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 04-eval-reportgit 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/04-eval-report)<a href="https://agentmods.dev/skills/agentscope-ai/openjudge/04-eval-report"><img src="https://agentmods.dev/badge/skills/agentscope-ai/openjudge/04-eval-report/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/04-eval-report"><img src="https://agentmods.dev/badge/skills/agentscope-ai/openjudge/04-eval-report.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 31 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 | $0.00073 | $0.02436 |
| Opus 5 | $0.00036 | $0.01218 |
| Sonnet 5 | $0.00015 | $0.00487 |
| Haiku 4.5 | $0.00007 | $0.00244 |
Grade A, and why
eval-report 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 — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Eval Report
Synthesize everything from your evaluation journey into a comprehensive report. This skill is read-only — it analyzes what exists, doesn't create new graders or datasets.
When to Activate
- You've run 2+ evaluation skills and want the big picture
- You need to report evaluation status to non-technical stakeholders
- You're making a ship/no-ship decision and need evidence
- The evaluation system has been running for a while — time for a health check
Checklist
You MUST create a task for each item and complete them in order:
- Inventory scan — catalog everything in eval-design.md + runs/ history
- Maturity assessment — 5 dimensions × 4 levels
- Cross-skill signal synthesis — consistent findings + contradictions
- Weakness diagnosis — failure concentration, correlations, stratum gaps
- Root cause classification — system / metric / data / unclear
- Prioritized recommendations — P0/P1/P2 actions with impact estimates
- Executive summary — ship readiness + top 3 risks + next actions
Step 1: Inventory Scan
Read eval-design.md and all runs/ directories. Build a timeline:
Timeline:
2026-04-15 01-eval-design → 5 failure modes → 3 dimensions from 200 traces
2026-04-18 02-metric-design → 4 graders configured (2 LLM + 1 rule + 1 executable)
2026-04-25 (evaluation run) → 90-sample stratified dataset scored
2026-05-01 03-align-human → 2 judges Phase 3, 1 Phase 2, 1 Phase 1 (TPR/TNR + kappa)
2026-05-10 07-redteam → safety audit not yet run
Report key metrics:
- Total skills run, total principles, total labels
- Calibrated judges: X of Y (with TPR/TNR range)
- Last activity date per skill
Step 2: Maturity Assessment
Rate the evaluation system across 5 dimensions:
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 · 234 lines · 73 tokens per session scan A 0b6049612bc1
eval-report is a skill published in the GitHub repository agentscope-ai/OpenJudge (824 stars, last pushed 2d ago), licensed Apache-2.0. It adds 73 tokens to every session and 2,436 once invoked, about $0.0004 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.
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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.
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.
ceo-setup
One-time onboarding for the executive/manager commitment workflow — delegation-heavy, meeting prep, decision capture, morning and evening digests. Creates a commitments project and installs two dashboard widgets. After successful setup this skill is excluded from selection until the marker file is deleted.
developer-setup
One-time onboarding for the developer workflow — installs github-workflow missions, creates the commitments workspace, registers per-repo projects, writes calibration memories. After successful setup this skill is excluded from selection until the marker file is deleted.
code-review
Paranoid architect review of code changes for bugs, security, missing tests, and undocumented assumptions. Works on local git diffs OR a GitHub pull request (e.g. owner/repo N). For PRs, can post findings as line-level review comments.