synaptiai/agent-capability-standard/digital-twin-bootstrap

Name: digital-twin-bootstrap
Author: synaptiai

Claude Code and agent skill by synaptiai/agent-capability-standard

Trust tier 3

community

Source check pending

Checking that the SKILL.md file is still in the repository.

Initialize a digital twin from scratch, building a grounded world model with state, dynamics, uncertainty and provenance, then run a first sync loop. Use when creating digital twins, constructing system representations, building simulation foundations, or establishing a baseline world state.

Install

npx vskill@latest install synaptiai/agent-capability-standard/digital-twin-bootstrap

SKILL.md

Intent

Run the composed workflow digital_twin_bootstrap (reference/workflow_catalog.yaml) to initialize a digital twin from scratch and then run a first sync loop. When no world state is passed in, it invokes world_model_build to construct a grounded baseline and then digital_twin_sync_loop from that baseline; when a world state is passed in, it skips the build and syncs from it.

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:

World model built (or the given world state used as the baseline)
First sync completed
Audit produced
Every claim in the model is grounded with evidence anchors

Compatible schemas:

reference/world_state_schema.yaml
reference/event_schema.yaml
reference/workflow_catalog.yaml

Inputs

Parameter	Required	Type	Description
`user_input`	No	object	`domain` (the system to model), passed to both invoked workflows; `world_model_build` binds it. `constraints` is accepted but no step binds it
`world_state_out`	No	object	An existing world state (`reference/world_state_schema.yaml#/world_state`). When given, the build is skipped and the first sync starts from it

Procedure

0) Create checkpoint marker if mutation might occur:

Create .claude/checkpoint.ok after confirming rollback strategy (the sync loop mutates the twin)

1) Invoke `/invoke` with workflow world_model_build and input user_input, if world_state_out is null, and store output as world_model_out

Build the initial world model baseline. The invoked workflow's stores land in world_model_out.result:

retrieve_out → observe_out → detect_out (domain: entity) → classify_out → integrate_out → state_out → transition_out → attribute_out (domain: causation) → measure_out (domain: uncertainty) → audit_out → ground_out → simulate_out → explain_out

2) Invoke `/invoke` with workflow digital_twin_sync_loop, if world_state_out is null, and store output as sync_out

Input: user_input, and the baseline world state (world_model_out.result.state_out.state) as previous_world_state
Run the initial sync loop using the baseline just built

3) Invoke `/invoke` with workflow digital_twin_sync_loop, if world_state_out is not null, and store output as sync_existing_out

Input: user_input, and world_state_out as previous_world_state: a world state, the same shape step 2 passes (the state step's output wraps the world state in its state field)
Run the initial sync loop from the world state passed in

Every step has skip_if_false: true, so exactly one branch runs: steps 1-2, or step 3.

Output Contract

Return a structured object:

yaml

workflow_id: string  # Unique bootstrap execution ID
user_input: object  # The input as given
status: completed | partial | failed | rolled_back
steps:
  world_model_out:  # null when world_state_out was given and the step was skipped
    result:  # world_model_build's stores, keyed by store_as
      state_out:
        state: object  # The baseline world state
        entities: array[object]
        relationships: array[object]
        evidence_anchors: array[string]
      transition_out:
        rules: array[object]
        evidence_anchors: array[string]
      attribute_out:
        causes: array[object]
        strengths: object  # Causal strength per cause
        evidence_anchors: array[string]
      measure_out:
        value: number
        uncertainty: object  # lower, upper
        evidence_anchors: array[string]
      ground_out:
        grounded: boolean
        gaps: array[string]
        evidence_anchors: array[string]
      simulate_out:
        outcomes: array[object]
        evidence_anchors: array[string]
      explain_out:
        explanation: string
        evidence_anchors: array[string]
    steps_executed: array[string]  # store_as names of the steps that ran, in step order
    evidence_anchors: array[string]
  sync_out:  # null when world_state_out was given and the step was skipped
    result:  # digital_twin_sync_loop's stores, keyed by store_as
      state_out:
        state: object  # The synced twin snapshot
        evidence_anchors: array[string]
      verify_out:
        passed: boolean
        evidence_anchors: array[string]
      audit_out:
        audit_id: string
        evidence_anchors: array[string]
      explain_out:
        explanation: string
        evidence_anchors: array[string]
    steps_executed: array[string]  # store_as names of the steps that ran, in step order
    evidence_anchors: array[string]
  sync_existing_out:  # null when the baseline was built (steps 1-2 ran)
    result: object  # digital_twin_sync_loop's stores, as in sync_out
    steps_executed: array[string]  # store_as names of the steps that ran, in step order
    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 bootstrap
`status`	enum	Final workflow status
`steps`	object	Each step's output, keyed by its `store_as`; a skipped step is null
`steps.*.result`	object	The invoked workflow's stores, keyed by their `store_as`
`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: Bootstrap a Supply Chain Twin

Input:

yaml

user_input:
  domain: "electronics supply chain, Asia-Pacific"
world_state_out: null

Output:

yaml

workflow_id: "bootstrap_20240115_100000_supplychain"
user_input:
  domain: "electronics supply chain, Asia-Pacific"
status: completed
steps:
  world_model_out:
    result:
      state_out:
        state:
          timestamp: "2024-01-15T10:00:00Z"
          entities: "[1247 entities - see state/supply_chain_world.yaml]"
        entities:
          - id: "supplier-taiwan-001"
            type: "supplier"
            location: "Hsinchu, Taiwan"
            lead_time_days: 45
          - id: "mfg-shenzhen-005"
            type: "manufacturer"
            location: "Shenzhen, China"
        relationships:
          - subject: "supplier-taiwan-001"
            predicate: "supplies_to"
            object: "mfg-shenzhen-005"
        evidence_anchors:
          - "tool:database:supply-chain-db/entities"
          - "tool:api:logistics.api/shipments"
      transition_out:
        rules:
          - trigger: "supplier shutdown"
            effect: "downstream inventory drawdown"
        evidence_anchors:
          - "tool:database:supply-chain-db/orders"
      attribute_out:
        causes:
          - id: single_source_taiwan
            cause: "Single-source dependency on Taiwan for 45% of semiconductor supply"
        strengths:
          single_source_taiwan: 0.8
        evidence_anchors:
          - "tool:database:supply-chain-db/suppliers"
      measure_out:
        value: 0.82  # overall confidence in the model
        uncertainty: {lower: 0.75, upper: 0.88}
        evidence_anchors:
          - "tool:api:logistics.api/shipments"
      ground_out:
        grounded: true
        gaps:
          - "Supplier capacity utilization (estimated from public data)"
          - "Shipping delays (historical average, not real-time)"
          - "Competitor orders affecting supplier allocation"
        evidence_anchors:
          - "file:state/supply_chain_world.yaml"
      simulate_out:
        outcomes:
          - scenario: "Taiwan supplier shutdown"
            outcome: "67% production halt within 2 weeks"
          - scenario: "Shipping route disruption"
            outcome: "12-day average delay added"
        evidence_anchors:
          - "file:state/supply_chain_world.yaml"
      explain_out:
        explanation: "Baseline covers 156 suppliers and 23 manufacturers; the largest risk is semiconductor source concentration"
        evidence_anchors:
          - "file:state/supply_chain_world.yaml"
    steps_executed: [retrieve_out, observe_out, detect_out, classify_out, integrate_out, state_out, transition_out, attribute_out, measure_out, audit_out, ground_out, simulate_out, explain_out]
    evidence_anchors:
      - "file:state/supply_chain_world.yaml"
  sync_out:
    result:
      state_out:
        state:
          timestamp: "2024-01-15T10:20:00Z"
          entities: "[1251 entities - 4 new shipments]"
        evidence_anchors:
          - "tool:api:logistics.api/shipments"
      verify_out:
        passed: true
        evidence_anchors:
          - "tool:bash:run_verification_suite"
      audit_out:
        audit_id: "audit_20240115_102000"
        evidence_anchors:
          - "file:.claude/audit/records.jsonl"
      explain_out:
        explanation: "First sync ingested 4 shipments; lead-time drift on supplier-taiwan-001 was planned for, passed the policy check and was applied after a checkpoint; verification passed, so rollback was skipped"
        evidence_anchors:
          - "file:.claude/audit/records.jsonl"
    steps_executed: [receive_out, search_out, transform_out, integrate_out, detect_entity_out, classify_out, state_out, compare_state_out, transition_out, detect_anomaly_out, measure_risk_out, predict_out, plan_out, constrain_out, checkpoint_out, mutate_out, execute_out, verify_out, audit_out, explain_out]  # rollback_out absent: skipped because verify_out.passed was true
    evidence_anchors:
      - "file:.claude/audit/records.jsonl"
  sync_existing_out: null  # skipped: no world state was passed in
confidence: 0.82
evidence_anchors:
  - "tool:database:supply-chain-db"
  - "tool:api:logistics.api"
  - "file:state/supply_chain_world.yaml"
  - "file:.claude/audit/records.jsonl"
assumptions:
  - "Database reflects current operational state"
  - "API provides accurate shipment tracking"
  - "Public capacity data is within 20% of actual"

Evidence pattern: Multi-source data integration, entity resolution across databases, causal analysis from transaction patterns, uncertainty from data freshness and coverage, then a first verified and audited sync.

Verification

[ ] One Branch Ran: Either world_model_out and sync_out are set, or sync_existing_out is
[ ] Entity Coverage: All entities in scope identified (detect_out → classify_out → integrate_out)
[ ] State Validity: The baseline world state (state_out.state) conforms to the world state schema
[ ] Dynamics Defined: transition_out.rules and attribute_out.causes documented
[ ] Uncertainty Quantified: measure_out.uncertainty present
[ ] Grounded: ground_out.grounded is true, gaps listed
[ ] Simulation Run: simulate_out.outcomes has at least one scenario
[ ] First Sync Verified: The sync's verify_out.passed is true, or its rollback ran
[ ] Audit Trail: The sync's audit_out was recorded

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

Safety Constraints

mutation: true (the first sync may mutate the twin)
requires_checkpoint: true
requires_approval: true (for mutations in the sync loop)
risk: high

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 the first sync
Rate-limit API calls to respect source limits

Composition Patterns

This is the start of the file. Read the full SKILL.md on GitHub.

Security

Security scanPassed, 95 of 100Pattern checks on the skill's files.
Model reviewPassed

See the full security report

Quality evaluation

Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

See evaluation results

Source

SKILL.md in the repositoryCheckingskills/digital-twin-bootstrap/SKILL.md

Popularity

7-day trend score: 0

Not refreshed yet.

Works with

Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.

Claude Code
Cursor
GitHub Copilot
Windsurf
Codex
Gemini CLI
Cline
Amp
Roo Code
Goose
Continue
Kilo Code
Junie
Trae
OpenHands
Qwen Code
AI Agent
Tabnine Chat
Sourcegraph Cody
Amazon Q
Firebase Genkit
Sweep
Devin
Phind
Mentat
Claude Desktop
Aider
GPT Pilot
Plandex
bolt.new
v0
Replit Agent
Anse
TypingMind
BetterChatGPT
LibreChat
Warp AI
Pieces
Fig AI

Embed badge

[![vskill VERIFIED](https://verified-skill.com/api/v1/skills/synaptiai/agent-capability-standard/digital-twin-bootstrap/badge)](https://verified-skill.com/skills/synaptiai/agent-capability-standard/digital-twin-bootstrap)

Something wrong with this skill or its page? Tell us.