Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Break problems to fundamentals, rebuild from truth, eliminate hidden assumptions.
Break problems to fundamentals, rebuild from truth, eliminate hidden assumptions.
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
User faces complex problem where conventional solutions fail. Existing approaches seem inadequate. Need to challenge assumptions or innovate fundamentally. Stuck in "that's how it's always done" thinking.
TopicFileDecomposition techniquesdecomposition.mdCommon assumption trapsassumptions.md
Step 1 β Decompose: Break the problem into fundamental components. What are the absolute physical/logical constraints? What is actually true vs what we assume is true? Strip away all conventions, traditions, analogies. Step 2 β Verify: Challenge each component. "Why do we believe this?" β trace to origin "Is this a law of nature or a human convention?" "What evidence supports this being fundamental?" Step 3 β Rebuild: Construct solution from verified fundamentals only. Build up from proven truths Ignore "how others do it" unless proven optimal Each layer must connect to fundamentals
Before solving, expose what's assumed: Assumption TypeExampleQuestion to AskHistorical"We've always done it this way""Why did it start? Does that reason still apply?"Authority"Experts say X""What's the underlying evidence?"Analogical"It's like Y, so...""Are the underlying mechanics actually similar?"Social"Everyone does it""Does popularity equal optimality?"Resource"We can't afford to...""What if resources weren't the constraint?"
For each constraint ask: Is this a law of physics? β Respect it Is this a logical necessity? β Respect it Is this a regulation/rule? β Can be changed (with effort) Is this a convention? β Can be ignored Is this an assumption? β Must be verified
First principles is expensive. Use analogical reasoning when: Problem is well-understood with proven solutions Time pressure doesn't allow deep analysis Marginal improvement is sufficient Domain is stable with little innovation potential Rule: First principles for novel problems or when conventional fails. Analogy for routine optimization.
Use recursive "why" questioning: Problem: "Electric cars are too expensive" Why expensive? β Batteries cost a lot Why batteries expensive? β Materials + manufacturing Why materials expensive? β Cobalt, lithium pricing Why those materials? β Current chemistry requires them Is that fundamental? β No, chemistry can change Fundamental: Need energy storage. Not: Need cobalt batteries. Continue until you hit physics, logic, or math β things that cannot be argued.
Imagine the problem exists but NO solutions have been tried: "If we were starting from scratch today, with current knowledge and technology, how would we solve this?" This bypasses legacy thinking and sunk cost fallacy.
Stopping too early β "Materials are expensive" isn't fundamental; "atoms have mass" is. Keep going. Confusing difficulty with impossibility β "It's hard" β "It's against physics" Rejecting all analogy β Analogies are useful heuristics; first principles is for when they fail Analysis paralysis β Set time limits; perfect decomposition isn't the goal, better thinking is Ignoring implementation β A fundamental solution that can't be built is useless; constraints matter Lone wolf thinking β First principles benefits from multiple perspectives challenging assumptions
DomainFirst Principles QuestionBusinessWhat does the customer fundamentally need (not want)?EngineeringWhat do physics and materials actually allow?ProductWhat job is being done at the most basic level?CostWhat are the raw inputs and minimum required labor?ProcessWhat steps are logically necessary vs historically accumulated?
Data that stays local: All reasoning happens in conversation context No data stored or transmitted This skill does NOT: Store any information between sessions Make network requests Access external files
Install with clawhub install <slug> if user confirms: decide β auto-learn decision patterns business β validate and refine strategy ceo β executive decision-making startup β build from zero to PMF
If useful: clawhub star first-principles-thinking Stay updated: clawhub sync
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Largest current source with strong distribution and engagement signals.