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Found 1,167 Skills
Design architecture for Ark features following existing patterns and principles. Use when planning new features, extending components, or evaluating technical approaches.
Write and evaluate effective Python tests using pytest. Use when writing tests, reviewing test code, debugging test failures, or improving test coverage. Covers test design, fixtures, parameterization, mocking, and async testing.
Query the BCRA (Banco Central de la República Argentina) Central de Deudores API to check the credit status of individuals or companies in Argentina's financial system. Use when the user asks to check someone's debt situation, credit report, financial standing, rejected checks, or credit history using a CUIT/CUIL/CDI number. Also use when the user mentions "central de deudores", "situación crediticia", "deudas BCRA", "cheques rechazados", "historial crediticio", "informe crediticio", or wants to know if a person or company has debts reported in Argentina's financial system.
Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, conducting literature reviews, finding related work, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, citation verification workflows, and paper discovery/evaluation criteria.
A pattern for generating higher-quality output by iterating against explicit scoring criteria. Use for headlines, CTAs, landing page copy, social content, ad copy — anything where quality matters. Generate → Evaluate → Diagnose → Improve → Repeat.
Use when asked to "thinking in bets", "make decisions under uncertainty", "think probabilistically", "avoid resulting", "separate decision quality from outcomes", or "reduce bias in decisions". Helps make explicit bets and evaluate decisions on process, not results. The Thinking in Bets framework (from Annie Duke) applies poker strategy to business and life decisions.
Design experiment plans with progressive stages — initial implementation, baseline tuning, creative research, and ablation studies. Plan baselines, datasets, hyperparameter sweeps, and evaluation metrics. Use when planning experiments for a research paper.
Interactive decision-making wizard using STREAM 6-layer framework for founders facing high-stakes choices. Use when user says "help me decide", "should I do this", "evaluate decision", "STREAM analysis", "run decision framework", or "pros and cons". Do NOT use for idea validation with PRD (use /validate).
Comprehensive patient stratification for precision medicine by integrating genomic, clinical, and therapeutic data. Given a disease/condition, genomic data (germline variants, somatic mutations, expression), and optional clinical parameters, performs multi-phase analysis across 9 phases covering disease disambiguation, genetic risk assessment, disease-specific molecular stratification, pharmacogenomic profiling, comorbidity/DDI risk, pathway analysis, clinical evidence and guideline mapping, clinical trial matching, and integrated outcome prediction. Generates a quantitative Precision Medicine Risk Score (0-100) with risk tier assignment (Low/Intermediate/High/Very High), treatment algorithm (1st/2nd/3rd line), pharmacogenomic guidance, clinical trial matches, and monitoring plan. Use when clinicians ask about patient risk stratification, treatment selection, prognosis prediction, or personalized therapeutic strategy across cancer, metabolic, cardiovascular, neurological, or rare diseases.
Comprehensive computational validation of drug targets for early-stage drug discovery. Evaluates targets across 10 dimensions (disambiguation, disease association, druggability, chemical matter, clinical precedent, safety, pathway context, validation evidence, structural insights, validation roadmap) using 60+ ToolUniverse tools. Produces a quantitative Target Validation Score (0-100) with GO/NO-GO recommendation. Use when users ask about target validation, druggability assessment, target prioritization, or "is X a good drug target for Y?"
Predict patient response to immune checkpoint inhibitors (ICIs) using multi-biomarker integration. Given a cancer type, somatic mutations, and optional biomarkers (TMB, PD-L1, MSI status), performs systematic analysis across 11 phases covering TMB classification, neoantigen burden estimation, MSI/MMR assessment, PD-L1 evaluation, immune microenvironment profiling, mutation-based resistance/sensitivity prediction, clinical evidence retrieval, and multi-biomarker score integration. Generates a quantitative ICI Response Score (0-100), response likelihood tier, specific ICI drug recommendations with evidence, resistance risk factors, and a monitoring plan. Use when oncologists ask about immunotherapy eligibility, checkpoint inhibitor selection, or biomarker-guided ICI treatment decisions.
Transform GWAS signals into actionable drug targets and repurposing opportunities. Performs locus-to-gene mapping, target druggability assessment, existing drug identification, safety profile evaluation, and clinical trial matching. Use when discovering drug targets from GWAS data, finding drug repurposing opportunities from genetic associations, or translating GWAS findings into therapeutic leads.