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.
git clone --depth 1 https://github.com/nedcodes-ok/cursorrules-collectionWrote 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.
[](https://agentmods.dev/rules/nedcodes-ok/cursorrules-collection/vercel-ai-sdk)<a href="https://agentmods.dev/rules/nedcodes-ok/cursorrules-collection/vercel-ai-sdk"><img src="https://agentmods.dev/badge/rules/nedcodes-ok/cursorrules-collection/vercel-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.
<a href="https://agentmods.dev/rules/nedcodes-ok/cursorrules-collection/vercel-ai-sdk"><img src="https://agentmods.dev/badge/rules/nedcodes-ok/cursorrules-collection/vercel-ai-sdk.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00654 | $0.00654 |
| Opus 5.5 | $0.00262 | $0.00262 |
| Sonnet 5.5 | $0.00131 | $0.00131 |
| Haiku 4.5 | $0.00065 | $0.00065 |
Grade A, and why
vercel-ai-sdk scanned grade A with 0 findings 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vercel AI SDK Cursor Rules
You are an expert in the Vercel AI SDK (ai package). Follow these rules:
Core Patterns
- Use
generateTextfor single completions,streamTextfor streaming responses - Use
generateObjectwith Zod schemas for structured output, never parse JSON manually - Prefer
streamObjectwhen showing structured data progressively - Always specify the model with provider prefix:
openai('gpt-4o'),anthropic('claude-sonnet-4-20250514') - Use the unified provider interface, not provider-specific SDKs directly
Streaming
- Use
useChathook for chat UIs,useCompletionfor single-turn completions - Always handle the
errorstate from hooks, not justisLoading - Set
maxStepsonuseChatwhen using multi-step tool calls - Use
onFinishcallback for logging/persistence, not useEffect watching messages - Stream responses with
toDataStreamResponse()in API routes, not manual SSE
Tool Calling
- Define tools with
tool()helper including Zod parameter schemas and execute functions - Keep tool descriptions clear and specific, they are the prompt for the model
- Use
maxStepsto allow multi-step tool use (model calls tool, gets result, continues) - Handle tool call errors gracefully with try/catch inside execute functions
- Return structured data from tools, not formatted strings
Structured Output
- Always use Zod schemas with
generateObject/streamObject - Set
mode: 'json'for models that support native JSON mode - Use
schemaDescriptionto help the model understand the output shape - Prefer enums and literals over free-form strings in schemas
- Validate output even with structured generation, models can still produce edge cases
API Routes
- Use
streamText+toDataStreamResponse()in Next.js route handlers - Pass
messagesfrom the request body, validate array is non-empty - Set appropriate
maxTokensto control costs, don't rely on model defaults - Use
systemparameter for system prompts, not as the first message - Handle rate limits and API errors with proper HTTP status codes
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.
- 1mo ago First seen · 58 lines · 654 tokens per session scan A 6b969dee361a
vercel-ai-sdk is a cursor rule published in the GitHub repository nedcodes-ok/cursorrules-collection (38 stars, last pushed 7mo ago), licensed MIT. It adds 654 tokens to every session, about $0.0026 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other cursor rules, from other repositories
next-type-llm
Cursor rules for Next.js development with Type LLM integration.
llm-integration-rules
A set of rules for connecting an application to a large language model, including Python wrappers, API endpoints, asynchronous calls, retries, and key management.
cursorrules
You are building an AI/ML project with Python. The project uses PyTorch for model training, handles data pipelines with proper validation, tracks experiments systematically, and follows production ML engineering practices. Code is type-hinted, tested, and reproducible.
55-data-model-versioning
Dataset versioning, model checkpoint management, and training reproducibility rules.
50-rag-system
Retrieval-augmented generation rules.
improve-cursorrules-efficiency
AI Query Efficiency & Auto-Optimization.