attribute

name: attribute
description: Establish cause-effect relationships between events or states. Use when analyzing root causes, mapping dependencies, tracing effects, or building causal models.
argument-hint: "[effect] [candidates] [context]"
disable-model-invocation: false
user-invocable: true
allowed-tools: Read, Grep
context: fork
agent: explore
layer: MODEL

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/causal_model_schema.yaml

Inputs

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:

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
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

Examples

Example 1: Attribute Performance Degradation

Input:

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:

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"
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:

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

Output:

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"
strengths:
  test_pollution: 0.90
  code_change: 0.05
  fixture_issue: 0.05
causal_chain:
  - step: "First test runs"
    from: "empty database"
    to: "user '[email protected]' 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 workflow_catalog.yaml#debug_code_change for attribution in debugging
- See workflow_catalog.yaml#world_model_build for causal modeling