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Science

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danielmiessler
Science

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).

Overview

Publisherdanielmiessler
RepositoryLifeOS
Skill nameScience
Stars
19K
Forks
2.5K
Bundled files
13
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 13 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by danielmiessler on GitHub. Read the source before you install it.

Installation

Install the Science AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/danielmiessler/LifeOS.git /tmp/LifeOS
mkdir -p .claude/skills
cp -r /tmp/LifeOS/LifeOS/install/skills/Science .claude/skills/Science
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Science in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Science on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Science is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

    bash
    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

WorkflowTriggerFile
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
FullCycleFull structured cycleWorkflows/FullCycle.md

Diagnostic Workflows

WorkflowTriggerFile
QuickDiagnosisQuick debugging (15-min rule)Workflows/QuickDiagnosis.md
StructuredInvestigationComplex investigationWorkflows/StructuredInvestigation.md

Resource Index

ResourceDescription
Methodology.mdDeep dive into each phase
Protocol.mdHow skills implement Science
Templates.mdGoal, Hypothesis, Experiment, Results templates
Examples.mdWorked examples across scales

Domain Applications

DomainManifestationRelated Skill
CodingTDD (Red-Green-Refactor)Development
ProductsMVP -> Measure -> IterateDevelopment
ResearchQuestion -> Study -> AnalyzeResearch
PromptsPrompt -> Eval -> IterateEvals
DecisionsOptions -> Council -> ChooseCouncil

Scale of Application

LevelCycle TimeExample
MicroMinutesTDD: test, code, refactor
MesoHours-DaysFeature: spec, implement, validate
MacroWeeks-MonthsProduct: MVP, launch, measure PMF

Integration Points

PhaseSkills to Invoke
GoalCouncil for validation
ObserveResearch for context
HypothesizeCouncil for ideas, RedTeam for stress-test
ExperimentDevelopment (Worktrees) for parallel tests
MeasureEvals for structured measurement
AnalyzeCouncil for multi-perspective analysis

Anti-Patterns

BadGood
"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:

bash
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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Science AI skill do?

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).

Why use Science on TypingMind?

Because you install it once and use it with any model. Science is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Science in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/danielmiessler/LifeOS/tree/main/LifeOS/install/skills/Science. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Science?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Science?

As many as you like. As long as a model supports skills, you can use Science with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Science AI skill free?

Yes. It is published on GitHub by danielmiessler under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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