vercel/next-evals-oss is a set of evaluations that tests how well AI coding agents complete tasks in Next.js applications. Each evaluation gives an agent a small app in an isolated sandbox and checks the resulting code with assertions that are hidden from the agent. The catalogue entries provide instructions and a skill for running or developing these evaluations.
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 vercel/next-evals-oss --skill release-eval-reportgit clone --depth 1 https://github.com/vercel/next-evals-ossWrote 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/vercel/next-evals-oss/release-eval-report)<a href="https://agentmods.dev/skills/vercel/next-evals-oss/release-eval-report"><img src="https://agentmods.dev/badge/skills/vercel/next-evals-oss/release-eval-report/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/vercel/next-evals-oss/release-eval-report"><img src="https://agentmods.dev/badge/skills/vercel/next-evals-oss/release-eval-report.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 | $0.00047 | $0.01266 |
| Opus 5.5 | $0.00019 | $0.00506 |
| Sonnet 5.5 | $0.00009 | $0.00253 |
| Haiku 4.5 | $0.00005 | $0.00127 |
Grade A, and why
release-eval-report 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 8d 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Release eval report
Use this skill only for a validated automated release-eval event. Produce one short Slack root message and exactly three detailed thread replies. The report should help the team decide which evals are saturated enough to become regression checks and which evals need auditing.
Separate the root and each reply with this exact line:
<!-- RELEASE_EVAL_THREAD_REPLY -->
The renderer removes the separators and posts the four parts as one root message and three replies. Never use the separator for any other purpose.
Root message
Match the established reports in #next-bots: a short framing paragraph,
followed by a few named findings written in compact prose.
- Use only
Next.js <version>as the title. - In the opening paragraph, identify release, backport, or canary, matrix size, and the Base and AGENTS.md totals.
- Organize the findings around the questions the team is answering:
Saturation,Harder tasks,Potential eval problems, andComparison. Saturationshould quantify tasks above 90% in both variants, identify the strongest regression-suite candidates, and say whether the strongest agents are already saturated.Harder tasksshould summarize the tasks below 90%, say whether failures cluster among weaker agents, and call out exceptions where a high-scoring agent failed.Potential eval problemsshould flag extreme results such as 0% tasks or surprising confirmed failures. Describe them as audit candidates unless the report data proves a cause.Comparisonshould summarize changed tasks, changed agent totals, and agent version changes only when a comparable prior run exists. On the first run, say that it establishes the controlled baseline and omit deltas.- Do not include execution links, pull requests, artifacts, metric methodology, or automation commentary.
- Do not use tables. Keep the root under 2,500 characters.
Use this shape:
**Next.js <version>**
This <release|backport|canary> measured <task count> tasks across <agent count> agents. Without AGENTS.md, <passed>/<total> passed (<percent>%); with AGENTS.md, <passed>/<total> passed (<percent>%).
**Saturation**
<count and interpretation of tasks above 90%, plus top-agent saturation>
**Harder tasks**
<count and interpretation of tasks below 90%, failure clusters, and notable exceptions>
**Potential eval problems**
<extreme or surprising results that warrant auditing, or `None`>
**Comparison**
<changes from the previous comparable run, or first-run baseline note>
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.
- 8d ago First seen · 136 lines · 47 tokens per session scan A 85ade8bd533b
release-eval-report is a skill published in the GitHub repository vercel/next-evals-oss (327 stars, last pushed yesterday), licensed MIT. It adds 47 tokens to every session and 1,266 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 2026-10-15.
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