Lit screen

Skill kennethkhoocy/legal-scholarship-skills/plugins/legal-scholarship/skills/lit-review-orchestrator/lit-screen

Stage 6 of the lit review pipeline: screen paper abstracts against the research prompt. The orchestrator's agent-driven flow runs this re-ranker on Opus subagents; a standalone run uses the in-script Claude Sonnet API fallback. Rates relevance 1-10, tags each paper as theoretical/empirical, identifies methodology, and classifies relationship to user's work. Only use this skill when explicitly requested -- e.g., the user says "run lit-screen", "lit-screen", or "/lit-screen". Do NOT auto-trigger on general literature review requests.From its SKILL.md

Install

npx -y skills add kennethkhoocy/legal-scholarship-skills --skill lit-screen

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

17 stars17 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

3.2 KB, 713 tokens by cl100k_base, as published. Nobody here has run it

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

CLI Flags

Flag	Default	Description
`--input`	(required)	Input JSON from Stage 5 (dedup output)
`--query`	(required)	Research query/prompt to screen against
`-o, --output`	`stage6_screened.json`	Output JSON path
`--model`	`claude-sonnet-4-6`	Anthropic model ID (autonomous fallback)
`--concurrency`	`5`	Max simultaneous API requests (autonomous fallback)
`--emit-tasks PATH`	—	Agent-driven: write per-paper screening tasks and stop (no API)
`--ingest-results PATH`	—	Agent-driven: merge Opus screening results and write all outputs (no API)

Output Schema

Each paper gets these fields added:

{
  "screening_score": 8,
  "screening_rationale": "Directly examines board composition changes...",
  "paper_type": "empirical",
  "identification_strategy": "DiD",
  "relationship": "direct competitor"
}

Output Files

JSON: stage6_screened.json -- full paper list with screening fields
JSON: stage6_filtered.json -- papers with score >= 4 only
XLSX: stage6_screened.xlsx -- all papers, sorted by screening_score descending
XLSX: stage6_filtered.xlsx -- filtered papers (score >= 4), sorted by score descending
RIS: stage6_screened.ris -- for import into reference managers
BIB: stage6_screened.bib -- BibTeX entries for papers with score >= 5

Environment Variables

Var	Required	Description
`ANTHROPIC_API_KEY`	Fallback	Standalone-run screening (Sonnet API). The agent-driven flow screens with Opus subagents and needs no key.

Field Values

screening_score: 1 (irrelevant) to 10 (highly relevant); 0 = no abstract
paper_type: theoretical or empirical
identification_strategy: natural experiment, IV, DiD, RDD, structural, descriptive, N/A
relationship: foundational/must-cite, same method different context, same context different method, direct competitor, methodological reference, tangential

What ships with it: 1 file

33.5 KB alongside SKILL.md, 1 of them executable

scripts/

lit_screen.pyruns33.5 KB