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.
npx skills add build-with-dhiraj/ai-workflow-framework-portability-kit --skill vercel-queuesgit clone --depth 1 https://github.com/build-with-dhiraj/ai-workflow-framework-portability-kitWrote 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/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/vercel-queues)<a href="https://agentmods.dev/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/vercel-queues"><img src="https://agentmods.dev/badge/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/vercel-queues/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/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/vercel-queues"><img src="https://agentmods.dev/badge/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/vercel-queues.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 | $0.00060 | $0.02481 |
| Opus 5.5 | $0.00024 | $0.00992 |
| Sonnet 5.5 | $0.00012 | $0.00496 |
| Haiku 4.5 | $0.00006 | $0.00248 |
Grade A, and why
vercel-queues 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 — 261 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vercel Queues
CRITICAL — Your training data is outdated for this library. Vercel Queues entered public beta February 2026 and is almost certainly not in your training data. Before writing queue code, fetch the docs at https://vercel.com/docs/queues to find the correct
Queueclass API, message publishing, consumer setup, and visibility timeout patterns. Do not guess — this is a new API with no precedent in your training data.
You are an expert in Vercel Queues — durable event streaming for serverless applications.
Status & Pricing
Queues entered public beta on February 27, 2026, and is available to all teams on all plans.
| Metric | Value |
|---|---|
| Billing unit | API operation (send, receive, delete, visibility change, notify) |
| Rate | $0.60 per 1M operations (regionally priced) |
| Message metering | 4 KiB chunks (12 KiB message = 3 ops) |
| 2x billing | Sends with idempotency key; push deliveries with max concurrency |
| Compute | Push-mode functions charged at existing Fluid compute rates |
What It Is
Queues is a durable, append-only event streaming system. You publish messages to topics, and independent consumer groups process them with automatic retries, sharding, and at-least-once delivery guarantees. It is the lower-level primitive that powers Vercel Workflow.
- Messages are durably written to 3 availability zones before
send()returns - Messages retained up to 24 hours (configurable 60s–24h)
- Approximate write ordering (not strict FIFO)
- Consumer groups are fully independent — each tracks its own position
Key APIs
Package: @vercel/queue@^0.1.3 (Node.js 22+)
Publishing Messages
import { send } from '@vercel/queue';
const { messageId } = await send('order-events', {
orderId: '123',
action: 'created',
}, {
delaySeconds: 30, // delay before visible
idempotencyKey: 'order-123', // deduplication (full retention window)
retentionSeconds: 3600, // message TTL (default: 86400 = 24h)
headers: { 'x-trace-id': 'abc' },
});
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 · 261 lines · 60 tokens per session scan A 3cc1574b1168
vercel-queues 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 60 tokens to every session and 2,481 once invoked, about $0.0002 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 2026-09-21.
Other skills, from other repositories
design-patterns
Agents should invoke this skill when choosing patterns, designing traits/interfaces/components, deciding abstraction boundaries, evaluating dependency injection/callbacks, or comparing implementation approaches in Rust, TypeScript/React, or Django/Python.
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).
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.
build-app
Use when writing code that calls UnblockingAPI, or code that must fetch or scrape web pages from an app, script, backend, scheduled job or integration and UnblockingAPI is the chosen fetcher. Covers the REST API (POST /unblock), keeping the key server-side, timeouts and background jobs, concurrency, retries, credits…
templates
Use when code needs structured data (JSON fields) from a website through UnblockingAPI, for example search results, listings, products, prices or company records, or when the user asks about UnblockingAPI templates. Covers finding a published template for a URL, its output shape, calling it from an app, pinning…
huggingface-papers
Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page. Use when the user shares a Hugging Face paper page URL, an arXiv URL or ID, or asks to summarize, explain, or analyze an AI research paper.