science

name	Science
version	1.1.17
description	The scientific method as a universal problem-solving algorithm — goal-first, plural falsifiable hypotheses, designed experiments, and honest measurement, scaling from TDD to feature validation to MVP launch. USE WHEN think about, figure out, experiment, iterate, optimize, hypothesis, science, full cycle, quick diagnosis, structured investigation, how do we test, analyze results. NOT FOR multi-angle lens passes (use IterativeDepth).
effort	high

Customization

Before executing, check for user customizations at: ~/.claude/LIFEOS/USER/CUSTOMIZATIONS/SKILLS/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

What It Does

Applies the scientific method as a general problem-solving algorithm: define the goal first, generate multiple hypotheses, design experiments that can fail, measure honestly, analyze against the goal, iterate. Seven core workflows plus two diagnostic shortcuts (quick 15-minute debugging and structured multi-factor investigation). It scales from micro (TDD) to meso (feature validation) to macro (MVP launch).

The Problem

Most problem-solving is guessing dressed up as work. You pick the first idea that comes to mind, change something, and call it done when it "seems better" — which is confirmation bias, not progress. Without a clear definition of success you can't tell whether a change helped, so you keep tweaking forever or stop too early. Single-hypothesis thinking means you only ever test the idea you already believed. This skill forces the discipline that fixes all of that: a stated goal, at least three competing hypotheses, falsifiable tests, and measurement that compares to the goal rather than to your hopes.

How It Works

The whole thing is one repeating cycle, and the goal anchors it — without clear success criteria you cannot judge results:

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 answer emerges from the cycle, not from guessing.

---

Workflow Routing

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

Core Workflows

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

Diagnostic Workflows

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

---

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

---

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

---

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

---

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

---

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"

---

Gotchas

• Minimum 3 hypotheses before testing. Single-hypothesis testing is confirmation bias — going straight to a single test is trial-and-error, not science.
• 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/LIFEOS/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.