generative-framework
Conversation-driven specification and execution of healthcare data generation at scale
llm-application-dev-prompt-optimize
You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques including constitutional AI, chain-of-thought reasoning, and model-specific optimizati
elevenlabs-tts
This skill converts text to high-quality audio files using ElevenLabs API. Use this skill when users request text-to-speech generation, audio narration, or voice synthesis with customizable voice parameters (stability, similarity boost) and voice presets (rachel, adam, bella, elli, josh, arnold, ava).
pufferlib
This skill should be used when working with reinforcement learning tasks including high-performance RL training, custom environment development, vectorized parallel simulation, multi-agent systems, or integration with existing RL environments (Gymnasium, PettingZoo, Atari, Procgen, etc.). Use this skill for implementing PPO training, creating PufferEnv environments, optimizing RL performance, or developing policies with CNNs/LSTMs.
agent-development
This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
voice-interface-builder
Expert in building voice interfaces, speech recognition, and text-to-speech systems
agent-contracts-app-builder
Build a production-minded AI agent using agent-contracts (contracts, slices, validation, graph, runtime, CLI tooling).
python-backend
Usa esta skill para el desarrollo del backend en Python de ObsidianRAG, incluyendo FastAPI, estructura del proyecto, dependencias y logging.
context-manager
Manages permanent memory storage for decisions, blockers, context, preferences, and procedures. Use when user says "remember", "save this decision", "what did we decide", "recall", "search memories", "any blockers", or when making important architectural decisions. Provides SDAM compensation through external memory.
using-braintrust
Enables AI agents to use Braintrust for LLM evaluation, logging, and observability. Includes scripts for querying logs with SQL, running evals, and logging data.
langchain-architecture
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
llm-application-dev-ai-assistant
You are an AI assistant development expert specializing in creating intelligent conversational interfaces, chatbots, and AI-powered applications. Design comprehensive AI assistant solutions with natur
character-naming
Break LLM name defaults with external entropy. Use when character names cluster around statistical medians (Chen, Patel, Maya, Marcus), when cast has collision risks, or when fantasy cultures need phonologically consistent naming.
session-startup
Session initialization sequence for community-patterns development. Use at the start of every Claude Code session. Checks for upstream updates, loads workspace configuration, and ensures dev servers are running.
dev-swarm-create-update-agent-skill
Create or update agent skills following the agent skill specification. Use when user asks to create a new skill or update an existing skill.
unwinding-codebase
Use after unwind:start to orchestrate layer-by-layer analysis using specialist subagents
rag-implementer
Implement retrieval-augmented generation systems. Use when building knowledge-intensive applications, document search, Q&A systems, or need to ground LLM responses in external data. Covers embedding strategy, vector stores, retrieval pipelines, and evaluation.
model-context-protocol
Model Context Protocol (MCP) - Open standard for connecting AI applications to external data sources, tools, and systems. Use for building MCP servers (tools, resources, prompts), clients, understanding protocol architecture, and implementing AI integrations.
stable-baselines3
Use this skill for reinforcement learning tasks including training RL agents (PPO, SAC, DQN, TD3, DDPG, A2C, etc.), creating custom Gym environments, implementing callbacks for monitoring and control, using vectorized environments for parallel training, and integrating with deep RL workflows. This skill should be used when users request RL algorithm implementation, agent training, environment design, or RL experimentation.
context-saver
Save session context to disk for seamless continuation in new chat sessions. This skill should be used when the user asks to save context, preserve work state, checkpoint progress, or prepare for session handoff. Triggers on "save context", "checkpoint", "save progress", "preserve state", or when explicitly asked to create a context file for later resumption. Optimizes for correctness, completeness, minimal size, and trajectory preservation.
