dspy-framework
DSPy declarative framework for automatic prompt optimization treating prompts as code with systematic evaluation and compilers
DSPy declarative framework for automatic prompt optimization treating prompts as code with systematic evaluation and compilers
FOUNDATIONAL SKILL - Analyze every prompt for underlying motivation using Golden Circle (WHY->HOW->WHAT). Runs BEFORE any other skill. No action without understanding WHY.
DigitalOcean Gradient AI agentic cloud and AI platform for building, training, and deploying AI agents on GPU infrastructure with foundation models, knowledge bases, and agent routes. Use when planning or operating AI agents on DigitalOcean.
Semantic memory recall using FTS5 and Chroma. Use PROACTIVELY whenever the user asks about past events, themes, conversations, or when historical context would enrich a response. Trigger words: remember, when did we, what did we talk about, last time, before, previously, that conversation, that time, history, past, recall.
Opencode OAuth authentication flows for Claude Code, Gemini/Antigravity, and Codex. Use when setting up multi-provider authentication, configuring Opencode with various AI services, or troubleshooting auth issues.
Consult external LLMs (Gemini, OpenAI/Codex, Qwen) for second opinions, alternative plans, independent reviews, or delegated tasks. Use when a user asks for another model's perspective, wants to compare answers, or requests delegating a subtask to Gemini/Codex/Qwen.
Generate stories about institutional outcasts given impossible mandates with minimal resources. Use when you want team dynamics in hostile institutions, David vs. Goliath within organizations, or narrative tension from constraint-driven creativity.
Develop AI agents, tools, and workflows with Mastra v1 Beta and Hono servers. This skill should be used when creating Mastra agents, defining tools with Zod schemas, building workflows with step data flow, setting up Hono API servers with Mastra adapters, or implementing agent networks. Keywords: mastra, hono, agent, tool, workflow, AI, LLM, typescript, API, MCP.
Integrate Microsoft Teams app with You.com MCP server using @youdotcom-oss/teams-anthropic package. Use when developer mentions Teams.ai, Microsoft Teams, or integrating Teams with Anthropic Claude and MCP.
OpenRouter unified AI API - Access 200+ LLMs through single interface with intelligent routing, streaming, cost optimization, and model fallbacks
Automatically remember and apply skillset configuration when first running a project. Use when initializing projects in .skills-template or any project requiring consistent AI agent setup. Handles CLAUDE.md generation, skill loading, and environment persistence.
Complete fal.ai model selection system. PROACTIVELY activate for: (1) Choosing image generation models (FLUX, SDXL), (2) Choosing video models (Kling, Sora, LTX), (3) Choosing audio models (Whisper, ElevenLabs), (4) Model quality vs speed comparison, (5) Cost optimization by model tier, (6) 3D generation models, (7) Model-specific parameters, (8) Development vs production model selection. Provides: Model comparison tables, decision trees, pricing tiers, performance benchmarks. Ensures optimal model selection for quality, speed, and cost.
Orchestrates multi-model LLM consensus through a three-phase deliberation protocol. Use when you need collaborative AI review, multi-model problem-solving, code review from multiple perspectives, or consensus-based decision making. Coordinates OpenAI Codex, Google Gemini, and Claude CLIs for opinion collection, peer review, and chairman synthesis.
Local LLM operations with Ollama on Apple Silicon, including setup, model pulls, chat launchers, benchmarks, and diagnostics.
Debug Samara session management, message batching, and task routing. Use when messages are scrambled, batching seems wrong, group chats behave oddly, or concurrent tasks interfere with each other. Trigger words: session, batch, group chat, concurrent, scrambled, task routing.
LangChain LLM application framework with chains, agents, RAG, and memory for building AI-powered applications
Design and implement comprehensive evaluation systems for AI agents. Use when building evals for coding agents, conversational agents, research agents, or computer-use agents. Covers grader types, benchmarks, 8-step roadmap, and production integration.
Complete fal.ai API reference system. PROACTIVELY activate for: (1) @fal-ai/client JavaScript setup, (2) fal_client Python setup, (3) fal.subscribe/run/stream methods, (4) Queue management (submit/status/result), (5) Webhook configuration, (6) File upload to fal.media, (7) REST API endpoints, (8) Real-time WebSocket connections, (9) Error handling patterns. Provides: Client configuration, method signatures, queue workflow, webhook payloads, common parameters. Ensures correct API usage with proper authentication and error handling.