Sprint Concept Graph Analysis
Overview
The recent sprint added 171,506 new concept relations to the Ma Core knowledge graph, demonstrating the rich interconnectedness of the sprint documentation and workflow artifacts.
Key Statistics
- Relations before sprint: 886,885
- Relations after sprint: 1,058,391
- New relations added: 171,506
- Files processed: 2,483
- Concept mentions: 33,576
Primary Concept Hubs
1. Parallel Agent Orchestration Hub
The parallel-agent-orchestration concept emerged as a central hub, connecting 20 major concepts:
This hub connects methodology concepts (agentic-slc, structured-workflows), issue tracking (SRCH-004, MEM-009, GRPH-009), and quality assurance (red-team, quality-gates).
2. RTM (Requirements Traceability Matrix) Network
The RTM concept spawned an entire family of related concepts:
3. Issue-Specific Networks
SRCH-004 (minScore Parameter)
Connected to implementation and quality concepts:
MEM-009 (Path Security)
Linked to technical implementation details:
4. Agentic-SLC Ecosystem
The agentic-slc concept connected to the broader agent ecosystem:
Analysis
Concept Density
The sprint retrospective alone contained 14 key concepts:
- rtm, agentic-slc, srch-004, mem-009, grph-009
- structured-workflows, embedded-thinking, atomic-rtm
- parallel-agent-orchestration, quality-gates, red-team-verification
- agent-orchestration, sprint-planning, red-team
With each concept potentially connecting to every other concept in the same file, this creates up to 14×13 = 182 relations per file.
Graph Growth Pattern
The exponential growth (171K relations) demonstrates:
- Dense Documentation: Sprint artifacts heavily cross-reference concepts
- Methodology Integration: Workflow concepts link to implementation details
- Issue Tracking: Each issue connects to multiple implementation and testing concepts
- Quality Assurance: Red-team and quality-gate concepts permeate the graph
Key Insights
- Parallel Agent Orchestration emerged as the most connected concept, serving as a bridge between methodology and implementation
- RTM spawned an entire concept family, showing the importance of requirements traceability
- Issue concepts (SRCH-004, MEM-009) created rich networks linking problems to solutions
- Agentic-SLC connected to the broader agent ecosystem, demonstrating tool integration
Hybrid RRF Search Results for Top Concepts
1. Parallel Agent Orchestration
Query: parallel-agent-orchestration
Top 5 Results (Hybrid RRF scoring):
| Document | Fused Score | Text Score | Semantic Score | Type |
|---|---|---|---|---|
| Sequential Thinking Session session-1758167550136 | 0.016 | 0.000 | 1.000 | Thinking Session |
| Sequential Thinking Session session-1758314975525 | 0.016 | 0.000 | 0.823 | Thinking Session |
| Sequential Thinking Session session-1758427730103 | 0.016 | 0.000 | 0.654 | Thinking Session |
| Chat Session: swe-fast | 0.016 | 0.000 | 0.551 | Chat Session |
| ExtendedMind Diagnostics Report | 0.015 | 0.000 | 0.467 | Technical Doc |
Note: Pure semantic matches indicate the concept is contextually related but not explicitly mentioned in text.
2. RTM (Requirements Traceability Matrix)
Query: RTM requirements traceability matrix
Top 5 Results (Hybrid RRF scoring):
| Document | Fused Score | Text Score | Semantic Score | Type |
|---|---|---|---|---|
| ExtendedMind Diagnostics Report | 0.016 | 0.000 | 1.000 | Technical Doc |
| VS-004 and VS-008 RTM Test Results | 0.016 | 0.120 | 0.000 | Test Report |
| GPT-5 Optimization Principles | 0.016 | 0.000 | 0.768 | Guidelines |
| Workflow Dispatch Meta-Cognitive | 0.016 | 0.070 | 0.000 | Architecture |
| Workflow Session workflow-1756562251307 | 0.016 | 0.000 | 0.726 | Workflow |
Text matches show explicit RTM mentions; semantic matches show related concepts.
3. Agentic-SLC Workflow
Query: agentic-slc workflow
Top 5 Results (Hybrid RRF scoring):
| Document | Fused Score | Text Score | Semantic Score | Type |
|---|---|---|---|---|
| Chat Session: extended-gpt5-swe | 0.016 | 0.000 | 1.000 | Chat Session |
| Discovery Integration Path | 0.016 | 0.630 | 0.000 | Technical Doc |
| Unicode Test 🌟 | 0.016 | 0.000 | 0.882 | Test Doc |
| Workflow Dispatch Meta-Cognitive | 0.016 | 0.490 | 0.000 | Architecture |
| ExtendedMind Diagnostics Report | 0.016 | 0.000 | 0.851 | Technical Doc |
High text scores (0.630, 0.490) indicate direct workflow mentions.
4. SRCH-004 (minScore Search Filter)
Query: SRCH-004 minScore search filter
Top 5 Results (Hybrid RRF scoring):
| Document | Fused Score | Text Score | Semantic Score | Type |
|---|---|---|---|---|
| AnnotatedMessageTool.cs | 0.016 | 0.000 | 1.000 | Source Code |
| Workflow Dispatch Meta-Cognitive | 0.016 | 0.220 | 0.000 | Architecture |
| Unicode Test 🌟 | 0.016 | 0.000 | 0.921 | Test Doc |
| Discovery Integration Path | 0.016 | 0.180 | 0.000 | Technical Doc |
| ExtendedMind Diagnostics Report | 0.016 | 0.000 | 0.719 | Technical Doc |
Text matches (0.220, 0.180) show documents discussing search/filter functionality.
5. MEM-009 (Path Security Validation)
Query: MEM-009 path security validation
Top 5 Results (Hybrid RRF scoring):
| Document | Fused Score | Text Score | Semantic Score | Type |
|---|---|---|---|---|
| Nested Test | 0.016 | 0.000 | 1.000 | Test Doc |
| ManBearPig Integration Analysis | 0.016 | 0.160 | 0.000 | Architecture |
| ../../../etc/passwd | 0.016 | 0.000 | 0.966 | Security Test |
| 60-Day Implementation Timeline | 0.016 | 0.160 | 0.000 | Planning |
| Chat Session: swe | 0.016 | 0.000 | 0.766 | Chat Session |
High semantic scores for security tests (0.966) show related path validation concerns.
RRF Scoring Analysis
Reciprocal Rank Fusion (RRF) combines text and semantic search results:
- Fused Score: Combined ranking from both search methods (max ~0.016)
- Text Score: BM25 full-text search relevance (0.000-1.000)
- Semantic Score: Vector similarity using embeddings (0.000-1.000)
Key Patterns:
- Pure Semantic Matches (Text: 0.000): Documents conceptually related but lacking exact terms
- Pure Text Matches (Semantic: 0.000): Documents with exact keywords but different contexts
- Hybrid Matches: Documents with both keyword and semantic relevance (strongest results)
The RRF approach ensures both exact matches and conceptually related documents surface in search results, providing comprehensive knowledge retrieval across the 2,483 files in the system.
Conclusion
The 171K new relations reflect not just documentation volume, but the deeply interconnected nature of the sprint's work. Each concept represents a node in a knowledge network that captures:
- Implementation decisions
- Quality processes
- Agent coordination patterns
- Requirements traceability
- Security considerations
This dense graph, combined with hybrid RRF search capabilities, enables powerful knowledge retrieval and relationship discovery, making the sprint's learnings permanently accessible through the Ma Core knowledge system.