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/attribute

SKILL.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

ParameterRequiredTypeDescription
effectYesanyThe observed effect to attribute
candidatesNoarrayPotential causes to evaluate
contextNoobjectSituational 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:

yaml
causes:
- cause: string # Identified cause
strength: number # 0.0-1.0 causal strength
mechanism: string # How cause produces effect
evidence: array[string] # Supporting evidence
type: string # direct, contributing, enabling
mechanism: string # Overall causal mechanism: how the primary cause produces the effect
strengths: # Comparative strengths
cause_1: number
cause_2: number
causal_chain:
- step: string # Intermediate step
from: string # Prior state/event
to: string # Resulting state/event
alternatives_ruled_out:
- cause: string
reason: string # Why ruled out
confidence: 0..1
evidence_anchors: ["file:line", "log:entry"]
assumptions: []
Field Definitions
FieldTypeDescription
causesarrayIdentified causes with strength and mechanism
causes[].strengthnumberCausal strength (0-1)
causes[].mechanismstringExplanation of causal pathway
causes[].typestringDirect, contributing, or enabling cause
mechanismstringOverall causal mechanism linking the primary cause to the effect
causal_chainarraySteps from cause to effect
alternatives_ruled_outarrayRejected alternatives with reasons
strengthsobjectCausal strength per cause, for comparison
confidencenumber0.0-1.0 confidence in the attribution
evidence_anchorsarrayReferences to logs, code, or data supporting each cause
assumptionsarrayAssumptions the attribution depends on

Examples

Example 1: Attribute Performance Degradation

Input:

yaml
effect: "API response time increased from 200ms to 2000ms"
candidates:
- "recent deployment"
- "database load increase"
- "memory leak"
context:
timing: "started 2024-01-15 14:30"
affected: "/api/users endpoint"

Output:

yaml
causes:
- cause: "N+1 query introduced in recent deployment"
strength: 0.85
mechanism: "New eager loading removed; each user now triggers 3 additional queries"
evidence:
- "Deployment at 14:25, issue started 14:30"
- "Query count per request increased from 2 to 50+"
- "Database CPU correlates with API latency"
type: "direct"
- cause: "Database connection pool saturation"
strength: 0.40
mechanism: "Increased queries exhaust connection pool, causing wait times"
evidence:
- "Pool utilization at 95%"
- "Connection wait time increased"
type: "contributing"
mechanism: "The deployment removed eager loading, multiplying queries per request (2 to 50+); the database overloads and responses slow from 200ms to 2000ms"
strengths:
n_plus_1_query: 0.85
connection_pool: 0.40
memory_leak: 0.05
causal_chain:
- step: "Code deployed"
from: "eager_load(:profile)"
to: "no eager loading"
- step: "Query multiplication"
from: "2 queries/request"
to: "50+ queries/request"
- step: "Database overload"
from: "normal CPU"
to: "90% CPU"
- step: "Response delay"
from: "200ms"
to: "2000ms"
alternatives_ruled_out:
- cause: "memory leak"
reason: "Memory usage stable, no growth pattern"
- cause: "external service latency"
reason: "No external calls in affected endpoint"
confidence: 0.85
evidence_anchors:
- "git log --since='2024-01-15 14:00'"
- "app/models/user.rb:45 (diff)"
- "monitoring:query_count"
- "monitoring:db_cpu"
assumptions:
- "Monitoring data is accurate"
- "No other deployments in window"
Example 2: Attribute Test Failure

Input:

yaml
effect: "UserService#create test failing with 'Email already taken'"
candidates:
- "test data pollution"
- "code change"
- "database state"

Output:

yaml
causes:
- cause: "Missing database cleanup between tests"
strength: 0.90
mechanism: "Test creates user, doesn't clean up, subsequent test finds duplicate"
evidence:
- "Test passes in isolation"
- "Test fails when run after create_user_test"
- "No database_cleaner configuration"
type: "direct"
mechanism: "A user created by an earlier test is never cleaned up, so a later create of the same email fails the uniqueness validation"
strengths:
test_pollution: 0.90
code_change: 0.05
fixture_issue: 0.05
causal_chain:
- step: "First test runs"
from: "empty database"
to: "user 'test@example.com' exists"
- step: "Second test attempts create"
from: "user exists"
to: "uniqueness validation fails"
alternatives_ruled_out:
- cause: "code change"
reason: "No recent changes to UserService"
- cause: "fixture issue"
reason: "Test doesn't use fixtures"
confidence: 0.90
evidence_anchors:
- "spec/services/user_service_spec.rb:45"
- "spec/rails_helper.rb (no database_cleaner)"
- "command:rspec spec/services/user_service_spec.rb:45 --order defined"
assumptions:
- "Test failure is deterministic with this order"
- "No parallel test execution"

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: false
  • requires_checkpoint: false
  • requires_approval: false
  • risk: 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 causes
  • observe - Observations reveal effects to attribute
  • measure - Measurements quantify effects

Commonly precedes:

  • plan - Causal understanding informs remediation
  • explain - Attribution enables explanation
  • critique - 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_change for attribution in debugging
  • See reference/workflow_catalog.yaml#world_model_build for causal modeling

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