Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add agentscope-ai/OpenJudge --skill 00-meta-evalgit clone --depth 1 https://github.com/agentscope-ai/OpenJudgeWrote 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/skills/agentscope-ai/openjudge/00-meta-eval)<a href="https://agentmods.dev/skills/agentscope-ai/openjudge/00-meta-eval"><img src="https://agentmods.dev/badge/skills/agentscope-ai/openjudge/00-meta-eval/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/skills/agentscope-ai/openjudge/00-meta-eval"><img src="https://agentmods.dev/badge/skills/agentscope-ai/openjudge/00-meta-eval.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 27 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00101 | $0.02291 |
| Opus 5 | $0.00051 | $0.01145 |
| Sonnet 5 | $0.00020 | $0.00458 |
| Haiku 4.5 | $0.00010 | $0.00229 |
Grade A, and why
meta-eval 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 10d 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 — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Meta Eval
Entry router for the eval skill collection. You diagnose what the user has and route them to the right sub-skill. You don't do evaluation yourself — you're the triage desk.
Each sub-skill is self-contained: it carries inline the data shapes, statistics, and data principles it needs, so it can be installed and used on its own.
Checklist
You MUST create a task for each item and complete them in order:
- Ask 4 diagnostic questions — data, labels, stakes, domain knowledge
- Match triage table — map user scenario to sub-skill
- Recommend sub-skill — tell the user which workflow to use and why
- Record routing decision — write a brief summary of what was diagnosed and recommended
Diagnostic Questions
Ask these 4 questions (all at once — don't drip-feed):
To route you to the right evaluation skill, I need to understand your situation:
1. What data do you have?
a) Agent traces / production logs
b) Product spec / design docs
c) Nothing yet — starting from scratch
2. Do you have human labels?
a) Yes, ≥50 labeled examples
b) Some, but fewer than 50
c) None
3. What are the stakes?
a) Low — internal experimentation, exploring options
b) Production — customer-facing, quality matters
c) Regulated — compliance requirements, audit trail needed
4. How well do you know this evaluation domain?
a) Very well — have clear standards and criteria
b) Somewhat — general idea but need structure
c) Not well — exploring what "good" even means
Shortcut rule: data_form + label_status already determine the entry workflow
(see triage table). The moment those two are clear — even if stakes and domain knowledge
are not — give the provisional recommendation AND ask the remaining questions in the same
message. stakes and user_prior refine the downstream path (how much calibration rigor,
how fast a path), not the entry point. Never make the user wait a round-trip for a route you
can already determine.
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.
- 10d ago First seen · 193 lines · 101 tokens per session scan A 73245bc3dc53
meta-eval is a skill published in the GitHub repository agentscope-ai/OpenJudge (824 stars, last pushed 3d ago), licensed Apache-2.0. It adds 101 tokens to every session and 2,291 once invoked, about $0.0005 per session on Opus 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 2026-08-30.
Other skills, from other repositories
llm-as-judge-evaluation
Evaluate LLM outputs using frontier models as judges. Use for pairwise model comparison, quality scoring with custom rubrics, and automated evaluation pipelines. Covers position bias mitigation, statistical significance, and generating preference data for DPO/RLHF.
tool-abuse-detection
Detect tool misuse and unexpected code execution via dialogue testing. Use when the agent exposes file, code-execution, or network tools.
tinker-fine-tuning
Provides guidance for fine-tuning LLMs using the Tinker cloud training API from Thinking Machines Lab. Use when running supervised fine-tuning, reinforcement learning (GRPO/PPO), or LoRA training on cloud GPUs via Tinker's managed infrastructure instead of local compute.
langsmith-observability
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
trulens-evaluation-workflow
Systematically evaluate your LLM application with TruLens.
generate-rag-dataset
Generate a synthetic evaluation dataset from your RAG knowledge base. Creates diverse Q&A pairs with expected answers and relevant context, ready for LangWatch experiments and platform import. Use when you need test data for your RAG pipeline.