Lit Screen

by kennethkhoocy

Lit Screen 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

View source on GitHub Download repo ZIP

applied-microeconomicsclaude-codeclaude-skillscodexcodex-skillsempirical-research

Install in seconds

Install Lit Screen

Copy Lit Screen 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/lit-review-orchestrator/lit-screen ~/.claude/skills/lit-screen

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/lit-review-orchestrator/lit-screen/SKILL.md in kennethkhoocy/applied-micro-skills
Installs to: ~/.claude/skills/lit-screen
Collection: One of 25 skills cataloged from this repository
Category: AI Engineering — 2451 skills

What Lit Screen does

Screens paper abstracts against a research prompt, producing a relevance score (1-10), paper type, methodology, and relationship to the user's work. Part of a literature review pipeline, used when explicitly requested.

Lit Screen is cataloged under AI Engineering on DirSkills. Lit Screen comes from a repository tagged applied-microeconomics, claude-code, claude-skills, codex and codex-skills.

Documentation

README

Excerpt

lit-screen (Stage 6 -- Abstract Screening)

Screen every paper's abstract against the original research prompt. In the orchestrator's agent-driven flow the relevance judgment is produced by Opus subagents through the --emit-tasks / --ingest-results seam (no API key); a standalone run uses the in-script Claude Sonnet API path instead. Produces a relevance score (1-10), rationale, and structured tags for each paper.

Usage

python ~/.claude/skills/lit-screen/scripts/lit_screen.py \
  --input stage5_merged.json \
  --query "your research prompt here" \
  -o stage6_screened.json

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

Frequently asked about Lit Screen

What else does kennethkhoocy publish alongside Lit Screen?
Lit Screen 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, Annotator Input Parity Check and AsyncOpenAI Concurrency Fix. Each one is a separate skill with its own page in this directory, installs the same way Lit Screen 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 Lit Screen compare to other AI Engineering skills?
Lit Screen ranks #2281 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 Lit Screen against them. Open each page to compare what they document and how they install.