experimental-design SKILL.md skillA
experimental-design from k-dense-ai/scientific-agent-skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add k-dense-ai/scientific-agent-skills/experimental-design@v1.5Pins v1.5 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- k-dense-ai/scientific-agent-skills
- Path
skills/experimental-design/SKILL.md- Versions recorded
- 7
- Latest version
- v1.5, committed Oct 1, 2026
- First version recorded
- Jun 11, 2026
- Size
- 16,328 bytes
- Repository stars
- 53,159
- License
- MIT
Designs experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so results are interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs…
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# 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. …
Read the whole file at its exact version.
How to install
mdr add k-dense-ai/scientific-agent-skills/experimental-design@sha256:db2de0f3d29b7729The line at the top of this page pins the label v1.5, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_jkxx6uxionetdutx)
4 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 7 versions of this file, between Jun 11, 2026 and Oct 1, 2026, about one every 19 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| v1.5 latest | 2026-10-01 | a92663a | 16,328 B | A | view · diff |
| v1.4 | 2026-09-30 | 8f1aa42 | 14,563 B | A | view · diff |
| v1.3 | 2026-09-30 | ce9e6e9 | 14,563 B | A | view · diff |
| v1.2 | 2026-09-02 | 1e5eeff | 14,002 B | A | view · diff |
| v1.1 | 2026-07-28 | 4fb7e0b | 13,044 B | A | view · diff |
| v1.0 | 2026-07-26 | b085e11 | 13,044 B | A | view · diff |
| git:20260611.fdf52ef | 2026-06-11 | fdf52ef | 13,071 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (16328 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
k-dense-ai/scientific-agent-skills · 53,159 stars · license MIT · pushed 2026-10-19 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_jkxx6uxionetdutx GET https://markdownregistry.com/api/v1/resolve?ref=k-dense-ai/scientific-agent-skills/experimental-design GET https://markdownregistry.com/api/v1/blob/db2de0f3d29b7729776a91e923c9c83801298f5ad8e88fe44570045fbefcc248
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.
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