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Found 73 Skills
Use this skill when building AI applications with OpenAI Agents SDK for JavaScript/TypeScript. The skill covers both text-based agents and realtime voice agents, including multi-agent workflows (handoffs), tools with Zod schemas, input/output guardrails, structured outputs, streaming, human-in-the-loop patterns, and framework integrations for Cloudflare Workers, Next.js, and React. It prevents 9+ common errors including Zod schema type errors, MCP tracing failures, infinite loops, tool call failures, and schema mismatches. The skill includes comprehensive templates for all agent types, error handling patterns, and debugging strategies. Keywords: OpenAI Agents SDK, @openai/agents, @openai/agents-realtime, openai agents javascript, openai agents typescript, text agents, voice agents, realtime agents, multi-agent workflows, agent handoffs, agent tools, zod schemas agents, structured outputs agents, agent streaming, agent guardrails, input guardrails, output guardrails, human-in-the-loop, cloudflare workers agents, nextjs openai agents, react openai agents, hono agents, agent debugging, Zod schema type error, MCP tracing failure, agent infinite loop, tool call failures, schema mismatch agents
Retrieve the latest Runway API reference from docs.dev.runwayml.com and use it as the authoritative source before any integration work
Use when the workflow works but needs polish, or as the final step in a diagnose → fix → refine cycle before shipping.
Use when starting a session, deciding which framework skill applies to the current task, or sequencing them across a feature. Maps the user's intent to one of the five framework skills (ai-driven-prd, init-claude-project, generate-dev-plan, declarative-design, execute-plan) and enforces the cross-skill operating behaviors. Triggers on "which skill should I use", "where do I start", "how do these skills fit together", "I have a PRD now what", "/using-agent-skills".
Integrated AI agent orchestration skill that combines plannotator, ralphmode, team or bmad execution, agent-browser verification, and agentation feedback loops, while maintaining a project-local `.jeo` ledger for planning, development, and QA. Use when the user wants an end-to-end multi-agent workflow with plan approval, implementation, UI review, cleanup, and durable task history. Triggers on: jeo, annotate, ui-review, multi-agent orchestration.
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.
Build, scaffold, refactor, and troubleshoot ChatGPT Apps SDK applications that combine an MCP server and widget UI. Use when Codex needs to design tools, register UI resources, wire the MCP Apps bridge or ChatGPT compatibility APIs, apply Apps SDK metadata or CSP or domain settings, or produce a docs-aligned project scaffold. Prefer a docs-first workflow by invoking the openai-docs skill or OpenAI developer docs MCP tools before generating code.
Ouroboros specification-first AI development — the complete system. Socratic interviewing crystallizes vague ideas into immutable specs (Ambiguity ≤ 0.2) before any code is written. Nine Minds agents (socratic-interviewer, ontologist, seed-architect, evaluator, contrarian, hacker, simplifier, researcher, architect) execute the Double Diamond. Ralph mode loops with state persistence until verification passes — the boulder never stops. Use when user says "ralph", "ooo", "ooo interview", "ooo seed", "ooo run", "ooo evaluate", "ooo evolve", "ooo unstuck", "ooo status", "ooo ralph", "stop prompting", "start specifying", "specification first", "socratic interview", "don't stop", "must complete", "keep going", or "the boulder never stops".
Curated documentation reference for developers building with Pinecone. Contains links to official docs organized by topic and data format references. Use when writing Pinecone code, looking up API parameters, or needing the correct format for vectors or records.
Official OpenAI CLI for interacting with the OpenAI REST API from the command line
Use when annotating code with structured metadata, tags, and markers for AI-assisted development workflows. Covers annotation formats, semantic tags, and integration with development tools.
Search and restore AI conversation context from git history