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Found 2,058 Skills
Designs multi-agent system architectures with orchestration patterns, tool schemas, and performance evaluation. Use when building AI agent systems, designing agent workflows, creating tool schemas, or evaluating agent performance.
Perform a PESTLE analysis covering Political, Economic, Social, Technological, Legal, and Environmental factors. Use when assessing the macro environment, doing strategic planning, or evaluating external factors affecting your business.
Master dispatcher for all MLflow workflows. Use this skill when the user wants to do anything with MLflow — tracing, evaluating, debugging, or improving an agent. Routes to the right MLflow sub-skill automatically. Triggers on: "use mlflow", "help with mlflow", "mlflow agent", "add mlflow to my project", "trace my agent", "evaluate my agent", or any MLflow task without a specific skill in mind.
Design multi-agent harnesses for long-running autonomous coding tasks. Covers generator/evaluator loops, context reset strategy, sprint contracts, and the planner-generator-evaluator architecture from Anthropic's harness research.
Update financial models with new data — quarterly earnings, management guidance, macro changes, or revised assumptions. Adjusts estimates, recalculates valuation, and flags material changes. Use after earnings, guidance updates, or when assumptions need refreshing. Triggers on "update model", "plug earnings", "refresh estimates", "update numbers for [company]", "new guidance", or "revise estimates".
Spot and evaluate trending product opportunities on Amazon, and tell a real trend from a fad. Reads trend signals, judges where a trend is in its curve, and decides whether a seller can enter in time to profit. Use when a user asks about trending products, hot products, viral products, jumping on a trend, trend spotting, or whether a product is a fad. Trigger phrases: "trending products", "hot products", "viral product", "is this a trend or a fad", "trend spotting", "should I jump on this trend". Works with zero tools.
Research and validate an Amazon product opportunity end to end, and evaluate whether the niche around it is winnable. Assesses demand, competition, profit potential, entry barriers, review wall, differentiation room, and seasonality, and returns a go/no-go with the reasoning. Use when a user asks to research a product, find a product to sell, validate a product idea, assess an opportunity, evaluate a niche, find a profitable niche, judge whether a category is worth entering, or compare niches. Trigger phrases: "product research", "find a product to sell", "validate this product", "is this a good product", "product opportunity", "should I sell this", "niche finder", "evaluate this niche", "is this niche worth it", "good niche", "low competition niche", "should I enter". Works with zero tools. the user describes the product and what they can observe.
Help users develop product taste and intuition. Use when someone wants to improve their product judgment, struggles to evaluate design quality, needs to make decisions without complete data, or wants to build better product instincts.
Review and improve documentation with parallel evaluation and iterative improvement loop.
Analyzes events through computer science lens using computational complexity, algorithms, data structures, systems architecture, information theory, and software engineering principles to evaluate feasibility, scalability, security. Provides insights on algorithmic efficiency, system design, computational limits, data management, and technical trade-offs. Use when: Technology evaluation, system architecture, algorithm design, scalability analysis, security assessment. Evaluates: Computational complexity, algorithmic efficiency, system architecture, scalability, data integrity, security.
Retrieve industry-specific P/E ratios using Octagon MCP. Use when comparing company valuations to specific industry peers, analyzing sub-sector valuations, and understanding niche market valuations beyond broad sector averages.
Master fine-tuning of large language models for specific domains and tasks. Covers data preparation, training techniques, optimization strategies, and evaluation methods. Use when adapting models for specialized applications, reducing inference costs, or improving domain-specific performance.