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 uchicago-dsi/ai-sci-skills --skill experiment-designgit clone --depth 1 https://github.com/uchicago-dsi/ai-sci-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/uchicago-dsi/ai-sci-skills/experiment-design)<a href="https://agentmods.dev/skills/uchicago-dsi/ai-sci-skills/experiment-design"><img src="https://agentmods.dev/badge/skills/uchicago-dsi/ai-sci-skills/experiment-design/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/uchicago-dsi/ai-sci-skills/experiment-design"><img src="https://agentmods.dev/badge/skills/uchicago-dsi/ai-sci-skills/experiment-design.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00043 | $0.00622 |
| Opus 5 | $0.00022 | $0.00311 |
| Sonnet 5 | $0.00009 | $0.00124 |
| Haiku 4.5 | $0.00004 | $0.00062 |
Grade A, and why
experiment-design 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Design
Design For Decisions
- The purpose of an experiment is to change a decision, not to accumulate runs.
- Start from the decision that needs to be made.
- Work backward to the smallest experiment that can separate the live hypotheses.
Use This Output Contract
When proposing an experiment, report:
- Decision to make.
- Live hypotheses.
- Nearest control or baseline.
- Minimal experiment matrix.
- Predicted outcomes by hypothesis.
- Readouts that will decide the result.
- Stop rule and follow-up rule.
- Baseline inheritance: what remains active if the candidate fails, and what evidence would be required to supersede it.
Apply These Rules
- Every arm should exist for a reason.
- If two arms would not change the decision differently, remove one.
- Prefer one-variable changes over broad combinatorial sweeps.
- Freeze everything not under test.
- Use the nearest baseline, not a weak or outdated one.
- If the baseline is unfair, stale, or confounded, fix that before treating the experiment as decision-worthy.
- Prefer cheap discriminative checks before expensive cluster-scale runs.
- Keep the best valid baseline as an explicit arm or immutable comparison until a prospectively defined successor beats it on the same decision readouts.
- For every candidate, identify the exact delta from the baseline and what remains active if that delta fails.
- Make stop rules hypothesis-scoped. Failure of an additive rescue or broader variant retires that addition or combination, not an unchanged successful parent method.
Choose Readouts Carefully
- Pick the smallest set of metrics, artifacts, or visual checks that can separate the hypotheses.
- Include a visual readout when failure modes are easier to see than summarize, for example overlays, curves, slices, masks, diff images, or before/after artifact views.
- Include at least one readout that reflects the real success criterion, not just a proxy.
- Name in advance what outcomes would favor each hypothesis.
- Decide how you will interpret mixed results before launching.
What ships with it
2 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.
- 10d ago First seen · 70 lines · 43 tokens per session scan A ee76773a102b
experiment-design is a skill published in the GitHub repository uchicago-dsi/ai-sci-skills (17 stars, last pushed yesterday), licensed MIT. It adds 43 tokens to every session and 622 once invoked, about $0.0002 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 2026-08-31.
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