science

name Science version 1.1.18 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). 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. 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 & 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.