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.
npx skills add majiayu000/claude-skill-registry --skill reasoning-abductive --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Type Signature
- When to Use
- Four-Stage Process
- Stage 1: Observation
- Stage 2: Hypotheses Generation
- Stage 3: Evidence Evaluation
- Stage 4: Best Explanation
- Quality Gates
- Common Failure Modes
- Output Contract
- Example Execution
What does the reasoning-abductive skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill reasoning-abductive --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.