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 03-align-humangit 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/03-align-human)<a href="https://agentmods.dev/skills/agentscope-ai/openjudge/03-align-human"><img src="https://agentmods.dev/badge/skills/agentscope-ai/openjudge/03-align-human/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/03-align-human"><img src="https://agentmods.dev/badge/skills/agentscope-ai/openjudge/03-align-human.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, 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 Excessive Agency · line 279 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Rogue Agent · line 32 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 | $0.00100 | $0.03305 |
| Opus 5 | $0.00050 | $0.01653 |
| Sonnet 5 | $0.00020 | $0.00661 |
| Haiku 4.5 | $0.00010 | $0.00331 |
Grade A, and why
align-human 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 — 294 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Align Human
Measure whether your automatic judge agrees with human judgment, detect where and why they disagree, and build a roadmap to reduce human review over time.
When to Activate
- You have a working judge/grader and 50+ human-labeled examples
- You want to know if the judge is trustworthy enough to replace human review
- You've noticed the judge's decisions being overturned by humans
- You're preparing to deploy an evaluation as a production gate
Checklist
You MUST create a task for each item and complete them in order:
- Load paired data — match judge verdicts with human labels
- Measure TPR/TNR — confusion matrix + per-stratum breakdown
- Calculate agreement — Cohen's kappa, Gwet's AC1, systematic bias
- Run bias detection — 5 systematic bias checks
- Analyze disagreements — cluster patterns + diagnose root causes
- Build human-reduction roadmap — 4-phase transition plan
- Confirm and record — one confirmation, then write results
Fast path: run the bundled script
Don't hand-write the calibration statistics — that is exactly where subtle bugs hide. Run
the bundled, tested script (scripts/calibration.py, standard library only, no OpenJudge
dependency):
python scripts/calibration.py --pairs pairs.jsonl # one paired file, OR
python scripts/calibration.py --verdicts verdicts.jsonl --labels labels.jsonl --stratum-key difficulty
Paired rows look like {"id","judge":"pass|fail","human":"pass|fail","stratum"?} (judge/human
may also be 1/0). It prints the confusion matrix, TPR/TNR/F1 with bootstrap 95% CIs, Cohen's
kappa, Gwet's AC1 (auto-flags the kappa paradox), directional bias, per-stratum TPR/TNR, and
the calibration gate verdict (calibrated / not_calibrated / insufficient_evidence;
exit code 0 only if calibrated). --json for machine output, --self-test to verify it.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 294 lines · 100 tokens per session scan A 62d9a6ebc171
align-human is a skill published in the GitHub repository agentscope-ai/OpenJudge (824 stars, last pushed 2d ago), licensed Apache-2.0. It adds 100 tokens to every session and 3,305 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
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.
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.
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.