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
code
┌─────────────────────────────────────────────────────────┐
│ 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
markdown
## 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:
- •FirstPrinciples - Deconstruct before hypothesizing
- •Engineer - Implement experiments
- •QATester - Validate results
Examples
Example 1: Performance debugging
code
"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
code
"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
Last Updated: 2026-02-02