Skills

Assists with writing, structuring, and editing scientific manuscripts, grant proposals, and technical reports. Supports IMRaD structure, proper citation formatting, abstract writing, methods sections with reproducibility details, results interpretation, and response to reviewer comments. Adapts to journal-specific guidelines and word limits.

Assists researchers in generating, refining, and evaluating scientific hypotheses. Analyzes existing literature and data to propose testable hypotheses, identifies confounding variables, suggests experimental designs, and evaluates feasibility. Supports structured frameworks like PICO for clinical research and helps formulate null/alternative hypotheses with appropriate statistical tests.

Performs cheminformatics and molecular property analysis using RDKit, PubChem, and ChEMBL. Supports SMILES/InChI parsing, molecular descriptor calculation, drug-likeness filtering (Lipinski, Veber), ADMET prediction, substructure search, and structure-activity relationship (SAR) analysis for drug discovery workflows.

Builds and executes bioinformatics analysis pipelines for genomics, transcriptomics, and proteomics data. Supports single-cell RNA-seq analysis with Scanpy, differential expression with PyDESeq2, sequence alignment, variant calling, gene ontology enrichment, and pathway analysis using KEGG and Reactome databases.

Conducts systematic literature reviews across scientific databases including PubMed, arXiv, bioRxiv, and Semantic Scholar. Synthesizes findings from multiple papers, identifies research gaps, maps citation networks, and produces structured review documents suitable for grant proposals or publication introductions.

Records learnings, mistakes, and corrections to enable continuous improvement of AI agent behavior. Maintains a structured memory of failures, user corrections, outdated knowledge, and discovered better approaches. Reviews past learnings before executing important tasks to avoid repeating mistakes.

Builds and queries typed knowledge graphs for structured agent memory and composable skills. Creates entities (people, projects, tasks, events, documents), links related objects, enforces constraints, and plans multi-step actions as graph transformations. Enables persistent, queryable memory across agent sessions.

Security-first AI agent skill auditing. Reviews skill definitions, SKILL.md files, and agent configurations for dangerous patterns, excessive permissions, data exfiltration risks, and suspicious behaviors before installation. Provides a safety score and actionable recommendations.

Assists with deploying applications to cloud platforms including AWS, GCP, Azure, and Vercel/Netlify. Generates infrastructure-as-code (Terraform, Pulumi, CDK), Dockerfiles, CI/CD pipelines, and deployment scripts. Handles environment configuration, secrets management, and production readiness checks.

Helps architect scalable distributed systems by analyzing requirements and producing high-level architecture diagrams, component breakdowns, data flow descriptions, and technology recommendations. Covers load balancing, caching strategies, database selection, message queues, and failure handling patterns.

Designs and scaffolds AI agent architectures including tool definitions, system prompts, memory strategies, and orchestration logic. Supports multi-agent workflows, ReAct patterns, function calling schemas, and MCP server configurations. Helps structure agents that are reliable, observable, and easy to debug.

Analyzes SQL queries and database schemas to identify performance bottlenecks and suggest optimizations. Recommends index strategies, query rewrites, denormalization opportunities, and partitioning schemes. Explains EXPLAIN plans and provides before/after comparisons with expected performance improvements.

Plans and generates migration strategies for framework upgrades, language versions, database changes, and architecture shifts. Produces step-by-step migration guides with rollback plans, risk assessment, and automated codemods where possible.

Translates code between programming languages while preserving logic, idioms, and best practices of the target language. Handles differences in type systems, error handling, concurrency models, and standard library APIs. Produces idiomatic target code, not line-by-line transliteration.

Performs comprehensive security analysis of code and configurations. Identifies OWASP Top 10 vulnerabilities, insecure patterns, missing input validation, authentication flaws, and secrets exposure. Provides remediation steps with secure code examples.

Designs, optimizes, and iterates on prompts for LLM applications. Covers system prompt design, few-shot examples, chain-of-thought reasoning, output formatting, and prompt testing strategies. Helps build reliable AI-powered features.

Identifies and resolves performance bottlenecks in code and systems. Covers algorithmic complexity analysis, memory optimization, caching strategies, database query tuning, and frontend performance (Core Web Vitals). Follows a measure-first approach.

Guides systematic code refactoring while preserving exact behavior. Identifies code smells, suggests appropriate refactoring patterns, and executes transformations incrementally with verification at each step. Follows Chesterton's Fence principle — understands why code exists before changing it.

Designs normalized database schemas from business requirements. Covers entity relationships, indexing strategies, migration planning, and performance considerations. Supports PostgreSQL, MySQL, MongoDB, and other databases with dialect-specific optimizations.

Designs RESTful and GraphQL APIs following contract-first principles. Covers endpoint structure, request/response schemas, error handling, versioning, pagination, authentication, and rate limiting. Produces OpenAPI/Swagger specifications and implementation scaffolding.