| license | Apache-2.0 |
| name | dag-cycle-analysis |
| description | Graph algorithms for detecting and resolving cycles in directed graphs and workflow definitions |
| category | Agent & Orchestration |
| tags | ["dag","cycle-detection","graph-theory","validation","algorithms"] |
Hierarchical Cycle Analysis
Skill ID: hierarchical-cycle-analysis
Domain: Complex systems, network science, information architecture
Description
This skill applies rigorous cycle-theoretic methods to understand the hidden organizational structure of hierarchical systems. It reveals how systems that appear "acyclic" (DAGs) actually contain rich cyclic structure that determines their information-processing capabilities.
Decision Points
Algorithm Selection Tree
IF analyzing system behavior/comparison
├── THEN decompose DAG = undirected graph + directional metadata
├── IF need functional differences between similar topologies
│ └── THEN use metadata metrics (height, stretch, balance) + topology
└── IF need structural simplification
└── THEN apply transitive reduction first
IF detecting cycles in DAG
├── IF raw cycle count needed
│ └── THEN use DFS on undirected substrate
├── IF functional classification needed
│ ├── THEN apply path-wedge contraction first
│ └── THEN classify: feedback/shortcut/diamond/mixer
└── IF comparing cycle organization
└── THEN compute antichain structure + metadata positioning
IF system shows unexpected behavior despite "good" topology
├── THEN check antichain structure (parallel vs sequential)
├── THEN measure cycle height/stretch/balance distribution
└── IF still unclear, THEN load metadata-localizes-topology framework
IF redesigning hierarchical process
├── IF need resilience THEN preserve/add diamonds (parallel alternatives)
├── IF need integration THEN check mixer positioning (multi-level convergence)
└── IF optimizing efficiency THEN identify shortcuts for potential removal
Stopping Criteria
- Stop iterating when TR converges (no more transitive edges to remove)
- Stop analysis when all cycles classified into four classes
- Stop decomposition when undirected substrate + metadata separated
- Escalate to domain expert if >50% of cycles are mixers (unusual integration pattern)
Failure Modes
Topology Blindness
Symptom: Two hierarchies have identical node/edge counts but behave differently
Diagnosis: Analyzing structure without considering ordering metadata
Detection Rule: If degree distributions match but information flow differs
Fix: Decompose into undirected graph + metadata, compute metadata-derived metrics
Acyclic Confusion
Symptom: Claiming "no cycles exist" in hierarchical system
Diagnosis: Confusing directional constraint-compatibility with topological absence
Detection Rule: If someone says "it's a DAG so no loops"
Fix: Extract undirected substrate to reveal hidden cyclic structure
Cycle Conflation
Symptom: Counting all cycles as equivalent structural features
Diagnosis: Missing functional differences between diamonds vs mixers
Detection Rule: If analysis treats 3-node triangle same as 6-node mixer
Fix: Apply path-wedge contraction, classify into four classes
Over-Reduction
Symptom: System performance degrades after applying transitive reduction
Diagnosis: Removing shortcuts that were functionally valuable despite being informationally redundant
Detection Rule: If TR improves "structural metrics" but worsens latency/reliability
Fix: Distinguish informational redundancy from functional optimization
Antichain Ignorance
Symptom: Cannot explain why parallel nodes behave differently
Diagnosis: Missing hierarchical coordinate system, treating all "same level" nodes as equivalent
Detection Rule: If surprised by different behaviors at "same hierarchical level"
Fix: Compute antichain decomposition, analyze cycle organization by level
Worked Examples
Example 1: Organizational Chart Analysis
Scenario: Two companies with identical reporting structures (30 nodes, 35 edges) but different decision-making speeds.
Step 1 - Decomposition:
Company A: Extract undirected substrate → 12 cycles found
Company B: Extract undirected substrate → 12 cycles found
Topology identical, so analyze metadata.
Step 2 - Classification:
Apply transitive reduction:
Company A: 8 diamonds, 2 mixers remain
Company B: 4 diamonds, 4 mixers remain
Step 3 - Metadata Analysis:
Company A diamonds: Height 2-4 (mid-hierarchy resilience)
Company A mixers: Height 6-7 (senior integration points)
Company B diamonds: Height 1-2 (low-level redundancy)
Company B mixers: Height 3-8 (integration at all levels)
Expert Insight: Company B's distributed mixers create more integration overhead but higher adaptability. Company A's concentrated senior mixers create bottlenecks but faster routine decisions.
Novice Miss: Would only count total cycles (12 each) and conclude structures identical.
Example 2: Software Architecture Comparison
Scenario: Microservice dependency graphs with similar complexity metrics but different deployment reliability.
Step 1 - Cycle Detection:
System X: 45 cycles in undirected substrate
System Y: 47 cycles in undirected substrate
Step 2 - TR + Classification:
System X after TR: 15 diamonds, 8 mixers
- Diamonds concentrated in data layer (height 1-3)
- Mixers in API gateway layer (height 5-6)
System Y after TR: 8 diamonds, 12 mixers
- Diamonds scattered across all layers
- Mixers concentrated in business logic (height 3-4)
Step 3 - Antichain Analysis:
System X: 6 major antichains, services well-layered
System Y: 3 major antichains, more cross-cutting concerns
Expert Diagnosis: System X has resilient data access (diamonds) but single points of integration failure. System Y has complex business logic integration creating brittleness.
Decision: System X better for read-heavy workloads, System Y better for complex transactional workflows.
Quality Gates
Analysis Complete When:
Ready for Implementation When:
NOT-FOR Boundaries
Do NOT use this skill for:
- Real-time dynamic graphs: For temporal networks, use
temporal-graph-analysis instead
- Undirected networks: For social networks without ordering, use
community-detection instead
- Weighted optimization: For cost/flow optimization, use
network-flow-algorithms instead
- Stochastic processes: For probabilistic workflows, use
markov-chain-analysis instead
Delegate when:
- Graph size >10K nodes: Use
distributed-graph-algorithms for scalability
- Real-time constraints: Use
streaming-dag-validation for online systems
- Complex temporal dynamics: Use
temporal-motif-analysis for time-varying hierarchies
- Domain-specific rules: Load domain frameworks (org-behavior, software-architecture, etc.)
Framework applies only when:
- Clear ordering constraint exists (time, causality, hierarchy, dependency)
- System can be meaningfully represented as DAG
- Cycles represent structural relationships, not just algorithmic artifacts
- Static analysis sufficient (system not rapidly evolving)
