synaptiai/agent-capability-standard/attribute
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Establish cause-effect relationships between events or states. Use when analyzing root causes, mapping dependencies, tracing effects, or building causal models.
Install
npx vskill@latest install synaptiai/agent-capability-standard/attributeSKILL.md
Intent
Establish causal relationships between observed effects and potential causes. This capability supports root cause analysis, dependency mapping, and causal reasoning for planning and debugging.
Success criteria:
- Causes identified for observed effects
- Causal strength estimated for each relationship
- Causal mechanism explained
- Alternative causes considered and ruled out
Compatible schemas:
schemas/output_schema.yaml
Inputs
| Parameter | Required | Type | Description | |
|---|---|---|---|---|
effect | Yes | any | The observed effect to attribute | |
candidates | No | array | Potential causes to evaluate | |
context | No | object | Situational context for attribution |
Inputs not listed here are outside the capability's contract: a workflow step that binds them gets W604 (STANDARD v1.1 §5.5).
Procedure
1) Characterize the effect: Understand what needs to be explained
- Document the observed effect precisely
- Note timing and circumstances
- Identify what changed
2) Identify candidate causes: Generate list of potential causes
- Use provided candidates if available
- Generate additional candidates from context
- Consider proximate and distal causes
3) Evaluate causal strength: Assess each candidate
- Check temporal precedence (cause before effect)
- Verify mechanism plausibility
- Look for correlation evidence
- Consider counterfactual (would effect occur without cause?)
4) Trace causal chain: Map the path from cause to effect
- Identify intermediate steps
- Note amplifying or dampening factors
- Document the causal mechanism
5) Rule out alternatives: Eliminate unlikely causes
- Document why alternatives are less likely
- Note any confounding factors
- Flag when multiple causes may contribute
6) Quantify confidence: Assess attribution certainty
- Rate strength of causal evidence
- Note missing evidence
- Identify what would confirm/refute attribution
Output Contract
Return a structured object:
Field Definitions
| Field | Type | Description | |
|---|---|---|---|
causes | array | Identified causes with strength and mechanism | |
causes[].strength | number | Causal strength (0-1) | |
causes[].mechanism | string | Explanation of causal pathway | |
causes[].type | string | Direct, contributing, or enabling cause | |
mechanism | string | Overall causal mechanism linking the primary cause to the effect | |
causal_chain | array | Steps from cause to effect | |
alternatives_ruled_out | array | Rejected alternatives with reasons | |
strengths | object | Causal strength per cause, for comparison | |
confidence | number | 0.0-1.0 confidence in the attribution | |
evidence_anchors | array | References to logs, code, or data supporting each cause | |
assumptions | array | Assumptions the attribution depends on |
Examples
Example 1: Attribute Performance Degradation
Input:
Output:
Example 2: Attribute Test Failure
Input:
Output:
Verification
- [ ] At least one cause identified with strength > 0.3
- [ ] Mechanism explains how cause produces effect
- [ ] Temporal precedence verified (cause before effect)
- [ ] Alternative causes considered
- [ ] Evidence supports attribution
Verification tools: Read, Grep (to verify evidence)
Safety Constraints
mutation: falserequires_checkpoint: falserequires_approval: falserisk: low
Capability-specific rules:
- Distinguish correlation from causation
- Consider multiple causes (multi-causality)
- Note when attribution is uncertain
- Do not assume causation without mechanism
Composition Patterns
Commonly follows:
detect- Detect anomalies then attribute causesobserve- Observations reveal effects to attributemeasure- Measurements quantify effects
Commonly precedes:
plan- Causal understanding informs remediationexplain- Attribution enables explanationcritique- Causal model enables critique
Anti-patterns:
- Never use attribute for prediction (use
predict) - Avoid attribution without sufficient evidence
Workflow references:
- See
reference/workflow_catalog.yaml#debug_code_changefor attribution in debugging - See
reference/workflow_catalog.yaml#world_model_buildfor causal modeling
Security
- Security scanPassed, 90 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingskills/attribute/SKILL.md
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