Annotator Input Parity Check

Annotator Input Parity Check is an AI Engineering 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

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applied-microeconomicsclaude-codeclaude-skillscodexcodex-skillsempirical-research

Install in seconds

Install Annotator Input Parity Check

Copy Annotator Input Parity Check 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/annotator-input-parity-check ~/.claude/skills/annotator-input-parity-check

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/annotator-input-parity-check/SKILL.md in kennethkhoocy/applied-micro-skills
Installs to: ~/.claude/skills/annotator-input-parity-check
Collection: One of 25 skills cataloged from this repository
Category: AI Engineering — 2451 skills

What Annotator Input Parity Check does

Audit the annotation protocol to ensure the model receives the same input as human annotators, preventing recall ceilings and misdiagnosed failures. Use before designing any label-replication model, or when a validation report shows low recall on label subsets.

Annotator Input Parity Check is cataloged under AI Engineering on DirSkills. Annotator Input Parity Check comes from a repository tagged applied-microeconomics, claude-code, claude-skills, codex and codex-skills.

Documentation

README

Excerpt

Annotator Input Parity Check

Problem

A model built to replicate human labels is fed a different evidence base than the one the annotators used. The mismatch masquerades as a modeling or construct problem: recall collapses on the label subset whose evidence lives only in the annotators' source, audits produce increasingly sophisticated theory ("invisible" positives, construct splits, per-domain reliability gates), and successive model generations inherit the wrong input because each review critiques the lineage from inside the frozen input assumption.

Context / Trigger Conditions

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

Frequently asked about Annotator Input Parity Check

What else does kennethkhoocy publish alongside Annotator Input Parity Check?
Annotator Input Parity Check is one of 25 skills that DirSkills catalogs from kennethkhoocy/applied-micro-skills, the repository it ships in. Its siblings there include Adversarial Empirical Review, AsyncOpenAI Concurrency Fix and Cite Placement. Each one is a separate skill with its own page in this directory, installs the same way Annotator Input Parity Check 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 Annotator Input Parity Check compare to other AI Engineering skills?
Annotator Input Parity Check ranks #2278 by stars among the 2451 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. 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 Annotator Input Parity Check against them. Open each page to compare what they document and how they install.