From laguagu-claude-code-nextjs-skills
Builds AI agents with OpenAI Agents SDK (Python): multi-agent handoffs, function tools, guardrails, sessions, streaming, tracing via `agents` package. Supports Azure OpenAI via LiteLLM.
npx claudepluginhub joshuarweaver/cascade-code-languages-misc-1 --plugin laguagu-claude-code-nextjs-skillsThis skill uses the workspace's default tool permissions.
Use this skill when developing AI agents using OpenAI Agents SDK (`openai-agents` package).
Guides Next.js Cache Components and Partial Prerendering (PPR) with cacheComponents enabled. Implements 'use cache', cacheLife(), cacheTag(), revalidateTag(), static/dynamic optimization, and cache debugging.
Guides building MCP servers enabling LLMs to interact with external services via tools. Covers best practices, TypeScript/Node (MCP SDK), Python (FastMCP).
Generates original PNG/PDF visual art via design philosophy manifestos for posters, graphics, and static designs on user request.
Use this skill when developing AI agents using OpenAI Agents SDK (openai-agents package).
pip install openai-agents
# OpenAI (direct)
OPENAI_API_KEY=sk-...
LLM_PROVIDER=openai
# Azure OpenAI (via LiteLLM)
LLM_PROVIDER=azure
AZURE_API_KEY=...
AZURE_API_BASE=https://your-resource.openai.azure.com
AZURE_API_VERSION=2024-12-01-preview
from agents import Agent, Runner
agent = Agent(
name="Assistant",
instructions="You are a helpful assistant.",
model="gpt-5.4", # or "gpt-5.4-mini", "gpt-5.4-nano"
)
# Synchronous
result = Runner.run_sync(agent, "Tell me a joke")
print(result.final_output)
# Asynchronous
result = await Runner.run(agent, "Tell me a joke")
| Pattern | Purpose |
|---|---|
| Basic Agent | Simple Q&A with instructions |
| Azure/LiteLLM | Azure OpenAI integration |
| AgentOutputSchema | Strict JSON validation with Pydantic |
| Function Tools | External actions (@function_tool) |
| Streaming | Real-time UI (Runner.run_streamed) |
| Handoffs | Specialized agents, delegation |
| Agents as Tools | Orchestration (agent.as_tool) |
| LLM as Judge | Iterative improvement loop |
| Guardrails | Input/output validation |
| Sessions | Automatic conversation history |
| Multi-Agent Pipeline | Multi-step workflows |
| Sandboxing | Isolated execution environment for agents |
| Subagents | Spawn specialized subordinate agents (Python + TS) |
| Observability | Built-in execution graph recording |
Model names and API details change frequently. When available, consult the OpenAI Developer Docs MCP server (openaiDeveloperDocs) before relying on the static references below.
Setup (Codex CLI):
codex mcp add openaiDeveloperDocs --url https://developers.openai.com/mcp
Or config (~/.codex/config.toml, VS Code .vscode/mcp.json, Cursor ~/.cursor/mcp.json):
[mcp_servers.openaiDeveloperDocs]
url = "https://developers.openai.com/mcp"
Key tools: mcp__openaiDeveloperDocs__search_openai_docs, fetch_openai_doc, list_api_endpoints, get_openapi_spec.
Rules: Cite fetched docs. Never speculate on field names, defaults, or current model IDs — fetch first. Keep quotes under 125 chars.
Fallback when MCP is unavailable: https://developers.openai.com/api/docs/llms.txt (plain-text index of all API docs; each entry has a .md twin at /api/docs/<slug>.md).
Offline/quick-lookup snippets. Verify model names and API signatures against the MCP or docs when accuracy matters.