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 Aperivue/medsci-skills --skill design-ai-benchmarkinggit clone --depth 1 https://github.com/Aperivue/medsci-skillsWrote 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/aperivue/medsci-skills/design-ai-benchmarking)<a href="https://agentmods.dev/skills/aperivue/medsci-skills/design-ai-benchmarking"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/design-ai-benchmarking/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/aperivue/medsci-skills/design-ai-benchmarking"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/design-ai-benchmarking.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.02655 |
| Opus 5 | $0.00050 | $0.01327 |
| Sonnet 5 | $0.00020 | $0.00531 |
| Haiku 4.5 | $0.00010 | $0.00265 |
Grade A, and why
design-ai-benchmarking 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 — 231 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Design-AI-Benchmarking Skill
Purpose
This skill pressure-tests an AI-vs-human-expert benchmark before any ratings are collected, so that
the comparison is fair, the rubric measures distinct constructs, the scale is calibrated, and the
reported reliability is interpretable. It is the AI-evaluation specialization of /design-study: where
/design-study reviews a study in general, this skill owns the specific machinery of comparing AI
system(s) to a panel of human experts (or to each other) on rated outputs.
Use it when:
- one or more AI systems will be scored against a human-expert reference (reader study, annotation panel, AI-output evaluation, model-vs-model bench)
- a rubric and rating protocol must be locked before reviewers begin
- a benchmark feels vulnerable to "the highest score is just the most tautological item" or "low agreement, but we cannot tell why" criticism
- a reviewer or editor asks how the evaluation controlled for rater drift, leakage, or judge bias
Do not use it for: general study/validity review (use /design-study); statistical execution such
as ICC or DeLong (use /analyze-stats); reporting-guideline item audits (use /check-reporting);
or reviewing an already-written manuscript (use /peer-review or /self-review).
Communication Rules
- Communicate with the user in their preferred language.
- Use English for statistical, machine-learning, and reporting-guideline terminology.
- Be direct about evaluation-validity risks, but always propose the smallest feasible fix first.
- Never invent reviewer ratings, reference labels, or agreement statistics; those come from collected data only.
Standard Output
## AI-Benchmark Design Review
Evaluation question: ...
Arms / systems compared: ...
Reference (human-expert panel): ...
Unit of rating: (item / case / output)
### Rubric (decoupled dimensions)
- dimension -> construct -> anchors (1..k)
### Calibration probes (blinded, randomized)
- positive-control / known-bad / instability / mechanism-contradiction
### Reviewer panel
- n reviewers, metadata captured, per-reviewer randomized order
### Reliability plan
- overall IRR target + control-item IRR (reported separately)
### Judge strategy
- human-as-judge / LLM-as-judge / both + adjudication rule
### Validity risks
1. ...
### Minimal fixes
- ...
### Decision
- Ready to collect / Needs rubric revision / Needs arm or judge redesign
What ships with it
4 files 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 · 231 lines · 100 tokens per session scan A b8f794a1f6c8
design-ai-benchmarking is a skill published in the GitHub repository Aperivue/medsci-skills (292 stars, last pushed 4d ago), licensed MIT. It adds 100 tokens to every session and 2,655 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.
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