Relationships: 47

Graph saved to .agent/knowledge/graph.json

Query with: "What do we know about [topic]?"

Step 3D: Show Stats (If STATS Action)

Display graph statistics:

python skills/nav-graph/functions/graph_manager.py \
  --action stats \
  --graph-path .agent/knowledge/graph.json

Output:

Knowledge Graph Statistics
==========================
Total Nodes: 65
Total Edges: 47
Memories: 5
Last Updated: 2025-01-23T10:30:00Z

By Type:
  Tasks: 35
  SOPs: 12
  System: 3
  Markers: 8
  Concepts: 15
  Memories: 5

Step 4: Find Related (Optional)

If user asks for related items:

User: "What's related to TASK-29?"

Run traversal:

python skills/nav-graph/functions/graph_manager.py \
  --action related \
  --node-id "TASK-29" \
  --max-depth 2 \
  --graph-path .agent/knowledge/graph.json

Memory Types

Pattern

"We use X for Y in this project"

• Reusable approaches
• Project conventions
• Best practices

Pitfall

"Watch out for X when touching Y"

• Common mistakes
• Gotchas
• Failure modes

Decision

"We chose X over Y because Z"

• Architecture decisions
• Technology choices
• Trade-off rationale

Learning

"X usually means Y in this codebase"

• Project-specific knowledge
• Error interpretations
• Domain insights

Confidence System

Base confidence:

• Correction-based: 0.8
• Explicit capture: 0.9

Decay:

• 1% per week since last validation

Boost:

• +5% per use (max +25%)

Threshold:

• Below 0.3: Candidate for pruning
• Above 0.7: Reliable memory

Integration with Other Skills

nav-start (Session Start)

Loads graph stats on session start:

Knowledge graph: 65 nodes, 5 memories
Relevant: 2 memories for current context

nav-task (Task Creation)

Auto-extracts concepts from new tasks:

Creating TASK-35: Project Memory
Extracted concepts: knowledge, memory, graph
Added to graph.

nav-profile (Corrections)

Corrections auto-create memories via correction_to_memory.py:

# When correction detected in nav-profile:
python3 skills/nav-graph/functions/correction_to_memory.py \
  --action convert-one \
  --correction-json '{"pattern": "...", "context": "...", "confidence": "high"}'

# Output:
[Correction detected]
→ Type: pitfall (based on pattern analysis)
→ Concepts: [auth, testing] (auto-extracted)
→ Created memory: mem-002
→ Added to graph

Sync all corrections:

python3 skills/nav-graph/functions/correction_to_memory.py \
  --action sync \
  --profile-path .agent/.user-profile.json \
  --graph-path .agent/knowledge/graph.json

nav-marker (Context Markers)

Markers reference graph state:

## Graph State
- Memories surfaced: mem-001, mem-003
- Concepts active: auth, testing

Configuration

In .agent/.nav-config.json:

{
  "knowledge_graph": {
    "enabled": true,
    "auto_capture_corrections": true,
    "auto_capture_decisions": true,
    "auto_surface_relevant": true,
    "max_session_memories": 5,
    "confidence_decay_rate": 0.01,
    "staleness_threshold_days": 90,
    "git_tracked": true
  }
}

Graph Maintenance

Health Check

python3 skills/nav-graph/functions/graph_maintenance.py --action health

Output:

Knowledge Graph Health Check
========================================
Total Nodes: 94
Total Edges: 819
Memories: 2 (2 high confidence)
Health Score: 100/100

No issues detected!

Conflict Detection

Find memories that may contradict each other:

python3 skills/nav-graph/functions/graph_maintenance.py --action conflicts

Stale Memory Detection

Find memories not validated in 90+ days:

python3 skills/nav-graph/functions/graph_maintenance.py --action stale --stale-days 90

Low Confidence Pruning

Find and optionally remove low-confidence memories:

# Preview what would be removed
python3 skills/nav-graph/functions/graph_maintenance.py --action prune --threshold 0.3 --dry-run

# Actually remove (use with caution)
python3 skills/nav-graph/functions/graph_maintenance.py --action prune --threshold 0.3 --execute

Apply Decay

Reduce confidence of stale memories:

python3 skills/nav-graph/functions/graph_maintenance.py --action decay --decay-rate 0.01

Token Budget

Session overhead: ~1.3k tokens

Success Criteria

Graph skill succeeds when:

• Query returns relevant results across knowledge types
• Memories persist and are surfaced appropriately
• Concepts connect related items
• Confidence decay/boost works
• Graph stays under 2k tokens overhead

Best Practices

Good queries:

• "What do we know about auth?" (specific concept)
• "Any pitfalls for testing?" (scoped type)
• "Show everything related to TASK-29" (node traversal)

Good memory capture:

• "Remember: we use X for Y" (clear pattern)
• "Remember this pitfall: X breaks Y" (specific issue)
• "Remember we decided X because Y" (rationale included)

Avoid:

• Overly broad queries ("What do we know?")
• Storing code snippets in memories (use paths instead)
• Capturing obvious knowledge (focus on project-specific insights)

This skill transforms Navigator from stateless assistant to knowledge-aware team member