Adversarial Empirical Review

by kennethkhoocy

Adversarial Empirical Review is a Quality skill for Claude Code, published by kennethkhoocy in applied-micro-skills.

48 stars0 forkson kennethkhoocy/applied-micro-skillsAdded 2026/08/11Repository updated 2026/07/22

View source on GitHub Download repo ZIP

applied-microeconomicsclaude-codeclaude-skillscodexcodex-skillsempirical-research

Install in seconds

Install Adversarial Empirical Review

Copy Adversarial Empirical Review 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/kennethkhoocy/applied-micro-skills/tree/main/plugins/applied-micro/skills/adversarial-empirical-review ~/.claude/skills/adversarial-empirical-review

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/kennethkhoocy/applied-micro-skills.git

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

In this catalog

Source file: plugins/applied-micro/skills/adversarial-empirical-review/SKILL.md in kennethkhoocy/applied-micro-skills
Installs to: ~/.claude/skills/adversarial-empirical-review
Collection: One of 25 skills cataloged from this repository
Category: Quality — 1354 skills

What Adversarial Empirical Review does

Automates verification that empirical research tables are correct by comparing them against the underlying data and analysis code through an adversarial, regression-gated review pipeline. Use when you need to audit whether numbers in LaTeX tables match computed results.

Adversarial Empirical Review is cataloged under Quality on DirSkills. Adversarial Empirical Review comes from a repository tagged applied-microeconomics, claude-code, claude-skills, codex and codex-skills.

Documentation

README

Excerpt

Adversarial Empirical Review

Runs a regression-gated, N-round adversarial loop over a project's empirical output. The design is in docs/2026-06-08-adversarial-empirical-review-design.md (v2) and the module API in docs/CONTRACTS.md. Read the design before operating the skill.

The costly error here is corrupting a result that was already correct, so the whole pipeline is incumbent-preserving: a hard failure-set regression gate, "no clear difference keeps the incumbent" for residuals, and minimization of LLM judgment in favour of mechanical checks.

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

Commands Adversarial Empirical Review provides

Slash commands named in this skill’s SKILL.md, listed in the order they first appear.

/adversarial-empirical-review

Frequently asked about Adversarial Empirical Review

What else does kennethkhoocy publish alongside Adversarial Empirical Review?
Adversarial Empirical Review is one of 25 skills that DirSkills catalogs from kennethkhoocy/applied-micro-skills, the repository it ships in. Its siblings there include Annotator Input Parity Check, AsyncOpenAI Concurrency Fix and Cite Placement. Each one is a separate skill with its own page in this directory, installs the same way Adversarial Empirical Review does, and is maintained by kennethkhoocy in that same repository. The rest of the collection is listed on the kennethkhoocy/applied-micro-skills page.
How does Adversarial Empirical Review compare to other Quality skills?
Adversarial Empirical Review ranks #1296 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 Adversarial Empirical Review against them. Open each page to compare what they document and how they install.