Skill Judge
shareAI-lab/lab-skills
Evaluate Agent Skill design quality with an opinionated, practice-derived rubric informed by public specifications and examples.
Framework for measuring and tracking agent response quality over time.
$ npx skills add vibeeval/vibecosystem --skill agent-benchmark -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install vibeeval/vibecosystem agent-benchmark --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "agent-benchmark" agent skill from https://github.com/vibeeval/vibecosystem/tree/main/skills/agent-benchmark into .claude/skills/agent-benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-benchmark", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/vibeeval/vibecosystem/tree/main/skills/agent-benchmarkType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add vibeeval/vibecosystem --skill agent-benchmark -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install vibeeval/vibecosystem agent-benchmark --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/vibeeval/vibecosystem.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/agent-benchmark .agents/skills/agent-benchmark && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "agent-benchmark" agent skill from https://github.com/vibeeval/vibecosystem/tree/main/skills/agent-benchmark into .agents/skills/agent-benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-benchmark", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add vibeeval/vibecosystem --skill agent-benchmark -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install vibeeval/vibecosystem agent-benchmark --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/vibeeval/vibecosystem.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/agent-benchmark .cursor/skills/agent-benchmark && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "agent-benchmark" agent skill from https://github.com/vibeeval/vibecosystem/tree/main/skills/agent-benchmark into .cursor/skills/agent-benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-benchmark", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/vibeeval/vibecosystem.git --path skills/agent-benchmark--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add vibeeval/vibecosystem --skill agent-benchmark -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install vibeeval/vibecosystem agent-benchmark --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/vibeeval/vibecosystem.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/agent-benchmark .gemini/skills/agent-benchmark && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "agent-benchmark" agent skill from https://github.com/vibeeval/vibecosystem/tree/main/skills/agent-benchmark into .gemini/skills/agent-benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-benchmark", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install vibeeval/vibecosystem agent-benchmarkInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add vibeeval/vibecosystem --skill agent-benchmark -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/vibeeval/vibecosystem.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/agent-benchmark .github/skills/agent-benchmark && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "agent-benchmark" agent skill from https://github.com/vibeeval/vibecosystem/tree/main/skills/agent-benchmark into .github/skills/agent-benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-benchmark", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add vibeeval/vibecosystem --skill agent-benchmark -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install vibeeval/vibecosystem agent-benchmark --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/vibeeval/vibecosystem.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/agent-benchmark .opencode/skills/agent-benchmark && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "agent-benchmark" agent skill from https://github.com/vibeeval/vibecosystem/tree/main/skills/agent-benchmark into .opencode/skills/agent-benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-benchmark", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
agent-benchmarkFramework 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. 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.
4 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 3b763b1. It shows what the files ask for, not the result of running them.
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.
Shell commands in SKILL.md call:
nodeFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from vibeeval/vibecosystem at commit 3b763b1, republished under its MIT licence (© vibeeval). 613 words, ~2,883 tokens.
.claude/skills/agent-benchmark/SKILL.md (or your agent's skills folder).Without benchmarks, we cannot know whether agent changes improve or degrade quality. This skill defines how to measure, track, and protect agent performance.
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.
| Type | Scope | Cost | Frequency |
|---|---|---|---|
| Prompt Benchmark | Single agent, single task | Low | Every agent change |
| Task Benchmark | End-to-end scenario | Medium | Feature changes |
| Regression Suite | All critical agents | High | Weekly / before release |
~/.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 outputEach agent has its own rubric file. The template:
## [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: 0Ground truth files define what a correct agent response must contain:
{
"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
}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 | Status | Action |
|---|---|---|
| 90-100 | EXCELLENT | No action needed |
| 75-89 | GOOD | Minor tuning optional |
| 60-74 | WARN | Investigate degradation |
| 40-59 | POOR | Agent needs rework |
| 0-39 | CRITICAL | Block deployment |
# Full suite
node ~/.claude/benchmarks/run.mjs
# Output: results/run-{timestamp}.json# 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 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.jsonOnly run this after verifying an improvement is real:
# Promote latest results to new baseline
node ~/.claude/benchmarks/run.mjs --baseline update
# Creates: baselines/{agent}-{date}.jsonA regression is triggered when:
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| Metric | Formula | Target |
|---|---|---|
| accuracy | correct_findings / total_findings | >= 0.85 |
| completeness | found_issues / total_issues | >= 0.90 |
| false_positive_rate | false_positives / total_findings | <= 0.10 |
| format_compliance | correct_format_runs / total_runs | >= 0.95 |
| response_time_p50 | median seconds to complete | <= 30s |
| response_time_p95 | 95th percentile seconds | <= 60s |
| token_usage_avg | average tokens per run | tracked only |
| pass_rate | fixtures scoring above min_score | >= 0.80 |
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
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
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
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
{
"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
}
}Create baseline → Make changes → Run benchmark →
Compare → PASS (no regression) → Update baseline
→ FAIL (regression) → Fix and rerunname: 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 fixtureA good benchmark fixture is:
// 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 fetchUserGround truth:
{
"required_findings": [{
"severity": "HIGH",
"description_contains": ["error handling", "network", "try"],
"location_hint": "fetchUser"
}],
"required_verdict": "FAIL",
"min_score": 70
}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.
When a benchmark run produces a regression, log it to the Canavar error ledger:
node ~/.claude/hooks/dist/canavar-cli.mjs errorsCanavar 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.
# 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 updateRemember: 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
Just SKILL.md in skills/agent-benchmark of vibeeval/vibecosystem.
Open the folder on GitHubat commit 3b763b1
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Agent Benchmark this skillvibeeval/vibecosystem | 531 | — | ~2.9k | Automated safety check: Pass | MIT | |
| Skill JudgeshareAI-lab/lab-skills | 314 | — | ~1.9k | Automated safety check: Pass | Apache-2.0 | |
| Agent Evaluationseb1n/awesome-ai-agent-skills | 206 | — | ~1.4k | Automated safety check: Pass | MIT | |
| MCP Server Builderanthropics/skills | 180k | 62 repos | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| Diagnosing Superpowers Sessionsobra/superpowers | 296k | 3 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Darwin Skill Optimizeralchaincyf/darwin-skill | 6.2k | 1 repos | ~4.7k | Automated safety check: Pass | MIT |
shareAI-lab/lab-skills
Evaluate Agent Skill design quality with an opinionated, practice-derived rubric informed by public specifications and examples.
seb1n/awesome-ai-agent-skills
Design reproducible evaluations for AI agents with representative task sets, explicit rubrics, appropriate graders, baselines, regression gates, and failure analysis.
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
obra/superpowers
Investigates a session where Superpowers went wrong, reads the transcripts on disk and produces an evidence-cited report, optionally prepared as a bug report for the maintainers.
alchaincyf/darwin-skill
Scores SKILL.md files on a nine-dimension rubric, then improves them in a keep-or-revert loop with independent judge agents, test prompts, git history and human checkpoints.
rohitg00/ai-engineering-from-scratch
Evaluates an Agent Skill bundle before release for structure, trigger quality, artifact improvement, script correctness, safety, installed-tree integrity and host portability.
vibeeval/vibecosystem
Security-focused differential code review with blast radius analysis, risk-adaptive depth (DEEP/FOCUSED/SURGICAL), git history correlation, and structured finding format.
vibeeval/vibecosystem
A skill your agent uses when making any factual claim about the codebase — existence, absence, or behavior.
vibeeval/vibecosystem
Systematic false positive verification for security findings.
vibeeval/vibecosystem
n8n otomasyon workflow'lari. An agent skill from vibeeval/vibecosystem.
vibeeval/vibecosystem
A skill your agent uses when context compression is imminent, when resuming a session, or when preserving critical decisions across long tasks.
vibeeval/vibecosystem
Property-based testing (PBT) patterns with fast-check (JS/TS), Hypothesis (Python), and gopter (Go).
Categories
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.
Agent Benchmark fits situations like: evaluating agent changes; auditing quality; establishing performance baselines.
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.
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.
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.
Going by SKILL.md and its folder, Agent Benchmark needs the command-line tools its instructions call (node).
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.
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.
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.
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.
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.
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.