
ai
AI SDK for Python (the ai package). Use for Python model calls (LLMs, image, video, speech, embedding, transcription, reranking, or evaluation), agents, model interaction tests, tool calling, subagents, approvals, durable execution, telemetry, AI SDK UI backends, and custom providers.
- Installs
- 169
- Last 24h
- —
- Trending rank
- —
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/vercel-labs/ai-python --skill ai- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads ai automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
AI SDK for Python
Package: ai. Requires Python 3.12+. Install with uv add ai.
Unprefixed model IDs use AI Gateway and AI_GATEWAY_API_KEY. Direct providers
use provider:model, their API key, and an extra:
uv add "ai[openai]" # OPENAI_API_KEY, ai.get_model("openai:gpt-5")
uv add "ai[anthropic]" # ANTHROPIC_API_KEY, ai.get_model("anthropic:claude-sonnet-4")
Basic use
Use ai.stream for one model call without Python tool execution:
import ai
model = ai.get_model("anthropic/claude-sonnet-4")
messages = [
ai.system_message("Be concise."),
ai.user_message("Write a haiku about rain."),
]
async with ai.stream(model, messages) as stream:
async for event in stream:
if isinstance(event, ai.events.TextDelta):
print(event.chunk, end="", flush=True)
answer = stream.output
message = stream.message
Use ai.Agent for a loop that executes Python tools and manages history:
@ai.tool
async def get_weather(city: str) -> str:
"""Get the weather for a city."""
return "Sunny"
agent = ai.Agent(tools=[get_weather])
async with agent.run(model, messages) as run:
async for event in run:
if isinstance(event, ai.events.TextDelta):
print(event.chunk, end="", flush=True)
answer = run.output
history = run.messages
These examples are sufficient for basic model calls, messages, tools, and agents.
Advanced work
For an advanced task, fetch its page under https://ai-python.dev/docs/ and
read the listed local notes before writing code.
| Task | Page | Local notes |
|---|---|---|
| Provider clients, options, discovery | basics/providers.md | — |
| Structured output, complex streams | basics/streaming.md | — |
| Buffered language-model calls | basics/streaming.md | — |
| Images, video, speech, embeddings, transcription, reranking, evaluation | basics/model-operations.md | — |
| Events and serialization | basics/messages-and-events.md | — |
| Advanced tools, streaming, aggregation | basics/tools.md | streaming-tools.md |
| Advanced agent behavior | basics/agents.md | — |
| Deterministic model and agent tests | basics/testing.md | — |
| Subagents and multi-agent | basics/subagents-and-multi-agent.md | streaming-tools.md |
| Custom agent loops | basics/custom-loops.md | custom-loops.md |
| Approvals and hooks | basics/human-in-the-loop.md | — |
| Serverless resume | basics/human-in-the-loop.md | serverless.md |
| Durable execution | basics/durable-execution.md | durable.md |
| Telemetry and tracing | basics/telemetry.md | — |
| AI SDK UI backends | basics/ai-sdk-ui.md | ui.md |
| Custom providers | basics/providers.md | custom-provider.md |
For exact APIs, use reference.md and the relevant reference/*.md page.
FAQ
What does the ai skill do?
AI SDK for Python (the ai package). Use for Python model calls (LLMs, image, video, speech, embedding, transcription, reranking, or evaluation), agents, model interaction tests, tool calling, subagents, approvals, durable execution, telemetry, AI SDK UI backends, and custom providers.
How do I install ai?
Run npx skills add https://github.com/vercel-labs/ai-python --skill ai in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/ai/ (personal) or .claude/skills/ai/ (project).
Which AI agents can use ai?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is ai safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains ai?
It is published by vercel-labs in vercel-labs/ai-python, and it is an official skill from the maker of the technology it covers. It has 169 recorded installs.
Popular ai & agents skills
google-agents-cli-publish
google/agents-cli
This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini", "publish to Gemini" (formerly Gemini Enterprise), or needs guidance on the agents-cli publish gemini command. Also use when the user wants to "manage agents in Agent Registry", "list/update/delete registered agents", or "register an MCP server". Covers ADK vs A2A registration modes, programmatic and interactive usage, flag reference, auto-detection from deployment metadata, Agent Registry fleet management, and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment (use google-agents-cli-deploy).
google-agents-cli-eval
google/agents-cli
This skill should be used when the user wants to "run an evaluation", "evaluate my agent", "evaluate my ADK agent", "write an eval dataset", "analyze eval failures", "compare eval results", "optimize agent", or needs guidance on the Gemini platform eval methodology and the Quality Flywheel. Covers eval metrics, dataset schema, LLM-as-judge scoring, and common failure causes. Applies to any agents-cli project, whatever framework the agent is written in. Do NOT use for agent API code patterns (ADK: use google-agents-cli-adk-code), deployment (use google-agents-cli-deploy), or project scaffolding (use google-agents-cli-scaffold).
skill-creator
anthropics/skills
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
codex-pet
prime-skills/runcomfy-agent-skills
Codex Pet generator on RunComfy. Build a Codex-compatible Codex Pet spritesheet.webp + pet.json from a single reference image, drop it into ${CODEXHOME:-$HOME/.codex}/pets/ / and Codex picks it up as a custom Codex Pet next to the 8 built-ins. This skill produces the exact Codex Pet atlas Codex expects (1536x1872 PNG/WebP, 8 cols x 9 rows, 192x208 cells, 9 animation states — idle, running-right, running-left, waving, jumping, failed, waiting, running, review). Calls OpenAI GPT Image 2 edit ONCE via the local RunComfy CLI as runcomfy run openai/gpt-image-2/edit to produce a canonical Codex Pet pose, then assembles all 9 animation rows programmatically with ImageMagick micro-transforms — no Codex Pro, no $imagegen, no OPENAIAPIKEY required, only RUNCOMFYTOKEN. Triggers on "codex pet", "create codex pet", "make codex pet", "hatch codex pet", "/hatch image", "desktop pet codex", "codex pets", "spritesheet.webp", or any explicit ask to build a custom pet for OpenAI Codex.
full-output-enforcement
leonxlnx/taste-skill
Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.
executing-plans
obra/superpowers
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available