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 agentlas-ai/agentlas-desktop --skill experimental-designgit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote 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/agentlas-ai/agentlas-desktop/experimental-design)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/experimental-design"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/experimental-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/agentlas-ai/agentlas-desktop/experimental-design"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/experimental-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 | $0.00200 | $0.03241 |
| Opus 5.5 | $0.00080 | $0.01296 |
| Sonnet 5.5 | $0.00040 | $0.00648 |
| Haiku 4.5 | $0.00020 | $0.00324 |
Grade A, and why
experimental-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 1mo 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.
This is a copy
92% identical to experimental-design — 85 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 252 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experimental Design
Overview
The design of a study — how units are assigned to conditions, what is held constant, what is varied, and in what structure — determines what questions the data can answer. No analysis can rescue a confounded or pseudoreplicated design after the fact. This skill is about the decisions made before data collection: picking a design that isolates the effect of interest, randomizing to license causal claims, blocking to remove known nuisance variation, and structuring multi-factor experiments so effects are estimable rather than tangled together.
The three ideas behind almost every good design (Fisher's principles):
- Randomization — assign treatments at random so that confounders, known and unknown, are balanced in expectation. This is what turns a comparison into a causal claim.
- Replication — independent repetition at the right level, so you can estimate variability and your effects aren't artifacts of a single unit. The most common fatal error is pseudoreplication: counting repeated measurements on the same unit as independent replicates.
- Blocking / local control — group similar units (by batch, day, site, litter) and randomize within blocks, removing that nuisance variation from the error term instead of letting it inflate noise.
This skill helps you choose among design types, generate the actual randomization or DOE layout (with reproducible scripts), and avoid the structural mistakes that make data uninterpretable.
When to Use This Skill
- Planning any comparative experiment or trial and deciding how to assign units
- Randomizing subjects/samples to arms (simple, blocked, stratified, or cluster)
- Removing nuisance variation by blocking or stratification
- Designing multi-factor experiments: full or fractional factorial, screening designs
- Optimizing a response over continuous factors (response-surface designs)
- Within-subject / repeated-measures, crossover, split-plot, or Latin-square designs
- Cluster- or group-randomized designs (sites, clinics, classrooms, litters)
- Deciding the number and level of replicates and avoiding pseudoreplication
- Sequential, group-sequential, or adaptive designs with interim analyses
- Laying out plates/batches and randomizing run order to defeat drift
What ships with it
6 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.
- 1mo ago First seen · 252 lines · 200 tokens per session scan A 8d1e072e96c4
experimental-design is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 200 tokens to every session and 3,241 once invoked, about $0.0008 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to experimental-design, differing in 85 lines, and is treated as a copy.
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