world-model-workflow

name: world-model-workflow description: Build a rigorous world model with state, dynamics, uncertainty, and provenance. Use when creating digital twins, constructing system representations, building simulation foundations, or establishing baseline world state. argument-hint: "[goal] [scope] [constraints]" disable-model-invocation: false user-invocable: true allowed-tools: Read, Grep, Bash, Edit, Git, Web context: fork agent: general-purpose

Intent

Run the composed workflow world-model-workflow using atomic capability skills to construct a comprehensive, grounded representation of a system or domain.

A world model captures:

• State: Current entity states and attributes
• Dynamics: How the system evolves over time
• Uncertainty: Confidence bounds and unknowns
• Provenance: Source and lineage of all facts

Success criteria:

• Complete entity inventory with identity resolution
• State representation follows canonical schema
• Causal relationships and dynamics modeled
• Uncertainty quantified for all assertions
• Full provenance chain for every fact
• Simulation capability established

Compatible schemas:

• reference/world_state_schema.yaml
• reference/event_schema.yaml

Inputs

Procedure

1. Create checkpoint marker if mutation might occur:

   • Create .claude/checkpoint.ok after confirming rollback strategy

2. Invoke /retrieve and store output as retrieve_out

   • Gather raw data from configured sources

3. Invoke /inspect and store output as inspect_out

   • Examine retrieved data for structure and quality

4. Invoke /identity-resolution and store output as identity-resolution_out

   • Resolve entity references and establish canonical IDs

5. Invoke /world-state and store output as world-state_out

   • Construct canonical state representation

6. Invoke /state-transition and store output as state-transition_out

   • Define rules for state evolution

7. Invoke /causal-model and store output as causal-model_out

   • Map cause-effect relationships

8. Invoke /uncertainty-model and store output as uncertainty-model_out

   • Quantify confidence and unknowns

9. Invoke /provenance and store output as provenance_out

   • Document source and lineage of all facts

10. Invoke /grounding and store output as grounding_out

    • Attach evidence anchors to assertions

11. Invoke /simulation and store output as simulation_out

    • Validate model through simulation runs

12. Invoke /summarize and store output as summarize_out

    • Generate human-readable model summary

Output Contract

Return a structured object:

workflow_id: string  # Unique model construction ID
goal: string  # Modeling objective
status: completed | partial | failed
world_model:
  version: string
  created_at: string  # ISO timestamp
  schema_version: string
  entities:
    count: integer
    by_type: object  # type -> count
    sample: array[object]  # representative entities
  relationships:
    count: integer
    types: array[string]
    sample: array[object]
  evidence_anchors: array[string]
state:
  snapshot: object  # Canonical world state
  hash: string  # Integrity hash
  timestamp: string
  evidence_anchors: array[string]
dynamics:
  transition_rules: integer
  causal_links: integer
  temporal_scope: string  # e.g., "real-time", "daily", "event-driven"
  evidence_anchors: array[string]
uncertainty:
  overall_confidence: number  # 0.0-1.0
  high_uncertainty_areas: array[string]
  unknown_factors: array[string]
  evidence_anchors: array[string]
provenance:
  sources: array[string]
  lineage_depth: integer
  coverage: number  # 0.0-1.0 (% of facts with provenance)
  evidence_anchors: array[string]
simulation:
  validated: boolean
  scenarios_tested: integer
  anomalies_found: array[string]
  evidence_anchors: array[string]
summary:
  description: string
  key_insights: array[string]
  recommended_actions: array[string]
  evidence_anchors: array[string]
confidence: number  # 0.0-1.0
evidence_anchors: array[string]
assumptions: array[string]

Field Definitions

Examples

Example 1: Build Supply Chain World Model

Input:

goal: "Model electronics supply chain for disruption risk analysis"
scope:
  - "suppliers"
  - "manufacturers"
  - "logistics"
  - "inventory"
constraints:
  time_horizon: "6 months"
  geographic_scope: "Asia-Pacific"
  confidence_threshold: 0.7
sources:
  - type: database
    connection: "postgres://supply-chain-db"
  - type: api
    endpoint: "https://logistics.api/shipments"

Output:

workflow_id: "world_20240115_100000_supplychain"
goal: "Model electronics supply chain for disruption risk analysis"
status: completed
world_model:
  version: "v1.0.0"
  created_at: "2024-01-15T10:00:00Z"
  schema_version: "world_state_schema_v2"
  entities:
    count: 1247
    by_type:
      supplier: 156
      manufacturer: 23
      warehouse: 45
      distribution_center: 12
      product: 892
      shipment: 119
    sample:
      - id: "supplier-taiwan-001"
        type: "supplier"
        name: "Taiwan Semiconductor Co"
        location: "Hsinchu, Taiwan"
        capacity: 50000
        lead_time_days: 45
      - id: "mfg-shenzhen-005"
        type: "manufacturer"
        name: "Shenzhen Electronics Assembly"
        location: "Shenzhen, China"
        capacity: 100000
  relationships:
    count: 3456
    types:
      - "supplies_to"
      - "located_in"
      - "transports_via"
      - "stores_at"
      - "depends_on"
    sample:
      - subject: "supplier-taiwan-001"
        predicate: "supplies_to"
        object: "mfg-shenzhen-005"
        attributes:
          volume: 25000
          frequency: "weekly"
  evidence_anchors:
    - "tool:database:supply-chain-db/entities"
    - "tool:api:logistics.api/shipments"
state:
  snapshot:
    timestamp: "2024-01-15T10:00:00Z"
    entities: "[1247 entities - see world_state.yaml]"
    relationships: "[3456 relationships - see world_state.yaml]"
  hash: "sha256:def456abc789..."
  timestamp: "2024-01-15T10:00:00Z"
  evidence_anchors:
    - "file:state/supply_chain_world.yaml"
dynamics:
  transition_rules: 34
  causal_links: 89
  temporal_scope: "daily"
  evidence_anchors:
    - "tool:state-transition:rule_extraction"
    - "tool:causal-model:dependency_graph"
uncertainty:
  overall_confidence: 0.82
  high_uncertainty_areas:
    - "Supplier capacity utilization (estimated from public data)"
    - "Shipping delays (historical average, not real-time)"
  unknown_factors:
    - "Competitor orders affecting supplier allocation"
    - "Regulatory changes in transit countries"
  evidence_anchors:
    - "tool:uncertainty-model:confidence_analysis"
provenance:
  sources:
    - "postgres://supply-chain-db (primary)"
    - "https://logistics.api (secondary)"
    - "public filings (supplementary)"
  lineage_depth: 3
  coverage: 0.94
  evidence_anchors:
    - "tool:provenance:lineage_trace"
simulation:
  validated: true
  scenarios_tested: 5
  anomalies_found:
    - "Taiwan supplier shutdown causes 67% production halt within 2 weeks"
    - "Shipping route disruption adds 12-day average delay"
  evidence_anchors:
    - "tool:simulation:scenario_results"
summary:
  description: "Electronics supply chain model covering 156 suppliers, 23 manufacturers, and supporting logistics infrastructure in Asia-Pacific region"
  key_insights:
    - "Single-source dependency on Taiwan for 45% of semiconductor supply"
    - "Shenzhen manufacturing hub handles 60% of assembly volume"
    - "Average supply chain depth of 3 tiers with limited visibility beyond tier 1"
  recommended_actions:
    - "Diversify semiconductor sourcing to reduce Taiwan concentration risk"
    - "Establish buffer inventory for critical components"
    - "Develop secondary logistics routes for key shipping lanes"
  evidence_anchors:
    - "tool:summarize:executive_summary"
confidence: 0.82
evidence_anchors:
  - "tool:database:supply-chain-db"
  - "tool:api:logistics.api"
  - "tool:simulation:scenario_results"
  - "file:state/supply_chain_world.yaml"
assumptions:
  - "Database reflects current operational state"
  - "API provides accurate shipment tracking"
  - "Public capacity data is within 20% of actual"
  - "Lead times based on historical 90-day average"

Evidence pattern: Multi-source data integration, entity resolution across databases, causal analysis from transaction patterns, uncertainty from data freshness and coverage.

Verification

• Entity Coverage: All entities in scope identified with canonical IDs
• Relationship Completeness: Key relationships mapped with evidence
• State Validity: World state conforms to schema
• Dynamics Defined: Transition rules and causal links documented
• Uncertainty Quantified: Confidence scores for all major assertions
• Provenance Complete: Source documented for >90% of facts
• Simulation Validated: At least 1 scenario successfully executed

Verification tools: Read (for state files), Bash (for simulation), Web (for API validation)

Safety Constraints

• mutation: false
• requires_checkpoint: false
• requires_approval: false
• risk: medium

Capability-specific rules:

• Do not modify source data during modeling
• Flag entities with confidence < threshold
• Document all assumptions explicitly
• Preserve raw data alongside derived state
• Validate schema conformance before completion
• Rate-limit API calls to respect source limits

Composition Patterns

Commonly follows:

• retrieve - After gathering raw data
• receive - After ingesting real-time signals
• inspect - After initial data quality assessment

Commonly precedes:

• digital-twin-sync-workflow - World model is prerequisite for sync
• simulate - To run what-if scenarios
• forecast-risk - To predict future states
• summarize - To generate executive reports

Anti-patterns:

• Never skip identity resolution before state construction
• Never omit uncertainty modeling for production use
• Never finalize without provenance documentation
• Never deploy model without simulation validation

Workflow references:

• See reference/workflow_catalog.yaml#world-model-workflow for step definitions
• See reference/world_state_schema.yaml for canonical state format