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Found 1,327 Skills
Build and run FastFold BoltzGen protein-design workflows end-to-end through API or Composer draft links. Use this whenever users mention BoltzGen, design-spec YAMLs, binder design, multi-spec scaffold workflows, CIF/PDB preparation, workflow graph upsert, `/workflow/composer/<id>`, candidate metrics/structure results, or ask naturally for "help me design a protein" / "give me a simple example".
Deep linter reference for authoring or debugging a vigiles enforce() rule — plugin tables, AST selectors, type-aware rules, auto-fix, and edge cases for ESLint, Ruff, Pylint, RuboCop, and Stylelint. Use when you need the exact rule name or config for a specific linter, not for running a linter.
Research on Historical Isomorphism and Standard Answers. Abstract the complex dilemma the user is facing into a structural fingerprint, search for truly isomorphic successful cases, failed cases, and counterexamples in business history, management history, technology history, career history, and institutional history. Compare the constraints, decisions, and outcomes of the parties involved, and extract recurring solutions, applicable conditions, and failure boundaries. Trigger methods: /dbs-standard-answer, /标准答案 (Standard Answer), "Who faced similar problems in history?", "Has anyone encountered this situation before?", "How did they resolve it back then?", "Is there a classic solution to this problem?", "First help me find historical analogies?", "What was the standard answer in the past?". Find structurally analogous historical cases and evidence-backed standard answers. Use when the user wants to situate a current dilemma in history, compare how others addressed it, and extract recurring mechanisms with conditions and limits.
Best practices and example-driven guidance for building SwiftUI views and components. Use when creating or refactoring SwiftUI UI, designing tab architecture with TabView, composing screens, or needing component-specific patterns and examples.
Create comprehensive TypeScript documentation using JSDoc, TypeDoc, and multi-layered documentation patterns for different audiences. Includes API documentation, architectural decision records (ADRs), code examples, and framework-specific patterns for NestJS, Express, React, Angular, and Vue.
Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits), manipulating algebraic expressions, working with matrices symbolically, physics calculations, number theory problems, geometry computations, and generating executable code from mathematical expressions. Apply this skill when the user needs exact symbolic results rather than numerical approximations, or when working with mathematical formulas that contain variables and parameters.
Iterative UI/UX polishing workflow for web applications. The exact prompt and methodology for achieving Stripe-level visual polish through multiple passes.
Validate startup ideas using Hexa's Opportunity Memo framework and Perceived Created Value (PCV) methodology. Assess problem-solution fit, market opportunity, and determine if an idea is worth pursuing.
The meta-skill that powers all other AI tools. Prompt engineering for creative applications is the art and science of communicating with AI models to produce exactly what you envision—in images, video, audio, and text. This isn't just "write better prompts." It's understanding how different models interpret language, how to structure requests for different modalities, how to iterate systematically, and how to build prompt libraries that encode your creative vision. The best prompt engineers have developed intuition for what words trigger what responses in each model. This skill is foundational—it amplifies the effectiveness of every other AI creative skill. Master this, and you master the interface to all AI creation. Use when "prompt, prompting, prompt engineering, better prompts, prompt optimization, how to prompt, prompt strategy, prompt library, prompt template, make AI understand, prompt-engineering, prompting, meta-skill, ai-creative, foundational, optimization, iteration" mentioned.
Designs robust function/tool calling schemas for LLMs with JSON schemas, validation strategies, typed interfaces, and example calls. Use when implementing "function calling", "tool use", "LLM tools", or "agent actions".
Recommend font combinations for different use cases. Provide Google Fonts alternatives to premium fonts with hierarchy examples.
Use when user input contains xlb topic queries (for example "xlb >vibe coding/vib", "xlb ??vibe coding", or "查询xlb vibe coding主题") and the task is to fetch Markdown index from local getPluginInfo API, then perform code-based retrieval with routing to available network skills/MCP tools when possible.