rlm
Run a Recursive Language Model-style loop for long-context tasks. Uses a persistent local Python REPL and an rlm-subcall subagent as the sub-LLM (llm_query).
Découvrez les skills tools llm ai, comparez leurs dépôts et consultez leurs instructions.
Run a Recursive Language Model-style loop for long-context tasks. Uses a persistent local Python REPL and an rlm-subcall subagent as the sub-LLM (llm_query).
Prevents generic AI-generated designs by guiding typography, color, motion, and background choices. Use when creating frontend designs, landing pages, dashboards, or any UI/UX work. Helps avoid the "AI slop" ae...
Use mcpc CLI to interact with MCP servers - call tools, read resources, get prompts. Use this when working with Model Context Protocol servers, calling MCP tools, or accessing MCP resources programmatically.
This skill should be used whenever users need YouTube video scripts written. On first use, collects comprehensive preferences including script type, tone, target audience, style, video length, hook style, use o...
Assist writers with story planning, character development, plot structuring, chapter writing, timeline tracking, and consistency checking. Use this skill when working with creative writing projects organized in...
Build and run Gemini 2.5 Computer Use browser-control agents with Playwright. Use when a user wants to automate web browser tasks via the Gemini Computer Use model, needs an agent loop (screenshot → function_ca...
Spawn Codex subagents via background shell to offload context-heavy work. Use for: deep research (3+ searches), codebase exploration (8+ files), multi-step workflows, exploratory tasks, long-running operations,...
Query the OpenAI developer documentation via the OpenAI Docs MCP server using CLI (curl/jq). Use whenever a task involves the OpenAI API (Responses, Chat Completions, Realtime, etc.), OpenAI SDKs, ChatGPT Apps...
This skill should be used when the user requests to create professional business documents (proposals, business plans, or budgets) from templates. It provides PDF templates and a Python script for generating fi...
This skill should be used whenever users request personal assistance tasks such as schedule management, task tracking, reminder setting, habit monitoring, productivity advice, time management, or any query requ...
Use when building NestJS applications requiring modular architecture, dependency injection, or TypeScript backend development. Invoke for modules, controllers, services, DTOs, guards, interceptors, TypeORM/Pris...
Use when building Python 3.11+ applications requiring type safety, async programming, or production-grade patterns. Invoke for type hints, pytest, async/await, dataclasses, mypy configuration.
Use when building high-performance async Python APIs with FastAPI and Pydantic V2. Invoke for async SQLAlchemy, JWT authentication, WebSockets, OpenAPI documentation.
Use when investigating errors, analyzing stack traces, or finding root causes of unexpected behavior. Invoke for error investigation, troubleshooting, log analysis, root cause analysis.
Use when fine-tuning LLMs, training custom models, or optimizing model performance for specific tasks. Invoke for parameter-efficient methods, dataset preparation, or model adaptation.
Use when building cross-platform applications with Flutter 3+ and Dart. Invoke for widget development, Riverpod/Bloc state management, GoRouter navigation, platform-specific implementations, performance optimiz...
Use when building ML pipelines, orchestrating training workflows, automating model lifecycle, implementing feature stores, or managing experiment tracking systems.
Use when building MCP servers or clients that connect AI systems with external tools and data sources. Invoke for MCP protocol compliance, TypeScript/Python SDKs, resource providers, tool functions.
Use when building CLI tools, implementing argument parsing, or adding interactive prompts. Invoke for CLI design, argument parsing, interactive prompts, progress indicators, shell completions.
Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, or context augmentation.
Use when designing REST or GraphQL APIs, creating OpenAPI specifications, or planning API architecture. Invoke for resource modeling, versioning strategies, pagination patterns, error handling standards.
Use when designing prompts for LLMs, optimizing model performance, building evaluation frameworks, or implementing advanced prompting techniques like chain-of-thought, few-shot learning, or structured outputs.
Build llm-mux binary and run locally for development/debugging
Integrate Honcho memory and social cognition into existing Python or TypeScript codebases. Use when adding Honcho SDK, setting up peers, configuring sessions, or implementing the dialectic chat endpoint for AI...
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.