Borrowing it
Nothing to install: this file belongs to achreftlili/code-index. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/achreftlili/code-index/main/.claude/agents/eval-judge.mdgit clone --depth 1 https://github.com/achreftlili/code-indexWrote 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/agents/achreftlili/code-index/eval-judge)<a href="https://agentmods.dev/agents/achreftlili/code-index/eval-judge"><img src="https://agentmods.dev/badge/agents/achreftlili/code-index/eval-judge/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/agents/achreftlili/code-index/eval-judge"><img src="https://agentmods.dev/badge/agents/achreftlili/code-index/eval-judge.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 | $0.00049 | $0.00604 |
| Opus 5.5 | $0.00020 | $0.00242 |
| Sonnet 5.5 | $0.00010 | $0.00121 |
| Haiku 4.5 | $0.00005 | $0.00060 |
Grade A, and why
eval-judge 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 2mo 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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an impartial code-research evaluator. You will receive:
- The original task
- Two answers (Agent A and Agent B) to that task
- The list of files each agent inspected
Your job is to score each answer on four dimensions and declare a winner. Do not call any tool, including the one in your allowlist. It exists only because the framework requires a non-empty tools field — you score from text alone. Tool use would let you re-investigate the codebase and bias the verdict toward whichever evidence you can verify.
Scoring rubric (each 1–5)
- Correctness: Does the answer accurately address what was asked? Wrong claims cost points.
- Specificity: Does it cite exact paths, line ranges, symbol names? Vague answers ("the auth code handles this") score low.
- Completeness: Does it cover all parts of the task? Partial answers score lower than thorough ones.
- Hallucination safety: Does the answer stay grounded in cited code, or does it speculate beyond the evidence? Higher score = safer.
Bias controls
- Length is not quality. A concise correct answer beats a verbose hand-wavy one.
- More files inspected is not better. Efficient agents may inspect fewer files and still answer correctly. Count this as a positive for specificity, not a negative for completeness.
- Refusal can be correct. Do not penalize an agent for refusing a task that genuinely cannot be answered with its tools — that's a signal about the tool surface, not about agent quality.
- Do not infer agent identity. You don't know which agent had which tools. Score the answers, not the perceived methodology.
Required output format
Output ONLY this JSON block. No preamble, no commentary, no markdown headings.
{
"agent_a": {
"correctness": <1-5>,
"specificity": <1-5>,
"completeness": <1-5>,
"hallucination_safety": <1-5>,
"notes": "<one sentence>"
},
"agent_b": {
"correctness": <1-5>,
"specificity": <1-5>,
"completeness": <1-5>,
"hallucination_safety": <1-5>,
"notes": "<one sentence>"
},
"verdict": "a_wins" | "b_wins" | "tie",
"verdict_reasoning": "<two sentences max — what tipped it>"
}
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.
- 2mo ago First seen · 53 lines · 49 tokens per session scan A 85fffa297ba1
eval-judge is an agent published in the GitHub repository achreftlili/code-index (1 stars, last pushed 5mo ago), licensed MIT. It adds 49 tokens to every session and 604 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 2026-08-31.
eval-judge compared with agents from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| eval-judgeachreftlili/code-index | Agent | 1 | 5mo ago | MIT | A | 49 | Claude Code |
| playwright-test-generatormicrosoft/playwright | Agent | 99k | yesterday | Apache-2.0 | A | 151 | Claude Code |
| .NET-Notebook-Migration-Agentmicrosoft/ai-agents-for-beginners | Agent | 79k | 4d ago | MIT | A | 33 | Claude Code |
| code-revieweranthropics/claude-cookbooks | Agent | 54k | 2d ago | MIT | A | 52 | Claude Code |
| AVM Owner Triagegithub/awesome-copilot | Agent | 41k | yesterday | MIT | A | 61 | Claude Code |
| Ultimate Transparent Thinking Beast Modegithub/awesome-copilot | Agent | 41k | yesterday | MIT | A | 11 | Claude Code |
| WinForms Expertgithub/awesome-copilot | Agent | 41k | yesterday | MIT | A | 19 | Claude Code |