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benchmark-agents标杆 Agent 商

Agent Skill

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。

总安装

4,584

周安装

191

GitHub Stars

156

下载量

1,528
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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/vercel-plugin
仓库路径:skills/benchmark-agents
安装命令:
npx skills add https://github.com/vercel/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/vercel-plugin --skill benchmark-agents

简介

benchmark-agents 用于查找、检索和筛选相关信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词、任务场景或来源线索快速定位候选结果。
  • 通过 npx skills add 命令从指定仓库安装,需结合原始 README 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。
  • 当前无底部简介,原始 SKILL.md 摘录为空,需进一步查阅源码了解细节。

SKILL.md

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 gatesbun 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

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

33.61%
按下载量换算514

Claude

29.28%
按下载量换算447

Cursor

18.88%
按下载量换算288

Gemini CLI

9.74%
按下载量换算149

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/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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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