发现 文档写作 相关 Skills，比较来源仓库和用途，查看适合你工作流的技能说明。
LMQL constraint-guided generation DSL for type-safe prompting with grammar constraints and logical conditions. Use when building structured LLM outputs with guaranteed format compliance, implementing constraine...
MCP (Model Context Protocol) server patterns with categorical tool composition and typed context protocols. Use when building MCP servers with categorical composition patterns, designing tool interfaces using f...
Full categorical framework for meta-prompting with verified laws. Use when implementing functor-based task routing, monadic iterative refinement, comonadic context extraction, or quality-tracked composition. In...
Universal template for implementing categorical structures in meta-prompting frameworks. Applies to functors, monads, comonads, natural transformations, adjunctions, hom-equivalences, limits, colimits, and enri...
Recursive Meta-Prompting (RMP) implementation with unified categorical syntax. Supports @mode:iterative, @quality: thresholds, >=> Kleisli composition, and comonadic context extraction. Use when implementing it...
Dynamic prompt registry with unified categorical syntax. Supports @skills:discover(), @skills:compose(A⊗B), Reader monad for runtime lookup, and quality-tracked prompt libraries. Use for meta-prompts referencin...
Domain-specific language for categorical prompt composition with functor combinators and natural transformation operators. Use when building composable prompt templates, implementing typed prompt algebras, crea...
DSPy compositional prompt optimization with categorical signatures, module chaining, and automated prompt tuning. Use when building declarative LLM programs with typed signatures, composing multi-step reasoning...
@effect/ai integration patterns for categorical AI composition, typed error handling, and production prompt pipelines. Use when building AI applications with Effect-TS, composing LLM calls with typed errors, cr...
CC2.0 seven-function research workflow (observe, reason, create, orchestrate, learn, verify, deploy) for categorical AI research. Use when conducting systematic research on categorical AI topics, coordinating m...
Guidance library grammar-constrained generation with categorical structure. Use when implementing structured LLM outputs with grammar constraints, building template-based generation with guaranteed formats, cre...
LangGraph stateful multi-agent graphs with categorical coordination patterns and cyclic workflows. Use when building stateful AI agent systems, implementing multi-agent orchestration with conditional routing, c...
Property-based testing for functor laws, monad laws, and naturality conditions using fp-ts and fast-check. Use when validating categorical implementations in TypeScript, testing algebraic laws in functional cod...
Systematic analysis patterns for categorical AI papers (Gavranović, de Wynter, Bradley, Zhang). Use when analyzing academic papers on categorical deep learning, extracting categorical structures from AI researc...
DisCoPy categorical quantum NLP for string diagrams, monoidal categories, and compositional semantics in Python. Use when implementing compositional distributional semantics, building string diagram computation...
Hasktorch type-safe tensor operations with categorical structure preservation. Use when implementing type-safe deep learning in Haskell, leveraging dependent types for tensor shape verification, applying catego...
VoltAgent multi-agent system design with natural transformation coordination between agents. Use when building TypeScript multi-agent AI systems, implementing agent coordination with categorical patterns, desig...
Systematic prompt evaluation framework with MATH, GSM8K, and Game of 24 benchmarks. Use when evaluating prompt effectiveness on standard benchmarks, comparing meta-prompting strategies quantitatively, measuring...
[0,1]-enriched category implementation for gradient-based prompt quality optimization. Use when implementing quality-aware prompt systems, building enriched categorical structures for prompt evaluation, creatin...
Spivak-Niu polynomial functor implementations for learner composition and dynamical systems. Use when modeling learning systems as categorical structures, composing machine learning components with polynomial f...
Professional framework for building premium $5k+ SaaS websites with AI - the Define, Build, Review, Refine loop used by real product teams
Deep knowledge of legendary designers and their enduring contributions. Learn from Saul Bass, Massimo Vignelli, Dieter Rams, Paula Scher, and others whose work defines excellence. Use when seeking inspiration,...
Integrating Chrome DevTools and browser automation via MCP for live UI inspection, screenshot-to-code workflows, and visual debugging. Bridges the gap between design and implementation.
Core visual design principles that underpin all great design. Master gestalt psychology, visual hierarchy, composition, color theory, and typography fundamentals. Use when making design decisions or evaluating...
默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库，同一仓库的多个技能可能共享相同数值；它反映仓库关注度，不代表单个技能的安装量或质量评分。