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Found 8 Skills
Build AI agents with Strands Agents SDK. Use when developing model-agnostic agents, implementing ReAct patterns, creating multi-agent systems, or building production agents on AWS. Triggers on Strands, Strands SDK, model-agnostic agent, ReAct agent.
Build applications with the Letta API — a model-agnostic, stateful API for building persistent agents with memory and long-term learning. Covers SDK patterns for Python and TypeScript. Includes 24 working code examples.
Create, optimize, update, and validate AGENTS.md files with maximum token efficiency. Use when the user asks to (1) create new AGENTS.md files for any repository, (2) optimize/condense existing AGENTS.md to reduce token count, (3) update/refresh AGENTS.md to sync with codebase changes, (4) validate AGENTS.md quality and completeness, or (5) improve AGENTS.md files to be more effective for AI agents. Always generates token-efficient, condensed output focused on actionable commands and patterns while maintaining model-agnostic language.
Use this skill to turn a raw user request into a structured, model-agnostic task brief before execution, and — when the brief survives confirmation — to be the sole entry point that creates a task directory at `.agents/tasks/<task-id>/`. Invoke whenever the request is complex, multi-step, cross-domain, ambiguous, or will be handed off to another model or agent. Also trigger when the user says things like 'help me figure out what I need', 'I'm not sure how to ask this', 'I want to do X but I don't know where to start', 'take this and make it clearer', or when the task mixes multiple goals or domains. Do NOT trigger for simple one-line requests with clear intent (e.g., 'fix the typo on line 42', 'rename this variable').
Turn approved storyboard logic, beat sheets, or prompt plans into provider-ready short-form video requests. Use this when the segment structure is already known and you need a model-agnostic request architecture that can later map cleanly into Seedance or other video generators.
Scaffold a minimal local LangChain agent in Python by following the official quickstart. Use when the user wants to quickly build or try a LangChain agent locally.
Craft high-quality natural-language image prompts for any modern text-to-image or image-edit model that accepts flowing English. Trigger when the user wants help writing, rewriting, improving, or translating an English natural-language image prompt — including "write me an image prompt", "improve this image prompt", "describe this scene for an image model", or "convert these tags into a natural language prompt". Do NOT trigger for requests that are purely about dispatching to an image API, choosing samplers/schedulers, picking LoRAs, or setting up ControlNet — those belong to a runtime skill.
Scaffold a minimal local LangChain agent in TypeScript by following the official quickstart. Use when the user wants to quickly build or try a LangChain agent locally.