Analyze events through the disciplinary lens of physics, applying fundamental physical laws (conservation of energy, momentum, mass; thermodynamics; electromagnetism; relativity), quantitative modeling, dimensional analysis, and systems dynamics to understand causation, evaluate constraints, assess technological feasibility, analyze energy systems, and identify physical limits that govern complex systems.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionphysicist-analystExecute the skills CLI command in your project's root directory to begin installation:
Fetches physicist-analyst from rysweet/amplihack and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate physicist-analyst. Access via /physicist-analyst in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Analyze events through the disciplinary lens of physics, applying fundamental physical laws (conservation of energy, momentum, mass; thermodynamics; electromagnetism; relativity), quantitative modeling, dimensional analysis, and systems dynamics to understand causation, evaluate constraints, assess technological feasibility, analyze energy systems, and identify physical limits that govern complex systems.
Physics analysis rests on fundamental principles:
Conservation Laws are Inviolable: Energy, momentum, mass-energy, angular momentum, and charge are conserved in all processes. Any claimed violation indicates error in analysis or measurement. These laws constrain all possible events and technologies.
Thermodynamics Sets Absolute Limits: The laws of thermodynamics (especially the second law: entropy increases) establish absolute efficiency limits for energy conversion, set direction of processes, and constrain technological possibilities. No cleverness can circumvent them.
Quantification and Measurement: Physics demands precise, quantitative understanding. Vague qualitative claims must be replaced with measurable quantities, units, and numerical predictions. "How much?" and "With what uncertainty?" are essential questions.
Symmetry and Invariance: Physical laws exhibit symmetries (e.g., laws are same everywhere, same in all directions, same over time). Symmetry principles reveal deep truths and guide prediction.
Causality and Mechanisms: Physics seeks mechanistic understanding: What physical processes cause observed phenomena? Correlation without mechanism is insufficient. Models must specify causal pathways grounded in physical laws.
Emergence from Fundamentals: Complex phenomena emerge from simpler, more fundamental laws. Understanding requires identifying relevant scales and principles. Reductionism is powerful but not always sufficient; emergent properties matter.
Models and Approximations: All models simplify reality. Good models capture essential physics while neglecting irrelevant details. Know your assumptions and approximations.
Dimensional Analysis: Checking units and scaling relationships reveals errors, guides intuition, and provides order-of-magnitude estimates without detailed calculation.
Physical Intuition: Develop sense for plausible magnitudes, timescales, and behaviors. "Does this answer make physical sense?" is a powerful check.
Core Principles:
Key Insights:
Applications:
Limitations:
When to Apply:
Sources:
Four Laws of Thermodynamics:
Zeroth Law: If A and B are in thermal equilibrium, and B and C are in thermal equilibrium, then A and C are in thermal equilibrium. (Establishes temperature as meaningful concept)
First Law: Energy is conserved. ΔU = Q - W (change in internal energy = heat added - work done)
Second Law: Entropy of isolated system increases over time. ΔS ≥ 0
Third Law: Entropy of perfect crystal at absolute zero is zero
Key Concepts:
Entropy: Measure of disorder or number of microstates. Drives spontaneous processes.
Carnot Efficiency: Maximum efficiency of heat engine: η = 1 - T_cold/T_hot
Free Energy: Energy available to do useful work (Gibbs and Helmholtz free energy)
Applications:
Implications:
When to Apply:
Sources:
Core Principles:
Maxwell's Equations: Four equations governing all classical electromagnetic phenomena
Key Insights:
Applications:
When to Apply:
Sources:
Core Principles:
Key Insights:
Applications:
When to Apply:
Sources:
Special Relativity (Einstein 1905):
Core Principles:
Applications:
General Relativity (Einstein 1915):
Core Principles:
Predictions (all confirmed):
Applications:
When to Apply:
Sources:
Statistical Mechanics: Connects microscopic behavior of particles to macroscopic thermodynamic properties
Core Principles:
Complex Systems Physics:
Emergent Properties: System exhibits behaviors not present in individual components
Nonlinearity and Feedback:
Scale Invariance and Power Laws:
Network Science:
Applications:
When to Apply:
Sources:
Purpose: Use units and dimensions to check equations, estimate magnitudes, and understand scaling behavior without detailed calculation
Process:
Buckingham Pi Theorem: Reduces number of variables by forming dimensionless groups
Applications:
Error Checking: Equation wrong if dimensions don't match on both sides
Order-of-Magnitude Estimates: "Fermi problems" - estimate without detailed calculation
Scaling Laws: Predict behavior at different sizes
Physical Intuition: Quickly assess plausibility
When to Apply:
Example - Energy Storage Claim: Claim: New battery stores 10 kWh in 1 kg
Sources:
Energy Forms:
Energy Conservation: Total energy conserved; transforms between forms
Energy Conversion Processes:
Efficiency: Useful energy out / Energy in
Energy Return on Investment (EROI): Energy delivered / Energy invested to produce
Analysis Process:
Example - Electric Vehicle Efficiency:
When to Apply:
Sources:
System Components:
Feedback Types:
Negative (Balancing) Feedback: Stabilizes system toward equilibrium
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Keeps context tight: physicist-analyst is the kind of skill you can hand to a new teammate without a long onboarding doc.
physicist-analyst has been reliable in day-to-day use. Documentation quality is above average for community skills.
physicist-analyst is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: physicist-analyst is focused, and the summary matches what you get after install.
Solid pick for teams standardizing on skills: physicist-analyst is focused, and the summary matches what you get after install.
Registry listing for physicist-analyst matched our evaluation — installs cleanly and behaves as described in the markdown.
We added physicist-analyst from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in physicist-analyst — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Keeps context tight: physicist-analyst is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added physicist-analyst from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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