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 The-AI-Directory-Company/agents-and-skills --skill experiment-designgit clone --depth 1 https://github.com/The-AI-Directory-Company/agents-and-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/the-ai-directory-company/agents-and-skills/experiment-design)<a href="https://agentmods.dev/skills/the-ai-directory-company/agents-and-skills/experiment-design"><img src="https://agentmods.dev/badge/skills/the-ai-directory-company/agents-and-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/the-ai-directory-company/agents-and-skills/experiment-design"><img src="https://agentmods.dev/badge/skills/the-ai-directory-company/agents-and-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 | $0.00036 | $0.02086 |
| Opus 5 | $0.00018 | $0.01043 |
| Sonnet 5 | $0.00007 | $0.00417 |
| Haiku 4.5 | $0.00004 | $0.00209 |
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 8d 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Design
Before you start
Gather the following from the user. If anything is missing, ask before proceeding:
- What change are you testing? (UI change, algorithm tweak, pricing model, new feature rollout)
- What outcome do you expect? (Increase conversion, reduce churn, improve engagement — be specific)
- Who is the target population? (All users, a segment, new users only, specific market)
- What is the current baseline? (Current conversion rate, average revenue, retention rate — with approximate numbers)
- What is the minimum detectable effect (MDE)? (Smallest improvement worth detecting — e.g., +2pp conversion, +5% revenue)
- What is the timeline? (How long can the experiment run before a decision is needed?)
- Are there any constraints? (Traffic volume, seasonality, regulatory requirements, shared infrastructure)
Experiment design template
1. Hypothesis
State a falsifiable hypothesis in this format:
If we [change], then [metric] will [direction] by at least [MDE],
because [reasoning based on user behavior or data].
A hypothesis without a mechanism ("because") is a guess. The mechanism forces you to articulate why the change should work, which informs metric selection and interpretation.
2. Primary Metric + Guardrail Metrics
Primary metric: One metric that decides the experiment. Exactly one — not two, not "primary and secondary." If you cannot pick one, you do not understand the goal yet.
Guardrail metrics: 2-4 metrics that must not degrade. These protect against winning on the primary metric at the cost of something else.
| Role | Metric | Current Baseline | MDE | Direction |
|---|---|---|---|---|
| Primary | Checkout conversion rate | 3.2% | +0.5pp | Increase |
| Guardrail | Revenue per user | $12.40 | -$0.50 | Must not decrease |
| Guardrail | Page load time (p95) | 1.8s | +200ms | Must not increase |
| Guardrail | Support ticket rate | 0.4% | +0.1pp | Must not increase |
What ships with it
3 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.
- 8d ago First seen · 161 lines · 36 tokens per session scan A 3a299848f1ed
experiment-design is a skill published in the GitHub repository The-AI-Directory-Company/agents-and-skills (2 stars, last pushed 5mo ago), licensed MIT. It adds 36 tokens to every session and 2,086 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 2026-09-03.
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