Claude-skill-registry world-model-workflow

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.

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/digital-twin-bootstrap" ~/.claude/skills/majiayu000-claude-skill-registry-world-model-workflow && rm -rf "$T"

manifest: skills/data/digital-twin-bootstrap/SKILL.md

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

Parameter	Required	Type	Description
`goal`	Yes	string	The modeling objective (e.g., "model supply chain for disruption analysis")
`scope`	Yes	string\|array	Domain, system, or entities to model
`constraints`	No	object	Limits (e.g., time horizon, resolution, confidence threshold)
`sources`	No	array	Data sources for world state extraction
`prior_model`	No	object	Existing model to extend or refine

Procedure

Create checkpoint marker if mutation might occur:
- Create
```
.claude/checkpoint.ok
```
  after confirming rollback strategy
Invoke
```
/retrieve
```
and store output as
```
retrieve_out
```
- Gather raw data from configured sources
Invoke
```
/inspect
```
and store output as
```
inspect_out
```
- Examine retrieved data for structure and quality
Invoke
```
/identity-resolution
```
and store output as
```
identity-resolution_out
```
- Resolve entity references and establish canonical IDs
Invoke
```
/world-state
```
and store output as
```
world-state_out
```
- Construct canonical state representation
Invoke
```
/state-transition
```
and store output as
```
state-transition_out
```
- Define rules for state evolution
Invoke
```
/causal-model
```
and store output as
```
causal-model_out
```
- Map cause-effect relationships
Invoke
```
/uncertainty-model
```
and store output as
```
uncertainty-model_out
```
- Quantify confidence and unknowns
Invoke
```
/provenance
```
and store output as
```
provenance_out
```
- Document source and lineage of all facts
Invoke
```
/grounding
```
and store output as
```
grounding_out
```
- Attach evidence anchors to assertions
Invoke
```
/simulation
```
and store output as
```
simulation_out
```
- Validate model through simulation runs
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

Field	Type	Description
`workflow_id`	string	Unique identifier for this model construction
`world_model`	object	Metadata about entities and relationships
`state`	object	Canonical world state snapshot with integrity hash
`dynamics`	object	Transition rules and causal structure
`uncertainty`	object	Confidence levels and unknown factors
`provenance`	object	Source tracking and lineage
`simulation`	object	Model validation results
`summary`	object	Human-readable insights
`confidence`	number	0.0-1.0 based on evidence completeness
`evidence_anchors`	array	All evidence references collected
`assumptions`	array	Explicit assumptions made during modeling

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