Agent skill

Agent Benchmark

by vibeeval in vibeeval/vibecosystem

Framework for measuring and tracking agent response quality over time.

MITAuto-check passedAgent Workflows

Install Agent Benchmark

skills CLI
$ npx skills add vibeeval/vibecosystem --skill agent-benchmark -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install vibeeval/vibecosystem agent-benchmark --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/vibeeval/vibecosystem.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/agent-benchmark .claude/skills/agent-benchmark && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
agent-benchmark
GitHub stars
531
Token cost
~2.9k tokens
SKILL.md length
613 words
Files
1
Skills in repo
144
Repo updated
First seen
Licence
MIT

At a glance

Framework for measuring and tracking agent response quality over time.

  • Works in 4 steps: Score drops more than 10 points on any… → Average score drops more than 5 points… → A previously PASS fixture becomes FAIL → …
  • Evaluating agent changes
  • SKILL.md covers When to Activate, Core Concepts, Directory Structure and Scoring Rubric Template, plus 9 more sections
  • Calls node

What it does

Agent Benchmark is an agent skill from vibeeval/vibecosystem. Framework for measuring and tracking agent response quality over time. Detects regressions before they reach production. Use when evaluating agent changes, auditing quality, or establishing performance baselines.

Its SKILL.md is about 2.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering Agent evaluation and testing. The repository describes itself as: AI software team for Claude Code - 138 agents, 295 skills, 73 hooks. Self-learning, multi-agent swarm, autonomous skill evolution. The licence is MIT.

When your agent uses it

  • Evaluating agent changes
  • Auditing quality
  • Establishing performance baselines

Example prompts

  • “/agent-benchmark”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Score drops more than 10 points on any single fixture
  2. Average score drops more than 5 points across all fixtures for an agent
  3. A previously PASS fixture becomes FAIL
  4. Format compliance drops below 80 (agent stopped following output contract)

What it can do on your machine

Read from SKILL.md and the folder at commit 3b763b1. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • node

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Agent Benchmark loads about 2.9k tokens when it runs. Until then it costs about 57 tokens; SKILL.md has 613 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~57
When it runs · the whole SKILL.md, loaded when a task matches
~2.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from vibeeval/vibecosystem at commit 3b763b1, republished under its MIT licence (© vibeeval). 613 words, ~2,883 tokens.

Download SKILL.mdSave it as .claude/skills/agent-benchmark/SKILL.md (or your agent's skills folder).
name
agent-benchmark
description
Framework for measuring and tracking agent response quality over time. Detects regressions before they reach production. Use when evaluating agent changes, auditing quality, or establishing performance baselines.

Agent Benchmark Framework

Without benchmarks, we cannot know whether agent changes improve or degrade quality. This skill defines how to measure, track, and protect agent performance.

When to Activate

  • Before and after modifying any agent definition file
  • When adding a new skill that an agent depends on
  • Periodic quality audits (weekly/monthly)
  • When a user reports degraded agent output
  • Before promoting an agent from experimental to production

Core Concepts

Why Benchmarks Matter

Agent quality degrades silently. A prompt tweak that improves one response can break ten others. Without a baseline to compare against, every change is a guess. Benchmarks make quality visible and regressions detectable.

Benchmark Types
TypeScopeCostFrequency
Prompt BenchmarkSingle agent, single taskLowEvery agent change
Task BenchmarkEnd-to-end scenarioMediumFeature changes
Regression SuiteAll critical agentsHighWeekly / before release

Directory Structure

~/.claude/benchmarks/
  fixtures/
    code-reviewer/
      missing-error-handling.ts      # Input: code with no try/catch
      sql-injection.py               # Input: unparameterized query
      clean-code.ts                  # Input: code with no issues
    security-reviewer/
      hardcoded-secret.ts            # Input: API key in source
      parameterized-query.py         # Input: safe query (no findings expected)
    verifier/
      passing-build/                 # Input: project that builds
      failing-types/                 # Input: project with type errors
  ground-truth/
    code-reviewer/
      missing-error-handling.json    # Expected findings
      sql-injection.json             # Expected findings
      clean-code.json                # Expected: empty findings
    security-reviewer/
      hardcoded-secret.json
      parameterized-query.json
  rubrics/
    code-reviewer.md                 # Scoring rubric
    security-reviewer.md
    verifier.md
  baselines/
    code-reviewer-2026-03-01.json    # Timestamped baseline scores
    code-reviewer-2026-03-26.json
    security-reviewer-2026-03-26.json
  results/
    run-2026-03-26T14-00.json        # Latest run output

Scoring Rubric Template

Each agent has its own rubric file. The template:

markdown
## [Agent Name] Scoring Rubric

### Completeness (0-30 points)
Did the agent find everything it should have found?

- Found all expected issues: 30
- Missed 1 non-critical issue: 22
- Missed 1 critical issue: 10
- Missed 2+ issues: 5
- Found nothing when issues exist: 0

### Accuracy (0-30 points)
Were the findings correct? No false positives?

- All findings verified correct: 30
- 1 false positive: 22
- 2 false positives: 12
- 3+ false positives: 5
- Majority of findings are wrong: 0

### Actionability (0-20 points)
Did the agent give concrete, implementable fixes?

- Clear fix with file/line reference: 20
- Clear fix without location: 14
- Vague suggestion (refactor this): 7
- No fix suggested: 0

### Format Compliance (0-20 points)
Did the output follow the agent's output contract?

- Matches contract exactly (VERDICT + sections): 20
- Minor deviation (missing one section): 12
- Major deviation (no VERDICT): 5
- Unstructured free text: 0

Ground Truth Format

Ground truth files define what a correct agent response must contain:

json
{
  "fixture": "missing-error-handling.ts",
  "agent": "code-reviewer",
  "required_findings": [
    {
      "id": "missing-try-catch",
      "severity": "HIGH",
      "description_contains": ["error handling", "try", "catch"],
      "location_hint": "fetchUserData"
    }
  ],
  "forbidden_findings": [],
  "required_verdict": "FAIL",
  "min_score": 70
}

Scoring Logic

How a Run Is Scored
1. Load fixture (input code / task)
2. Run agent with fixture as input
3. Parse agent output
4. Check required_findings: each found = +completeness points
5. Check forbidden_findings: each false positive = -accuracy points
6. Check verdict matches required_verdict
7. Check format follows output contract
8. Sum scores → final 0-100
9. Compare against min_score threshold
Score Interpretation
ScoreStatusAction
90-100EXCELLENTNo action needed
75-89GOODMinor tuning optional
60-74WARNInvestigate degradation
40-59POORAgent needs rework
0-39CRITICALBlock deployment

Running Benchmarks

Run All Benchmarks
bash
# Full suite
node ~/.claude/benchmarks/run.mjs

# Output: results/run-{timestamp}.json
Run Single Agent
bash
# Benchmark one agent
node ~/.claude/benchmarks/run.mjs --agent code-reviewer

# With verbose output (shows actual vs expected per fixture)
node ~/.claude/benchmarks/run.mjs --agent code-reviewer --verbose
Compare Against Baseline
bash
# Compare latest run against saved baseline
node ~/.claude/benchmarks/run.mjs --compare

# Compare specific run against specific baseline
node ~/.claude/benchmarks/run.mjs \
  --compare results/run-2026-03-26.json \
  --baseline baselines/code-reviewer-2026-03-01.json
Update Baseline

Only run this after verifying an improvement is real:

bash
# Promote latest results to new baseline
node ~/.claude/benchmarks/run.mjs --baseline update

# Creates: baselines/{agent}-{date}.json

Regression Detection Rules

A regression is triggered when:

  1. Score drops more than 10 points on any single fixture
  2. Average score drops more than 5 points across all fixtures for an agent
  3. A previously PASS fixture becomes FAIL
  4. Format compliance drops below 80 (agent stopped following output contract)
Regression Report Format
REGRESSION DETECTED: code-reviewer

Fixture: sql-injection.py
  Baseline score:  88
  Current score:   61
  Delta:           -27 (CRITICAL)

  Missing finding: SQL injection in execute_query() line 14
  Root cause: Agent definition changed, removed security focus

  Recommendation: Revert agent change or add SQL injection examples

Metrics Tracked Per Agent

MetricFormulaTarget
accuracycorrect_findings / total_findings>= 0.85
completenessfound_issues / total_issues>= 0.90
false_positive_ratefalse_positives / total_findings<= 0.10
format_compliancecorrect_format_runs / total_runs>= 0.95
response_time_p50median seconds to complete<= 30s
response_time_p9595th percentile seconds<= 60s
token_usage_avgaverage tokens per runtracked only
pass_ratefixtures scoring above min_score>= 0.80

Per-Agent Benchmark Definitions

code-reviewer

Fixtures: 6 (2 missing error handling, 2 code smell, 1 SQL injection, 1 clean code) Pass threshold: 70/100 Critical findings: error handling, injection vulnerabilities, magic numbers Non-critical findings: naming conventions, comment quality

Show full SKILL.md (255 more words)Show less
security-reviewer

Fixtures: 8 (hardcoded secrets, injection flaws, auth bypass, safe code) Pass threshold: 75/100 Zero tolerance: must find all HIGH/CRITICAL security issues Acceptable miss: LOW severity cosmetic issues only

verifier

Fixtures: 4 (passing build, type errors, failing tests, lint errors) Pass threshold: 80/100 Critical: must correctly identify PASS vs FAIL state Scoring focus: verdict accuracy over prose quality

sleuth (bug investigator)

Fixtures: 5 (null pointer, race condition, wrong logic, correct code) Pass threshold: 65/100 Critical: must identify root cause, not just symptom Scoring focus: root cause analysis depth

Baseline Management

Baseline File Format
json
{
  "agent": "code-reviewer",
  "created_at": "2026-03-26T00:00:00Z",
  "commit": "abc1234",
  "scores": {
    "missing-error-handling": 88,
    "sql-injection": 92,
    "clean-code": 95,
    "code-smell-nesting": 79,
    "magic-numbers": 82,
    "dead-code": 76
  },
  "aggregate": {
    "average": 85.3,
    "min": 76,
    "max": 95,
    "pass_rate": 1.0
  }
}
Baseline Lifecycle
Create baseline → Make changes → Run benchmark →
Compare → PASS (no regression) → Update baseline
                               → FAIL (regression) → Fix and rerun

CI Integration

GitHub Actions Example
yaml
name: Agent Benchmark
on:
  push:
    paths:
      - '.claude/agents/**'
      - '.claude/skills/**'

jobs:
  benchmark:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Run agent benchmarks
        run: node ~/.claude/benchmarks/run.mjs --compare

      - name: Comment PR with results
        if: github.event_name == 'pull_request'
        uses: actions/github-script@v7
        with:
          script: |
            const results = require('./benchmark-output.json')
            github.rest.issues.createComment({
              issue_number: context.issue.number,
              body: formatBenchmarkResults(results)
            })

      - name: Fail on regression
        run: |
          node ~/.claude/benchmarks/run.mjs --check-regression
          # Exits non-zero if regression > 10 points on any fixture

Benchmark Authoring Guide

Writing a Good Fixture

A good benchmark fixture is:

  1. Realistic - Code that could exist in a real project
  2. Focused - Tests one specific thing the agent should find
  3. Unambiguous - The ground truth is objectively correct
  4. Minimal - No unnecessary noise that could confuse the agent
Example: Good Fixture (code-reviewer)
typescript
// fixtures/code-reviewer/missing-error-handling.ts
// BENCHMARK: Agent must find missing error handling in fetchUser

async function fetchUser(id: string) {
  const response = await fetch(`/api/users/${id}`)
  const data = await response.json()
  return data
}

export default fetchUser

Ground truth:

json
{
  "required_findings": [{
    "severity": "HIGH",
    "description_contains": ["error handling", "network", "try"],
    "location_hint": "fetchUser"
  }],
  "required_verdict": "FAIL",
  "min_score": 70
}
Example: Bad Fixture (too complex)

Do not create fixtures with 10 different issues. The agent may find 7, miss 3, and you cannot tell if the misses are regressions or noise. One fixture = one primary concern.

Integration with Canavar

When a benchmark run produces a regression, log it to the Canavar error ledger:

bash
node ~/.claude/hooks/dist/canavar-cli.mjs errors

Canavar cross-training means a regression in code-reviewer will inject a warning into all producer agents that use code-reviewer output, preventing cascading quality failures.

Quick Reference

bash
# Before changing an agent:
node ~/.claude/benchmarks/run.mjs --agent code-reviewer --save-as before

# After changing the agent:
node ~/.claude/benchmarks/run.mjs --agent code-reviewer --compare before

# Full regression check:
node ~/.claude/benchmarks/run.mjs --compare --fail-on-regression

# Update baselines after confirmed improvement:
node ~/.claude/benchmarks/run.mjs --baseline update

Remember: A benchmark suite that is never run is decoration. Run benchmarks before every agent change. Protect quality proactively, not reactively.

© vibeeval, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/agent-benchmark of vibeeval/vibecosystem.

Open the folder on GitHubat commit 3b763b1

Compare with similar skills

Agent Benchmark next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Agent Evaluationseb1n/awesome-ai-agent-skills206—~1.4kAutomated safety check: PassMIT
MCP Server Builderanthropics/skills180k62 repos~2.3kAutomated safety check: PassApache-2.0
Diagnosing Superpowers Sessionsobra/superpowers296k3 repos~1.7kAutomated safety check: PassMIT
Darwin Skill Optimizeralchaincyf/darwin-skill6.2k1 repos~4.7kAutomated safety check: PassMIT

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Questions about Agent Benchmark

What does Agent Benchmark do?

Framework for measuring and tracking agent response quality over time. Agent Benchmark is an agent skill from vibeeval/vibecosystem. Framework for measuring and tracking agent response quality over time.

When should I use Agent Benchmark?

Agent Benchmark fits situations like: evaluating agent changes; auditing quality; establishing performance baselines.

How do I install Agent Benchmark in Claude Code?

Run `npx skills add vibeeval/vibecosystem --skill agent-benchmark -a claude-code`. Or copy the skill folder (skills/agent-benchmark in vibeeval/vibecosystem) into .claude/skills/agent-benchmark in your project. Claude Code loads it when a task matches its description.

How do I install Agent Benchmark in Codex?

Run `npx skills add vibeeval/vibecosystem --skill agent-benchmark -a codex`. Or copy the skill folder (skills/agent-benchmark in vibeeval/vibecosystem) into .agents/skills/agent-benchmark in your project. Codex loads it when a task matches its description.

Can I use Agent Benchmark in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add vibeeval/vibecosystem --skill agent-benchmark -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/agent-benchmark, .gemini/skills/agent-benchmark, .github/skills/agent-benchmark and .opencode/skills/agent-benchmark in your project.

What does Agent Benchmark need to run?

Going by SKILL.md and its folder, Agent Benchmark needs the command-line tools its instructions call (node).

Does Agent Benchmark access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Agent Benchmark safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Agent Benchmark use?

Agent Benchmark is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Agent Benchmark use?

About 2.9k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Agent Benchmark?

Skills that share tags, products or a category with Agent Benchmark: Skill Judge (shareAI-lab/lab-skills, 314 stars), Agent Evaluation (seb1n/awesome-ai-agent-skills, 206 stars), MCP Server Builder (anthropics/skills, 180k stars) and Diagnosing Superpowers Sessions (obra/superpowers, 296k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Agent Benchmark?

vibeeval (a GitHub user) maintains it in vibeeval/vibecosystem, which has 531 GitHub stars. The repository holds 144 skills in this directory. The repository was last updated on August 8, 2026.

Source: vibeeval/vibecosystem on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.