Detection Score Population Claims

by tangxiangru

Detection Score Population Claims is a Quality skill for Claude Code, published by tangxiangru in AutoR.

804 stars25 forkson tangxiangru/AutoRAdded 2026/08/22Repository updated 2026/08/22

View source on GitHub Download repo ZIP

agentaiai-scientistauto-researchclaudeclaude-codecliharnessllmopenaipaperscience

Install in seconds

Install Detection Score Population Claims

Copy Detection Score Population Claims into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/tangxiangru/AutoR/tree/main/src/skills/a-detection-score-is-a-claim-about-its-population ~/.claude/skills/a-detection-score-is-a-claim-about-its-population

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/tangxiangru/AutoR.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: src/skills/a-detection-score-is-a-claim-about-its-population/SKILL.md in tangxiangru/AutoR
Installs to: ~/.claude/skills/a-detection-score-is-a-claim-about-its-population
Collection: One of 25 skills cataloged from this repository
Category: Quality — 1354 skills

What Detection Score Population Claims does

Detection Score Population Claims explains how to compare detection or ranking scores across studies by publishing the candidate population, checking prevalence effects, and describing extra detections. Use it when a score or arm comparison could be distorted by different denominators or unlabeled items.

Detection Score Population Claims is cataloged under Quality on DirSkills. Detection Score Population Claims comes from a repository tagged agent, ai, ai-scientist, auto-research and claude.

Documentation

README

Excerpt

A detection score is a claim about the population it was computed on

What goes wrong

Two arms are scored on the same candidates, one is better, and the result ships as two areas under two curves. Areas are the least transferable statistic available. The area under a precision-recall curve moves with the positive rate; the area under an ROC curve moves with the composition of the negatives. Your candidate set is almost never the set another study scored on. So when your area and theirs disagree, neither you nor the reader can tell whether you disagree about the method or about the denominator - and the run writes the arithmetic up as a substantive contradiction of the source, which is a false claim that also displaces the true one.

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Detection Score Population Claims

What else does tangxiangru publish alongside Detection Score Population Claims?
Detection Score Population Claims is one of 25 skills that DirSkills catalogs from tangxiangru/AutoR, the repository it ships in. Its siblings there include A Deliverable Is Not an Instruction, A Value You Did Not Measure Still Has A Source and Answer The Why, Not Only The What. Each one is a separate skill with its own page in this directory, installs the same way Detection Score Population Claims does, and is maintained by tangxiangru in that same repository. The rest of the collection is listed on the tangxiangru/AutoR page.
How does Detection Score Population Claims compare to other Quality skills?
Detection Score Population Claims ranks #956 by stars among the 1354 Quality skills in this catalog. The most-starred ones next to it are Benchmark, Benchmark Optimization Loop and API Design Patterns. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Detection Score Population Claims against them. Open each page to compare what they document and how they install.