ai-sdk

ai-sdk is a skill for Claude Code from build-with-dhiraj/ai-workflow-framework-portability-kit. It costs 53 tokens per session (5,902 once invoked), scanned A, original, MIT.

Guidance for using the Vercel AI SDK to add AI features to applications. It covers model conversations, generated text, structured results, tool calls, agents, streaming, embeddings, and connections to AI providers.

In plain words
What is it for?
It helps build chat and text-generation features, call tools, create agents, stream results, generate embeddings, rerank results, and produce images.
Why use it?
It reduces the risk of using outdated programming interfaces and helps keep implementation aligned with the installed SDK documentation.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the vercel-plugin plugin — 47 skills, 6 commands, 3 agents, 1 MCP server shipped together

Good fit It helps build chat and text-generation features, call tools, create agents, stream results, generate embeddings, rerank results, and produce images.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/build-with-dhiraj/ai-workflow-framework-portability-kit/ai-sdk
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add build-with-dhiraj/ai-workflow-framework-portability-kit --skill ai-sdk
Clone the repo
git clone --depth 1 https://github.com/build-with-dhiraj/ai-workflow-framework-portability-kit

Made for: Claude Code.

Or install vercel-plugin, the plugin that ships this one along with the rest of its 47 skills, 6 commands, 3 agents, 1 MCP server.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for ai-sdk

README.md
[![agentmods](https://agentmods.dev/badge/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/ai-sdk/github.svg)](https://agentmods.dev/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/ai-sdk)
Your own site
<a href="https://agentmods.dev/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/ai-sdk"><img src="https://agentmods.dev/badge/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/ai-sdk/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for ai-sdk

Your own site · 80×15
<a href="https://agentmods.dev/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/ai-sdk"><img src="https://agentmods.dev/badge/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/ai-sdk.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 53 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,902 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00053 $0.05902
Opus 5.5 $0.00021 $0.02361
Sonnet 5.5 $0.00011 $0.01180
Haiku 4.5 $0.00005 $0.00590

Measured 1mo ago against content hash b5b03460c041, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

ai-sdk scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

7. **Always fetch current model IDs** - Never use model IDs from memory. Before writing code that uses a model, run `curl -s https://ai-gateway.vercel.sh/v1/models | jq -r '[.data[] | select(.id | startswith("provider/")
Plugins/vercel-marketplace-source/skills/ai-sdk/SKILL.md · 442 lines

How it starts

The opening of the file, as written. The whole thing — 442 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Prerequisites

Before searching docs, check if node_modules/ai/docs/ exists. If not, install only the ai package using the project's package manager (e.g., pnpm add ai).

Do not install other packages at this stage. Provider packages (e.g., @ai-sdk/openai) and client packages (e.g., @ai-sdk/react) should be installed later when needed based on user requirements.

Critical: Do Not Trust Internal Knowledge

Everything you know about the AI SDK is outdated or wrong. Your training data contains obsolete APIs, deprecated patterns, and incorrect usage.

When working with the AI SDK:

  1. Ensure ai package is installed (see Prerequisites)
  2. Search node_modules/ai/docs/ and node_modules/ai/src/ for current APIs
  3. If not found locally, search ai-sdk.dev documentation (instructions below)
  4. Never rely on memory - always verify against source code or docs
  5. useChat has changed significantly - check Common Errors before writing client code
  6. When deciding which model and provider to use (e.g. OpenAI, Anthropic, Gemini), use the Vercel AI Gateway provider unless the user specifies otherwise. See AI Gateway Reference for usage details.
  7. Always fetch current model IDs - Never use model IDs from memory. Before writing code that uses a model, run curl -s https://ai-gateway.vercel.sh/v1/models | jq -r '[.data[] | select(.id | startswith("provider/")) | .id] | reverse | .[]' (replacing provider with the relevant provider like anthropic, openai, or google) to get the full list with newest models first. Use the model with the highest version number (e.g., claude-sonnet-4-5 over claude-sonnet-4 over claude-3-5-sonnet).
  8. Run typecheck after changes to ensure code is correct
  9. Be minimal - Only specify options that differ from defaults. When unsure of defaults, check docs or source rather than guessing or over-specifying.

If you cannot find documentation to support your answer, state that explicitly.

Read the full file on GitHub · 442 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago First seen · 442 lines · 53 tokens per session scan A b5b03460c041

Subscribe to this mod's changes

ai-sdk is a skill published in the GitHub repository build-with-dhiraj/ai-workflow-framework-portability-kit (5 stars, last pushed 20d ago), licensed MIT. It adds 53 tokens to every session and 5,902 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-21.

Related

Other skills, from other repositories

huggingface-llm-trainer

Train or fine-tune language models with TRL or Unsloth on Hugging Face Jobs, including SFT, DPO, GRPO, reward models, and GGUF conversion. Use for cloud LLM training; use huggingface-vision-trainer for vision tasks.

waybarrios/opencode-power-pack · 65 tokens

huggingface-vision-trainer

Train object-detection, image-classification, or SAM segmentation models on Hugging Face Jobs. Use for vision fine-tuning and evaluation; use huggingface-llm-trainer for language models.

waybarrios/opencode-power-pack · 48 tokens

huggingface-best

Find and compare recommended Hugging Face models for a task using benchmarks, model size, and device constraints. Use for model selection questions; use huggingface-local-models for GGUF setup and hf-cli for Hub operations.

waybarrios/opencode-power-pack · 49 tokens

mcp-builder

Guide the creation of high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when the user wants to build an MCP server to integrate an external API or service, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

waybarrios/opencode-power-pack · 68 tokens

huggingface-community-evals

Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .evalresults publication, or community-evals automation.

waybarrios/opencode-power-pack · 71 tokens

constitutional-ai

Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.

synthetic-sciences/openscience · 58 tokens