caching-architecture
LiteLLM-RS Caching Architecture. Covers Redis caching, vector database semantic caching, multi-tier cache strategy, TTL management, and cache invalidation…
Skills
Find an Agent Skill for your task. Read the guide and copy the install command.
Updated 11-11 00:16
Agents & models
26,104 results
error-handling
LiteLLM-RS Error Handling Architecture. Covers two-tier error hierarchy, ProviderError factory methods, HTTP status mapping, retry logic, and error context…
mcp-gateway
LiteLLM-RS MCP Gateway Architecture. Covers Model Context Protocol, JSON-RPC 2.0 implementation, multi-transport support (HTTP, SSE, WebSocket, Stdio), and…
specrail-plan-tasks
Use when turning an approved SpecRail product and technical spec into the numbered `tasks.md` plan. Creates stable task IDs, owners, done-when conditions,…
streaming-architecture
LiteLLM-RS Streaming Architecture. Covers UnifiedSSEParser, SSETransformer trait, VecDeque buffering, provider-specific transformers, and real-time event…
mcp-audit
The English introduction is being prepared.
codex-issue-digest
Run a GitHub issue digest for openai/codex by feature-area labels, all areas, and configurable time windows. Use when asked to summarize recent Codex bug…
mcp-csharp-publish
Publish and deploy C# MCP servers. Covers NuGet packaging for stdio servers, Docker containerization for HTTP servers, Azure Container Apps and App Service…
mcp-csharp-test
Test C# MCP servers at multiple levels: unit tests for individual tools and integration tests using the MCP client SDK. USE FOR: unit testing MCP tool methods,…
team-brain-sync
Regenerate BRAIN.md and optionally generate .cursorrules and AGENTS.md from team brain entries. Use when user says 'sync', 'regenerate', 'update brain', or…
setup
Initialize or scaffold a repository with standardized project baselines (README, license, tests, lint/types, AGENTS.md, pre-commit, CI, skills). Use when the…
atomic-blocks
Composable atomic blocks for categorical meta-prompting. Defines 16 reusable blocks across 4 layers (Assessment, Transformation, Refinement, Composition) that…
categorical-meta-prompting
Full categorical framework for meta-prompting with verified laws. Use when implementing functor-based task routing, monadic iterative refinement, comonadic…
categorical-structure-builder
Universal template for implementing categorical structures in meta-prompting frameworks. Applies to functors, monads, comonads, natural transformations,…
dspy-categorical
DSPy compositional prompt optimization with categorical signatures, module chaining, and automated prompt tuning. Use when building declarative LLM programs…
dynamic-prompt-registry
Dynamic prompt registry with unified categorical syntax. Supports @skills:discover(), @skills:compose(A⊗B), Reader monad for runtime lookup, and…
effect-ts-ai
@effect/ai integration patterns for categorical AI composition, typed error handling, and production prompt pipelines. Use when building AI applications with…
guidance-grammars
Guidance library grammar-constrained generation with categorical structure. Use when implementing structured LLM outputs with grammar constraints, building…
hasktorch-typed
Hasktorch type-safe tensor operations with categorical structure preservation. Use when implementing type-safe deep learning in Haskell, leveraging dependent…
llm4s-scala
LLM4S Scala functional LLM interfaces with Effect system integration. Use when building LLM applications in Scala with ZIO or Cats Effect, implementing…
lmql-constraints
LMQL constraint-guided generation DSL for type-safe prompting with grammar constraints and logical conditions. Use when building structured LLM outputs with…
mcp-categorical
MCP (Model Context Protocol) server patterns with categorical tool composition and typed context protocols. Use when building MCP servers with categorical…
meta-self
Master reference for categorical meta-prompting unified syntax. Contains all modifiers, operators, composition patterns, and execution protocols. Use this…
prompt-benchmark
Systematic prompt evaluation framework with MATH, GSM8K, and Game of 24 benchmarks. Use when evaluating prompt effectiveness on standard benchmarks, comparing…