science

name	science
description	Applies hypothesis-test-analyze loops to narrow uncertainty with evidence. Use when a problem is iterative, multiple hypotheses are plausible, or experimentation is the right way to learn.
key	science
include_when	Iterative problem. Experimentation needed. Multiple hypotheses to test.

science skill

Apply the scientific method with hypothesis formation, controlled testing, and analysis of results. Ideal for problems that require systematic experimentation.

Workflow Routing

Route to the appropriate workflow based on the request.

Trigger	Workflow
Full scientific investigation	Workflows/Investigate.md
Quick hypothesis test	Workflows/QuickTest.md

Quick Reference

Workflow	Purpose	Output
Investigate	Full scientific method cycle	Hypothesis → Experiment → Analysis → Conclusion
QuickTest	Rapid hypothesis validation	Single hypothesis → Test → Result

The Six-Step Method

┌─────────────────────────────────────────────────────────┐
│  STEP 1: OBSERVE                                         │
│  Gather data about the current state                     │
│  What do we know? What patterns exist?                   │
└─────────────────────────────────────────────────────────┘
                          ↓
┌─────────────────────────────────────────────────────────┐
│  STEP 2: HYPOTHESIZE                                     │
│  Form testable predictions                               │
│  "If X, then Y because Z"                                │
└─────────────────────────────────────────────────────────┘
                          ↓
┌─────────────────────────────────────────────────────────┐
│  STEP 3: DESIGN                                          │
│  Create experiments with controls                        │
│  What will you measure? What's the control?              │
└─────────────────────────────────────────────────────────┘
                          ↓
┌─────────────────────────────────────────────────────────┐
│  STEP 4: EXECUTE                                         │
│  Run experiments systematically                          │
│  Document everything, avoid bias                         │
└─────────────────────────────────────────────────────────┘
                          ↓
┌─────────────────────────────────────────────────────────┐
│  STEP 5: ANALYZE                                         │
│  Interpret results objectively                           │
│  Did results support or refute hypothesis?               │
└─────────────────────────────────────────────────────────┘
                          ↓
┌─────────────────────────────────────────────────────────┐
│  STEP 6: ITERATE                                         │
│  Refine hypotheses based on findings                     │
│  New questions → New cycle                               │
└─────────────────────────────────────────────────────────┘

When to Use

• Debugging: "Something is wrong but I don't know what" → Form hypotheses, test systematically
• Performance: "It's slow but why?" → Hypothesize bottlenecks, measure each
• Behavior: "Users aren't converting" → Hypothesize causes, A/B test
• Architecture: "Will this scale?" → Hypothesize limits, load test

Output Format

## Scientific Analysis: [Topic]

### Observation

- **Current State**: [What we know]
- **Patterns**: [What we notice]
- **Anomalies**: [What's unexpected]

### Hypotheses

| #   | Hypothesis    | Testable Prediction  | Priority     |
| --- | ------------- | -------------------- | ------------ |
| 1   | [If X then Y] | [Measurable outcome] | High/Med/Low |

### Experiment Design

- **Variable**: [What we're changing]
- **Control**: [What stays constant]
- **Measurement**: [How we'll know]

### Results

| Hypothesis | Prediction | Actual     | Supported?     |
| ---------- | ---------- | ---------- | -------------- |
| H1         | [Expected] | [Observed] | Yes/No/Partial |

### Conclusion

- **Finding**: [What we learned]
- **Confidence**: [How certain]
- **Next Steps**: [Follow-up experiments or actions]

Integration

Works well with:

• first-principles - Deconstruct before hypothesizing
• Engineer - Implement experiments
• QATester - Validate results

Examples

Example 1: Performance debugging

"Why is the API slow on Mondays?"
→ Observe: Response times spike 3x on Monday mornings
→ Hypothesize: H1: Cache cold after weekend. H2: Traffic spike. H3: Batch jobs.
→ Test: Monitor cache hits, traffic volume, job scheduler
→ Result: H1 supported - cache hit rate drops from 95% to 40%

Example 2: User behavior

"Why aren't users completing onboarding?"
→ Observe: 60% drop-off at step 3
→ Hypothesize: H1: Form too long. H2: Unclear instructions. H3: Bug.
→ Test: Session recordings, form analytics, error logs
→ Result: H2 supported - users pause 30+ seconds at field X

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Last Updated: 2026-02-02