# Graph Query Logic Specification (N-04)

## Purpose
Define how the recommendation engine uses graph intelligence. The graph is the reasoning layer that turns theoretical logic into a working intelligence system.

## SECTION 1: GRAPH OBJECTIVES
The graph must support:
- Dependency discovery
- Prerequisite discovery
- Complementary skills
- Substitute skills
- Architecture bundles
- Learning paths
- Risk detection

## SECTION 2: NODE TYPES
- **Skill**: Atomic unit of ability.
- **Capability**: High-level functional outcome.
- **Framework**: Structural methodology or toolset.
- **Path**: Sequential learning or execution track.
- **Benchmark**: Success metric or performance standard.
- **Blueprint**: Pre-configured architecture pattern.

## SECTION 3: EDGE TYPES
- **REQUIRES**: Hard dependency.
- **USES**: Optional but common dependency.
- **SUPPORTS**: Indirect benefit or enablement.
- **EXTENDS**: Specialized version of another node.
- **ALTERNATIVE_TO**: Substitute relationship.
- **PART_OF**: Compositional relationship.
- **RECOMMENDED_WITH**: Complementary pairing.
- **VALIDATED_BY**: Quality assurance relationship.
- **LEARN_BEFORE**: Prerequisite ordering.

## SECTION 4: QUERY OPERATIONS
1. **Expand Dependencies**: Recursive traversal of hard and soft requirements.
2. **Find Alternatives**: Identification of substitutes for missing or rejected nodes.
3. **Find Complements**: Discovery of nodes that enhance the current selection.
4. **Find Bundles**: Detection of logical groupings for architecture.
5. **Generate Learning Paths**: Optimal traversal based on prerequisites.
6. **Detect Gaps**: Identification of missing nodes in a proposed blueprint.
7. **Detect Risks**: Identification of conflicting or low-confidence paths.

## SECTION 5: ARCHITECT QUERIES
**Example Query:**
- **Input**: Goal = Coding Agent
- **Output**:
  - Required Skills: (e.g., Python, LLM Orchestration)
  - Optional Skills: (e.g., Vector DBs)
  - Dependencies: (e.g., API keys, environment setup)
  - Alternatives: (e.g., LangChain vs. LlamaIndex)
  - Learning Path: (e.g., Python -> Transformers -> Agents)
  - Risk Warnings: (e.g., API rate limits, hallucination risks)

## SECTION 6: SCORING
- **Graph Centrality**: Importance of a node within the network.
- **Relationship Confidence**: Weighted probability of edge validity.
- **Coverage Score**: Percentage of goals met by selected nodes.
- **Bundle Score**: Cohesion of a selected group of nodes.
- **Risk Score**: Combined probability of failure modes.

## SECTION 7: FAILURE MODES
- **Missing Nodes**: Gaps in the knowledge graph.
- **Weak Relationships**: Low-confidence edges causing fuzzy reasoning.
- **Circular Dependencies**: Traversal loops in prerequisites.
- **Conflicting Frameworks**: Mutually exclusive node selections.
- **Low-Confidence Recommendations**: Insufficient data for a strong path.

## SECTION 8: OUTPUT CONTRACTS
- **Graph Expansion Output**: Serialized adjacency list of related nodes.
- **Dependency Output**: Sorted list of requirements with priority levels.
- **Bundle Output**: Logical groupings for architecture generation.
- **Alternative Output**: Ranked list of substitutes with trade-offs.
- **Risk Output**: Heatmap of potential failures and mitigations.
