science

name

Science

description

Hypothesis-test-analyze cycles for systematic problem-solving — the meta-skill governing all others. Includes define goal, generate hypotheses, design experiment, measure results, analyze results, iterate, full cycle, quick diagnosis, and structured investigation. USE WHEN think about, figure out, try approaches, experiment with, iterate on, improve, optimize, define goal, generate hypotheses, design experiment, measure results, analyze results, full cycle, quick diagnosis, structured investigation, science, hypothesis.

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:8888/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

Trigger	Workflow
"define the goal", "what are we trying to achieve"	Workflows/DefineGoal.md
"what might work", "ideas", "hypotheses"	Workflows/GenerateHypotheses.md
"how do we test", "experiment design"	Workflows/DesignExperiment.md
"what happened", "measure", "results"	Workflows/MeasureResults.md
"analyze", "compare to goal"	Workflows/AnalyzeResults.md
"iterate", "try again", "next cycle"	Workflows/Iterate.md
Full structured cycle	Workflows/FullCycle.md

Diagnostic Workflows

Trigger	Workflow
Quick debugging (15-min rule)	Workflows/QuickDiagnosis.md
Complex investigation	Workflows/StructuredInvestigation.md

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Resource Index

Resource	Description
METHODOLOGY.md	Deep dive into each phase
Protocol.md	How skills implement Science
Templates.md	Goal, Hypothesis, Experiment, Results templates
Examples.md	Worked examples across scales

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Domain Applications

Domain	Manifestation	Related Skill
Coding	TDD (Red-Green-Refactor)	Development
Products	MVP -> Measure -> Iterate	Development
Research	Question -> Study -> Analyze	Research
Prompts	Prompt -> Eval -> Iterate	Evals
Decisions	Options -> Council -> Choose	Council

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Scale of Application

Level	Cycle Time	Example
Micro	Minutes	TDD: test, code, refactor
Meso	Hours-Days	Feature: spec, implement, validate
Macro	Weeks-Months	Product: MVP, launch, measure PMF

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Integration Points

Phase	Skills to Invoke
Goal	Council for validation
Observe	Research for context
Hypothesize	Council for ideas, RedTeam for stress-test
Experiment	Development (Worktrees) for parallel tests
Measure	Evals for structured measurement
Analyze	Council for multi-perspective analysis

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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

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Anti-Patterns

Bad	Good
"Make it better"	"Reduce load time from 3s to 1s"
"I think X will work"	"Here are 3 approaches: X, Y, Z"
"Prove I'm right"	"Design test that could disprove"
"Pretend failure didn't happen"	"What did we learn?"
"Keep experimenting forever"	"Ship and learn from production"

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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.