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 06-prompt-regressiongit 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/06-prompt-regression)<a href="https://agentmods.dev/skills/agentscope-ai/openjudge/06-prompt-regression"><img src="https://agentmods.dev/badge/skills/agentscope-ai/openjudge/06-prompt-regression/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/06-prompt-regression"><img src="https://agentmods.dev/badge/skills/agentscope-ai/openjudge/06-prompt-regression.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 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 | $0.00086 | $0.02733 |
| Opus 5 | $0.00043 | $0.01367 |
| Sonnet 5 | $0.00017 | $0.00547 |
| Haiku 4.5 | $0.00009 | $0.00273 |
Grade A, and why
prompt-regression 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 11d 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 — 282 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Regression
Compare two prompts head-to-head and determine, with statistical rigor, whether the candidate is better, worse, or tied on each evaluation dimension.
When to Activate
- You changed the system prompt and want to verify it's actually better
- You're iterating on RAG answer templates
- You're optimizing agent step-by-step instructions
- You want data to support a prompt change decision
Checklist
You MUST create a task for each item and complete them in order:
- Load and analyze prompts — diff the baseline vs candidate
- Derive comparison dimensions — from the prompt changes + task type
- Select graders per dimension — pairwise, judge, or rule
- Run position-debiased comparison — swap-aggregate to eliminate order bias
- Compute statistics — win rates + bootstrap 95% CI per dimension
- Present results — per-dimension verdict with confidence intervals
Fast path: run the bundled script
Don't hand-write the win-rate + bootstrap math (the swap-aggregation and CI are easy to get
wrong). Run the bundled, tested script (scripts/pairwise.py, standard library only, no
OpenJudge dependency):
python scripts/pairwise.py --comparisons comparisons.jsonl --candidate candidate --baseline baseline
Each comparison row: {"id","model_a","model_b","score","dimension"?} where score >= 0.5
means model_a won. Emit two rows per query with A/B swapped to debias position. The
script reports per-dimension candidate/baseline/tie rates, bootstrap 95% CI, and a verdict
(BETTER / WORSE / TIED / INSUFFICIENT_EVIDENCE / INCONCLUSIVE; exit 0 only if
better). --self-test to verify it.
Steps below explain how to derive dimensions and produce the comparisons (with OpenJudge or any judge); the inline snippets are the reference behind the script.
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.
- 11d ago First seen · 282 lines · 86 tokens per session scan A beb1e5d32139
prompt-regression is a skill published in the GitHub repository agentscope-ai/OpenJudge (826 stars, last pushed 3d ago), licensed Apache-2.0. It adds 86 tokens to every session and 2,733 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.
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.
prompt-evaluation-runner
Use when evaluating prompts, LLM outputs, red-team suites, or model behavior with local eval configs and safe provider/cost controls.
ai-hacker
Use when aI and LLM security testing — prompt injection, model manipulation, data exfiltration via AI. Use when testing AI-powered applications, finding prompt injection vulnerabilities, or assessing LLM-integrated systems.
llm-tester
You are the LLM Tester, specializing in systematic prompt evaluation, red-teaming, and LLM output quality assurance. You replace "vibes-based" AI evaluation with rigorous, automated, and repeatable verification suites.
promptfoo-evaluation
Configure and run Promptfoo evaluations, assertions, rubrics, and datasets when testing LLM prompts or comparing model behavior.
ai-engineering-toolkit
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.