bundled/skills/math-model-selector/SKILL.md
Routes problems to appropriate mathematical frameworks using expert heuristics
npx skillsauth add foryourhealth111-pixel/vco-skills-codex math-model-selectorInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Trigger on phrases like:
Use when user has a problem but doesn't know which mathematical domain applies.
Guide user through decision tree using Polya-style questions:
Ask: "What quantity or phenomenon are you trying to understand?"
Ask: "What changes, and how does it change?"
Ask: "Is there randomness or uncertainty involved?"
Ask: "Are you optimizing something?"
Ask: "What level of answer do you need?"
Framework Recommendation:
- Primary: [framework name]
- Why: [one-sentence justification]
Starting Point:
- Key equations: [relevant formulas]
- Initial approach: [first step]
Tools to Use:
- [specific script or computation tool]
Related Skills:
- [domain skill to activate next]
User: I want to model how a rumor spreads through a population
Claude: Let me help you find the right framework.
Q1: What quantity are we tracking?
> Number of people who heard the rumor over time.
Q2: What causes it to change?
> Spread rate depends on who knows and who doesn't.
Q3: Is there randomness?
> Assume deterministic (average behavior) for now.
Q4: Optimizing anything?
> No, just understanding dynamics.
Q5: What precision?
> Qualitative behavior, maybe predict peak rate.
Framework Recommendation:
- Primary: First-order ODE (logistic growth)
- Why: Rate depends on current state nonlinearly
Starting Point:
- dN/dt = r * N * (1 - N/K)
- N = heard count, K = population, r = spread rate
Tools:
- sympy_compute.py solve (equilibrium)
- Phase line for qualitative behavior
Related Skills:
- math/math-intuition-builder (understand the model)
- odes-pdes/first-order-odes (solve it)
After framework selection, suggest:
development
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.
development
Use when the user asks to inspect Sentry issues or events, summarize recent production errors, or pull basic Sentry health data via the Sentry API; perform read-only queries with the bundled script and require `SENTRY_AUTH_TOKEN`.
development
World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-thought, and AI evaluation. Includes RAG optimization, agent design, and LLM system architecture. Use when building AI products, optimizing LLM performance, designing agentic systems, or implementing advanced prompting techniques.
development
World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.