What is the output format of this skill?

The skill generates a 'papers/paper_notes.jsonl' file, where each line is a JSON object containing the summary, method, results, limitations, and metadata for a single paper.

What is the primary goal of the paper-notes skill?

The primary goal is to create structured, searchable evidence from research papers—specifically focusing on methods, results, and limitations—to facilitate accurate technical writing and synthesis.

Does it require full-text PDFs to work?

No, it can operate in abstract-only mode for long-tail papers, though it provides higher-fidelity 'fulltext' evidence levels when paper text files are available in the workspace.

How does it handle the quality of summaries?

It uses a 'No Prose' guardrail, forcing the output into bullet points and specific fields to avoid generic boilerplate and ensure the extracted data is actually useful for citations.

Academic Paper Notes

Name: Academic Paper Notes
Author: WILLOSCAR

byWILLOSCAR

•

Data Science & ML

Extracts structured evidence, methods, and results from research papers into a queryable JSONL format for automated research pipelines.

About

The paper-notes skill is a specialized research tool designed to build a structured evidence base for literature reviews and technical surveys. Instead of generating narrative summaries, it follows an evidence-first methodology to extract specific, checkable data points—including experimental setups, performance metrics, and paper-specific limitations—into a machine-readable JSONL format. This ensures that subsequent writing stages have access to high-fidelity data, preventing 'hollow' writing and ensuring every claim is backed by extracted artifacts.

Key Features

Stable bibkey generation for consistent citation management
Structured JSONL output for machine-readable evidence
Evidence-first methodology focusing on metrics and methods over prose
Automated logging of specific paper limitations and caveats
Intelligent prioritization for full-text vs. abstract-level enrichment
89 GitHub stars

Use Cases

Organizing research repositories for automated synthesis and writing
Building a comprehensive evidence base for academic literature surveys
Extracting benchmarks and results for technical state-of-the-art reports

About

Key Features

Stable bibkey generation for consistent citation management
Structured JSONL output for machine-readable evidence
Evidence-first methodology focusing on metrics and methods over prose
Automated logging of specific paper limitations and caveats
Intelligent prioritization for full-text vs. abstract-level enrichment
89 GitHub stars

Use Cases

Organizing research repositories for automated synthesis and writing
Building a comprehensive evidence base for academic literature surveys
Extracting benchmarks and results for technical state-of-the-art reports