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Agent Skill · 研究检索

benchmark-agents(标杆 Agent 商)

benchmark-agents 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

benchmark-agents 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为执行命令;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 29,155。

总安装 3,103周安装 128GitHub Stars 156下载 1,014核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:benchmark-agents(标杆 Agent 商)
来源仓库:https://github.com/vercel-labs/vercel-plugin
仓库路径:skills/benchmark-agents
安装命令:
npx skills add https://github.com/vercel-labs/vercel-plugin --skill benchmark-agents
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/vercel-labs/vercel-plugin --skill benchmark-agents

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

benchmark-agents 用于启动远程 Claude Code 会话,验证技能注入并生成覆盖率报告。适合完整评估流程:设置→启动→监控→验证→修复→发布→重复。覆盖 evals 正确方法,强调人工创建目录、spawn WezTerm 会话和等待结果。

安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/benchmark-agents

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/vercel-labs/vercel-plugin --skill benchmark-ag…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

benchmark-agents 怎么安装?

在终端执行 npx skills add https://github.com/vercel-labs/vercel-plugin --skill benchmark-agents。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

benchmark-agents 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

benchmark-agents 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/vercel-labs/vercel-plugin --skill benchmark-agents 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

benchmark-agents 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

benchmark-agents 用的人多吗?

公开数据是累计安装 3,103 次,近一周安装 128 次,下载 1,014 次,GitHub 156 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

benchmark-agents 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
benchmark-agents

标杆 Agent 商 · 本页

3,103156执行命令未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Benchmark Agents — Advanced AI Systems

Launch real Claude Code sessions with the plugin installed, verify skill injection, monitor PostToolUse validation catches, and produce a coverage report. This skill covers the full eval loop: setup → launch → monitor → verify → fix → release → repeat.

How Evals Work (The Only Correct Method)

Evals are run by you, in this conversation, not by scripts. The process is:

  1. You create directories and install the plugin via Bash tool calls
  2. You spawn WezTerm panes with wezterm cli spawn — each pane runs an independent Claude Code interactive session
  3. You wait, then check debug logs and claim dirs to see what the plugin injected
  4. You inspect the generated source code for correctness
  5. You read conversation logs to find what the user had to correct
  6. You update skills/hooks, run /release, and spawn more evals

Never use claude --print, eval scripts, or Bun.spawn(["claude",...]). These do not work because:

  • Plugin hooks (PreToolUse, PostToolUse, UserPromptSubmit) only fire during interactive tool-calling sessions
  • --print mode generates text without executing tools — no files are created, no deps installed, no dev servers started
  • No session_id means dedup, profiler, and claim files don't work

The WezTerm interactive approach is the only method that exercises the plugin correctly. Every eval in our history (60+ sessions) used this approach.

DO NOT (Hard Rules)

These are absolute prohibitions. Violating any of them wastes the entire eval run:

  • DO NOT use claude --print or -p flag — hooks don't fire, no files created
  • DO NOT use --dangerously-skip-permissions — changes agent behavior
  • DO NOT create projects in /tmp/ — always use ~/dev/vercel-plugin-testing/
  • DO NOT manually create settings.local.json or wire hooks by hand — use npx add-plugin
  • DO NOT set CLAUDE_PLUGIN_ROOT manually — the plugin manages this
  • DO NOT use bash -c or bash -lc in WezTerm — always use /bin/zsh -ic
  • DO NOT use the full path to claude — use the x alias (it's configured in zsh)
  • DO NOT create custom debug.log files with stderr redirects — debug logs go to ~/.claude/debug/
  • DO NOT write eval runner scripts in TypeScript/JavaScript — do everything as Bash tool calls in the conversation
  • DO NOT try to git init or create package.json manually — npx add-plugin + the WezTerm session handle all scaffolding
  • DO NOT use uppercase letters in directory names — npm rejects them (e.g. T in timestamps breaks create-next-app)

Copy the exact commands below. Do not improvise.

Setup & Launch (Exact Commands)

Naming convention

Always append a timestamp to directory names so reruns don't overwrite old projects:

<slug>-<yyyymmdd>-<hhmm>

Example: tarot-card-deck-20260309-1227, interior-designer-20260309-1227

Generate the timestamp with: date +%Y%m%d-%H%M

1. Create test directory and install plugin

TS=$(date +%Y%m%d-%H%M)
SLUG="my-app-$TS"
mkdir -p ~/dev/vercel-plugin-testing/$SLUG
cd ~/dev/vercel-plugin-testing/$SLUG
npx add-plugin https://github.com/vercel/vercel-plugin -s project -y

2. Launch session via WezTerm

wezterm cli spawn --cwd /Users/johnlindquist/dev/vercel-plugin-testing/$SLUG -- /bin/zsh -ic \
  "unset CLAUDECODE; VERCEL_PLUGIN_LOG_LEVEL=debug x '<PROMPT>' --settings .claude/settings.json; exec zsh"

Key flags:

  • unset CLAUDECODE — prevents nested session detection error
  • VERCEL_PLUGIN_LOG_LEVEL=debug — enables hook debug output in ~/.claude/debug/
  • x — alias for claude CLI
  • --settings.claude/settings.json — loads project-level plugin settings

3. Find the debug log (wait ~25s for SessionStart hooks)

find ~/.claude/debug -name "*.txt" -mmin -2 -exec grep -l "$SLUG" {} +

4. Launch multiple sessions in parallel

Create dirs and install plugin in a loop, then spawn each WezTerm pane:

TS=$(date +%Y%m%d-%H%M)
cd ~/dev/vercel-plugin-testing
for name in tarot-deck interior-designer superhero-origin; do
  d="${name}-${TS}"
  mkdir -p "$d" && (cd "$d" && npx add-plugin https://github.com/vercel/vercel-plugin -s project -y)
done

# Then spawn each (these run in separate terminal panes)
wezterm cli spawn --cwd .../tarot-deck-$TS -- /bin/zsh -ic "unset CLAUDECODE; VERCEL_PLUGIN_LOG_LEVEL=debug x '...' --settings .claude/settings.json; exec zsh"
wezterm cli spawn --cwd .../interior-designer-$TS -- /bin/zsh -ic "unset CLAUDECODE; VERCEL_PLUGIN_LOG_LEVEL=debug x '...' --settings .claude/settings.json; exec zsh"
wezterm cli spawn --cwd .../superhero-origin-$TS -- /bin/zsh -ic "unset CLAUDECODE; VERCEL_PLUGIN_LOG_LEVEL=debug x '...' --settings .claude/settings.json; exec zsh"

Monitoring

Skill injection claims (the key metric)

TMPDIR=$(node -e "import {tmpdir} from 'os'; console.log(tmpdir())" --input-type=module)
CLAIMDIR="$TMPDIR/vercel-plugin-<session-id>-seen-skills.d"

# List all injected skills
ls "$CLAIMDIR"

# Count
ls "$CLAIMDIR" | wc -l

# Check specific skill
ls "$CLAIMDIR/workflow" && echo "YES" || echo "NO"

Hook firing

LOG=~/.claude/debug/<session-id>.txt

# SessionStart hooks
grep -c 'SessionStart.*success' "$LOG"

# PreToolUse calls and injections
grep -c 'executePreToolHooks' "$LOG"        # total calls
grep -c 'provided additionalContext' "$LOG"  # actual injections

# PostToolUse validation catches
grep 'VALIDATION' "$LOG" | head -10

# UserPromptSubmit
grep -c 'UserPromptSubmit.*success' "$LOG"

Quick status check for multiple sessions

TMPDIR=$(node -e "import {tmpdir} from 'os'; console.log(tmpdir())" --input-type=module 2>/dev/null)

for label_id in "slug1:SESSION_ID_1" "slug2:SESSION_ID_2" "slug3:SESSION_ID_3"; do
  label="${label_id%%:*}"
  id="${label_id##*:}"
  claimdir="$TMPDIR/vercel-plugin-$id-seen-skills.d"
  echo "=== $label ==="
  count=$(ls "$claimdir" 2>/dev/null | wc -l | tr -d ' ')
  claims=$(ls "$claimdir" 2>/dev/null | sort | tr '\n' ', ')
  echo "Skills ($count): $claims"
done

Verification — What to Check in Generated Code

After sessions build, verify these patterns in the generated projects:

Project structure

echo -n "src/: "; test -d "$base/src" && echo YES || echo NO          # Should be NO for WDK projects
echo -n "workflows/: "; test -d "$base/workflows" && echo YES || echo NO
echo -n "withWorkflow: "; grep -q "withWorkflow" "$base"/next.config.* && echo YES || echo NO
echo -n "components.json: "; test -f "$base/components.json" && echo YES || echo NO

Image generation model

# Should use gemini-3.1-flash-image-preview, NOT dall-e-3 or older gemini models
grep -rn "gemini.*image\|dall-e\|experimental_generateImage\|result\.files" "$base/workflows/" "$base/app/" 2>/dev/null | grep "\.ts"

Gateway vs direct provider

# Should use gateway() or plain "provider/model" strings, NOT openai("gpt-4o") directly
grep -rn "from.*@ai-sdk/openai\|openai(" "$base" 2>/dev/null | grep "\.ts" | grep -v node_modules
grep -rn "gateway(\|model:.*\"openai/" "$base" 2>/dev/null | grep "\.ts" | grep -v node_modules

AI Elements installed

find "$base" -path "*/ai-elements/*.tsx" 2>/dev/null | grep -v node_modules | wc -l

Workflow API usage

wf=$(find "$base" -name "*.ts" -path "*/workflow*" 2>/dev/null | grep -v node_modules | head -1)
head -5 "$wf"   # Should show: import { getWritable } from "workflow"

Prompt Design Rules

Describe products, not technologies. Let the plugin infer which skills to inject. This tests whether the plugin's pattern matching and prompt signals work from natural language.

DO:

  • "runs a multi-step creation pipeline that streams each phase"
  • "generates a portrait image"
  • "users can chat with an AI advisor"
  • "store all designs in a gallery"

DON'T:

  • "use Vercel Workflow DevKit with getWritable"
  • "use gateway('google/gemini-3.1-flash-image-preview')"
  • "install npx ai-elements"
  • "add withWorkflow to next.config.ts"

Always end prompts with:

"Link the project to my vercel-labs team so we can deploy it later. Skip any planning and just build it. Get the dev server running."

Phrases that trigger key skills (via promptSignals):

  • workflow: "multi-step pipeline", "streams progress", "streams each phase", "durable pipeline", "creation pipeline"
  • ai-sdk: Triggered by imports/install patterns (very broad)
  • shadcn: Triggered by create-next-app bash pattern
  • ai-elements: Triggered when ai-sdk is active + chat UI patterns

Common Issues Found in Evals (and Fixes Applied)

IssueCausePlugin Fix (version)
Workflow not triggered from natural languagepromptSignals too narrowBroadened phrases, lowered minScore 6→4 (v0.9.5)
Agent uses openai("gpt-4o") instead of gatewayAgent's training data defaults to openaiPostToolUse validate warns "your knowledge is outdated" (v0.9.9)
Agent uses dall-e-3 for imagesAgent doesn't know about gemini image genPostToolUse validate warns, capabilities table in ai-sdk (v0.9.7)
Agent uses experimental_generateImageOld APIPostToolUse validate warns, recommend generateText + result.files (v0.9.9)
Raw markdown rendering (**bold** visible)Agent skips AI ElementsMessageResponse documented as universal renderer (v0.9.2)
@/../../workflows/ broken importWorkflows outside @ alias rootCanonical structure docs: no src/ for WDK (v0.8.3)
withWorkflow missing from next.configAgent skipped setup stepMarked as "Required" in workflow skill (v0.8.1)
defineHook but no resume routeAgent didn't wire the 3-piece patternDocumented as 3 required pieces (v0.9.3)
generateObject() used (removed in v6)Agent's training dataPostToolUse validate catches as error (v0.9.3)
getWritable() in workflow scopeSandbox violationStrengthened warning in skill (v0.8.1)
Missing vercel link + vercel env pullNo OIDC credentialsAdded as "Required" setup step (v0.9.1)
getStepMetadata().retryCount undefined on first attemptWDK quirkDocumented: guard with ?? 0 (v0.9.1)
shadcn not installedNo trigger for scaffoldingAdded create-next-app bashPattern to shadcn (v0.8.0)
Skill cap too low (3)Only 3 skills injected per tool callRaised to 5 with 18KB budget (v0.8.0)

Agent-Browser Verification

After dev server starts, verify with agent-browser. Note: agents currently DO NOT self-verify despite the skill being injected. You must launch verification manually:

agent-browser open http://localhost:<port>
agent-browser wait --load networkidle
agent-browser screenshot
agent-browser snapshot -i

Coverage Report

Write results to .notes/COVERAGE.md with:

  1. Session index — slug, session ID, unique skills, dedup status
  2. Hook coverage matrix — which hooks fired in which sessions
  3. Skill injection table — which of the 43 skills triggered
  4. Code quality checks — gateway vs direct, image model, withWorkflow, AI Elements
  5. PostToolUse validation catches — outdated models, deprecated APIs
  6. Issues found — bugs, pattern gaps, new findings to feed back into skills

Release → Eval Loop

The standard improvement cycle:

  1. Run evals — launch 3 sessions with natural language prompts
  2. Check results — skill claims, project structure, code quality
  3. Identify gaps — what skills didn't trigger, what patterns are wrong
  4. Read conversation logs — find user follow-up corrections
  5. Fix skills — update SKILL.md content, patterns, validate rules
  6. Run gates — bun run typecheck && bun test && bun run validate
  7. Release — bump version, bun run build, commit, push
  8. Repeat — launch 3 more evals to verify fixes

Scenario Table

#SlugPrompt SummaryExpected Skills
01doc-qa-agentPDF Q&A with embeddings, citations, multi-step reasoningai-sdk, nextjs, vercel-storage, ai-elements
02customer-support-agentDurable support agent, escalation, confidence trackingai-sdk, workflow, nextjs, ai-elements
03deploy-monitorUptime monitoring, AI incident responder, durable investigationworkflow, cron-jobs, observability, ai-sdk
04multi-model-routerSide-by-side model comparison, parallel streaming, cost trackingai-gateway, ai-sdk, nextjs, ai-elements
05slack-pr-reviewerMulti-platform chat bot, PR review, threaded conversationschat-sdk, ai-sdk, nextjs
06content-pipelineDurable multi-step content production with image generationworkflow, ai-sdk, satori, nextjs
07feature-rolloutFeature flags, A/B testing, AI experiment analysisvercel-flags, ai-sdk, nextjs
08event-driven-crmEvent-driven CRM, churn prediction, re-engagement emailsvercel-queues, workflow, ai-sdk, email
09code-sandbox-tutorAI coding tutor with sandbox execution, auto-fixvercel-sandbox, ai-sdk, nextjs, ai-elements
10multi-agent-researchParallel sub-agents, durable orchestration, streaming synthesisworkflow, ai-sdk, ai-elements, nextjs
11discord-game-masterRPG bot, persistent game state, scene illustration generationchat-sdk, ai-sdk, vercel-storage, nextjs
12compliance-auditorScheduled AI audits, durable approval workflow, deploy blockingworkflow, cron-jobs, ai-sdk, vercel-firewall

Complexity Tiers

Tier 1 — Core AI (30-45 min, --quick)

Scenarios 01, 04, 09 — AI SDK, Gateway, Sandbox, AI Elements without durable workflows.

Tier 2 — Durable Agents (45-60 min)

Scenarios 02, 03, 06, 10 — Workflow DevKit, multi-step durability, agent orchestration.

Tier 3 — Platform Integration (45-60 min)

Scenarios 05, 07, 08, 11, 12 — Chat SDK, Queues, Flags, Firewall, cross-platform messaging.

Full Suite

All 12 scenarios, ~3-4 hours.

Cleanup

rm -rf ~/dev/vercel-plugin-testing

平台分布

Codex

35.36%
按下载量换算359

Claude

29.98%
按下载量换算304

Cursor

19.89%
按下载量换算202

Gemini CLI

8.99%
按下载量换算91

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

可疑

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/vercel-labs/vercel-plugin --skill benchmark-agents 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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