science

name: Science description: "The scientific method as a universal problem-solving algorithm — goal-first, hypothesis-plural, falsifiable experiments, honest measurement. Seven core workflows: DefineGoal, GenerateHypotheses (minimum 3 required — single-hypothesis testing is confirmation bias), DesignExperiment, MeasureResults, AnalyzeResults, Iterate, and FullCycle. Two diagnostic shortcuts: QuickDiagnosis (15-minute rule for fast debugging) and StructuredInvestigation (complex multi-factor issues). Scales across micro (TDD, minutes), meso (feature validation, hours-days), and macro (MVP launch, weeks-months). Reference files: METHODOLOGY.md (deep dive on each phase), Protocol.md (how other skills invoke Science), Templates.md (goal/hypothesis/experiment/results templates), Examples.md (worked examples across scales). Integrates with Council (hypothesis validation), Evals (measurement), Development (parallel experiment worktrees), and RedTeam (stress-test hypotheses). RootCauseAnalysis applies Science to failure investigation — pair them when investigating incidents. NOT FOR multi-angle lens passes on requirements (use IterativeDepth for pre-build exploration). USE WHEN think about, figure out, experiment, iterate, improve, optimize, hypothesis, science, full cycle, quick diagnosis, structured investigation, what might work, how do we test, what happened, analyze results." effort: high

Customization

Before executing, check for user customizations at: ~/.claude/PAI/USER/SKILLCUSTOMIZATIONS/Science/

If this directory exists, load and apply any PREFERENCES.md, configurations, or resources found there. These override default behavior. If the directory does not exist, proceed with skill defaults.

🚨 MANDATORY: Voice Notification (REQUIRED BEFORE ANY ACTION)

You MUST send this notification BEFORE doing anything else when this skill is invoked.

1. Send voice notification:

   curl -s -X POST http://localhost:31337/notify \
     -H "Content-Type: application/json" \
     -d '{"message": "Running the WORKFLOWNAME workflow in the Science skill to ACTION"}' \
     > /dev/null 2>&1 &
   

2. Output text notification:

   Running the **WorkflowName** workflow in the **Science** skill to ACTION...
   

This is not optional. Execute this curl command immediately upon skill invocation.

Science - The Universal Algorithm

The scientific method applied to everything. The meta-skill that governs all other skills.

The Universal Cycle

GOAL -----> What does success look like?
   |
OBSERVE --> What is the current state?
   |
HYPOTHESIZE -> What might work? (Generate MULTIPLE)
   |
EXPERIMENT -> Design and run the test
   |
MEASURE --> What happened? (Data collection)
   |
ANALYZE --> How does it compare to the goal?
   |
ITERATE --> Adjust hypothesis and repeat
   |
   +------> Back to HYPOTHESIZE

The goal is CRITICAL. Without clear success criteria, you cannot judge results.

Workflow Routing

Output when executing: Running the **WorkflowName** workflow in the **Science** skill to ACTION...

Core Workflows

Diagnostic Workflows

Resource Index

Domain Applications

Scale of Application

Integration Points

Key Principles (Quick Reference)

1. Goal-First - Define success before starting
2. Hypothesis Plurality - NEVER just one idea (minimum 3)
3. Minimum Viable Experiments - Smallest test that teaches
4. Falsifiability - Experiments must be able to fail
5. Measure What Matters - Only goal-relevant data
6. Honest Analysis - Compare to goal, not expectations
7. Rapid Iteration - Cycle speed > perfect experiments

Anti-Patterns

Quick Start

1. Goal - What does success look like?
2. Observe - What do we know?
3. Hypothesize - At least 3 ideas
4. Experiment - Minimum viable tests
5. Measure - Collect goal-relevant data
6. Analyze - Compare to success criteria
7. Iterate - Adjust and repeat

The answer emerges from the cycle, not from guessing.

Gotchas

• Hypothesis-test-analyze is the core loop. Don't skip the hypothesis step — going straight to testing is just trial-and-error, not science.
• Minimum 3 hypotheses before testing. Single-hypothesis testing is confirmation bias.
• Measurements must be specific and reproducible. "It seems better" is not a measurement.
• Full cycle is for systematic investigation. For quick debugging, use quick diagnosis mode.

Examples

Example 1: Quick diagnosis

User: "figure out why Surface time filters show stale items"
→ Quick diagnosis mode
→ Hypothesis: timestamp format mismatch in D1
→ Test: query D1 for actual stored format
→ Analyze: compare stored vs expected format
→ Result: ISO string vs Unix timestamp mismatch

Example 2: Full systematic investigation

User: "experiment with different prompt structures for better output"
→ Full cycle mode
→ 3+ hypotheses generated
→ Controlled experiments with measurements
→ Analysis identifies winning approach
→ Iterates until convergence

Execution Log

After completing any workflow, append a single JSONL entry:

echo '{"ts":"'$(date -u +%Y-%m-%dT%H:%M:%SZ)'","skill":"Science","workflow":"WORKFLOW_USED","input":"8_WORD_SUMMARY","status":"ok|error","duration_s":SECONDS}' >> ~/.claude/PAI/MEMORY/SKILLS/execution.jsonl

Replace WORKFLOW_USED with the workflow executed, 8_WORD_SUMMARY with a brief input description, and SECONDS with approximate wall-clock time. Log status: "error" if the workflow failed.