field-experiment
Skill25 starsChanged 34 days agoLicence not identifiedView on GitHub
Design, analyze, and write up randomized experiments (field experiments, A/B tests, RCTs): stratified and clustered designs, randomization inference, covariate adjustment done right, noncompliance, attrition and gated outcomes, treatment-effect heterogeneity, and interference. TRIGGER on "A/B test", "randomized experiment", "RCT", "field experiment", "randomized holdout", "lift test", "randomization inference", "stratified randomization", "matched pairs", "rerandomization", "cluster randomized", "geo experiment", "switchback", "randomized rollout", "stepped wedge", "power analysis", "MDE", "sequential test", "peeking", "ratio metric", "encouragement design", "noncompliance", "ITT", "attrition", "Lee bounds", "CUPED", "uplift", "GATES", "heterogeneous treatment effects", "interference", "spillover", "SUTVA". Observational causal forests belong to causal-design.
In this repository19 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- bibcheckSkill
- causal-designSkill
- compile-latexSkill
- conjointSkill
- councilSkill
- course-siteSkill
- didSkill
- ivSkill
- litreviewSkill
- rddSkill
- reading-papersSkill
- referee-responseSkill
- replication-packageSkill
- research-talkSkill
- review-paperSkill
- slide-reviewSkill
- synthetic-controlSkill
- teaching-lectureSkill
The file
We split this skill into the rules it gives an agent
5 rules. About 0 are common, 4 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈4
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
The licence could not be identified — read it at the source. We count its rules but don't quote them. Read them on GitHub.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
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Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

