Agent skill

reasoning-abductive

Generate and evaluate explanatory hypotheses from incomplete observations. Use when diagnosing anomalies, explaining unexpected outcomes, or inferring causes from effects. Produces ranked hypotheses with evidence and confidence scores.

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SKILL.md

Abductive Reasoning

Generate best explanations from observations. The logic of diagnosis and inference to cause.

Type Signature

Abductive : Observation → Hypotheses[] → Evidence → BestExplanation

Where:
  Observation     : RawData × Surprise → AnomalyDescription
  Hypotheses      : AnomalyDescription → [PossibleCause]
  Evidence        : [PossibleCause] × AvailableData → [ScoredHypothesis]
  BestExplanation : [ScoredHypothesis] → (Cause × Confidence × NextSteps)

When to Use

Use abductive when:

  • Anomaly or surprise present (metric deviation, unexpected behavior)
  • Need to explain an observation (why did X happen?)
  • Incomplete data, must infer cause
  • Diagnostic context (errors, issues, failures)
  • Multiple possible explanations exist

Don't use when:

  • Cause is already known (use causal execution instead)
  • Need to compare decision alternatives
  • Resolving stakeholder disagreements

Four-Stage Process

Stage 1: Observation

Purpose: Transform raw data into structured anomaly description.

Input:

yaml
observation:
  raw_data: "Conversion dropped from 12% to 7% in Q4"
  context:
    timeframe: "Q4 2025"
    baseline: "12% historical average"
    current: "7% observed"
  surprise_level: 0.8  # How unexpected is this?

Process:

  1. Quantify the deviation (% change, sigma from mean)
  2. Identify temporal boundaries (when did it start?)
  3. Scope the phenomenon (which segments affected?)
  4. Rate surprise level (expected vs unexpected)

Output:

yaml
anomaly:
  description: "42% drop in conversion rate (12% → 7%)"
  deviation: "-5 percentage points, -42% relative"
  temporal: "Started week 3 of Q4, persists through Q4"
  scope: "All segments equally affected"
  surprise: 0.8
  baseline_source: "12-month rolling average"

Stage 2: Hypotheses Generation

Purpose: Generate diverse possible explanations without judgment.

Rules:

  • Generate ≥5 hypotheses (more is better initially)
  • Include obvious AND non-obvious causes
  • Consider internal AND external factors
  • Don't filter yet - cast wide net

Hypothesis Categories:

Category Examples
Technical Site issues, bugs, performance
Product Features, pricing, positioning
Market Competition, trends, seasonality
Operational Team changes, process issues
External Economy, regulations, events

Output:

yaml
hypotheses:
  - id: H1
    cause: "Website performance degradation"
    category: technical
    mechanism: "Slow load times → abandonment"
    
  - id: H2
    cause: "Competitor launched aggressive pricing"
    category: market
    mechanism: "Price undercut → customer diversion"
    
  - id: H3
    cause: "Seasonal Q4 shopping behavior change"
    category: market
    mechanism: "Holiday spending patterns differ"
    
  - id: H4
    cause: "Product-market fit weakening"
    category: product
    mechanism: "Customer needs evolving away"
    
  - id: H5
    cause: "Sales qualification criteria changed"
    category: operational
    mechanism: "Different lead quality entering funnel"
    
  # ... continue until exhaustive

Stage 3: Evidence Evaluation

Purpose: Score each hypothesis against available evidence.

For each hypothesis, evaluate:

Criterion Question Score
Explanatory power Does it fully explain the anomaly? 0-1
Simplicity Fewest assumptions required? 0-1
Coherence Consistent with other known facts? 0-1
Testability Can we verify/falsify it? 0-1
Prior probability How likely independent of this data? 0-1

Evidence Collection:

yaml
evidence:
  H1_technical:
    supporting:
      - "Page load time increased 2s in Q4" (confidence: 0.9)
      - "Mobile bounce rate up 15%" (confidence: 0.85)
    contradicting:
      - "Desktop conversion stable" (confidence: 0.8)
    net_score: 0.65
    
  H2_competitor:
    supporting:
      - "Competitor launched Oct 15" (confidence: 1.0)
      - "Google Trends shows competitor interest up" (confidence: 0.7)
    contradicting:
      - "Our traffic unchanged" (confidence: 0.9)
    net_score: 0.55
    
  # ... evaluate all hypotheses

Scoring Formula:

Score(H) = (Explanatory × 0.3) + (Simplicity × 0.2) + 
           (Coherence × 0.25) + (Testability × 0.1) + 
           (Prior × 0.15)

Stage 4: Best Explanation

Purpose: Select most probable cause with confidence and next steps.

Ranking:

yaml
ranked_hypotheses:
  - rank: 1
    hypothesis: H1
    cause: "Website performance degradation"
    score: 0.78
    confidence: 0.75
    
  - rank: 2
    hypothesis: H3
    cause: "Seasonal behavior change"
    score: 0.62
    confidence: 0.60
    
  - rank: 3
    hypothesis: H2
    cause: "Competitor pricing"
    score: 0.55
    confidence: 0.50

Best Explanation Output:

yaml
conclusion:
  primary_cause: "Website performance degradation"
  confidence: 0.75
  mechanism: "2s increase in load time caused 42% more abandonment, 
              consistent with industry benchmarks (1s = ~7% conversion loss)"
  
  contributing_factors:
    - "Seasonal patterns may account for 10-15% of drop"
    
  ruled_out:
    - "Competitor pricing (traffic unchanged, not price-sensitive segment)"
    
  remaining_uncertainty:
    - "Whether mobile-specific or site-wide"
    - "Whether fix will fully restore conversion"
    
  next_steps:
    - "Verify: Run A/B test with performance fix (high priority)"
    - "Measure: Mobile vs desktop split post-fix"
    - "Monitor: Competitor activity (low priority)"
    
  suggested_next_mode: causal  # Ready to act on diagnosis

Quality Gates

Gate Requirement Failure Action
Hypothesis count ≥5 hypotheses Generate more before proceeding
Category diversity ≥3 categories Expand hypothesis search
Evidence present ≥1 data point per top-3 Gather more evidence
Confidence threshold ≥0.6 for best Flag as inconclusive
Testability Best hypothesis testable Propose test design

Common Failure Modes

Failure Symptom Fix
Anchoring First hypothesis gets all attention Force diversity in Stage 2
Confirmation bias Only seek supporting evidence Require contradicting evidence
Complexity creep Elaborate explanations preferred Weight simplicity appropriately
Premature closure Stop at first plausible cause Complete all 4 stages

Output Contract

yaml
abductive_output:
  conclusion:
    primary_cause: string
    confidence: float  # 0.0-1.0
    mechanism: string  # How cause produces effect
    
  hypotheses:
    ranked: [ScoredHypothesis]  # All evaluated
    ruled_out: [string]         # Definitively excluded
    
  evidence:
    supporting: [EvidenceItem]
    contradicting: [EvidenceItem]
    gaps: [string]  # What evidence is missing?
    
  uncertainty:
    remaining_questions: [string]
    confidence_bounds: [float, float]  # Low, high
    
  next:
    immediate_actions: [string]
    tests_to_run: [string]
    suggested_mode: optional<ReasoningMode>
    
  trace:
    stages_completed: [1, 2, 3, 4]
    duration_ms: int
    hypotheses_generated: int
    evidence_points: int

Example Execution

Context: "Enterprise conversion dropped 40% last quarter"

Stage 1 - Observation:

Anomaly: 40% drop (15% → 9%) in enterprise conversion
Temporal: Started week 5 of Q3, accelerated Q4
Scope: Enterprise only, SMB stable
Surprise: 0.85

Stage 2 - Hypotheses:

H1: Enterprise buyer behavior changed (economic uncertainty)
H2: Sales team restructuring disrupted relationships
H3: Competitor launched enterprise-specific offering
H4: Our enterprise pricing became uncompetitive
H5: Product gaps for enterprise use cases
H6: Longer sales cycles (not drop, just delay)
H7: Key account manager departures

Stage 3 - Evidence:

H1: Supporting (CFO involvement up 40%), Contradicting (overall enterprise IT spend flat)
H2: Supporting (3 senior reps left Q3), Contradicting (coverage maintained)
H6: Supporting (average cycle +45 days), Strong supporting
H7: Supporting (2 key AMs left), Moderate supporting

Stage 4 - Conclusion:

Primary: Sales cycle elongation (not true drop) + AM departures (relationship gaps)
Confidence: 0.72
Mechanism: Economic uncertainty extended CFO approval cycles by 45 days;
           AM departures created relationship gaps in 6 key accounts

Next: Wait 45 days to see if "delayed" deals close (causal monitoring)
      Immediately backfill AM roles (causal action)

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