Lit screen

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

Claude Code and Codex skills for legal scholarship: verified citation placement (Bluebook/OSCOLA/McGill), law-review docx pipelines, document delivery tooling

Install

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

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What its author says it does

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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.

SKILL.md

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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