Lingzhi Yang 38f02812ca Fix GitHub username in README install instructions
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-19 21:30:10 -05:00
2026-02-19 20:40:03 -05:00

claude-skills

Skills for Claude Code.

Available Skills

deep-research

Systematic academic literature review in 6 phases. Searches arXiv, Semantic Scholar, and conference proceedings. Produces structured notes, a curated paper database, and a synthesized final report.

Includes:

  • 7 Python scripts (search, download, extract, database management, BibTeX, report compilation)
  • 3 reference guides (API reference, note templates, workflow phases)
  • /research <topic> slash command

Installation

Step 1: Install the skill

npx skills add lingzhi227/claude-skills -g -a claude-code

Important: Use the -g (global) flag. The scripts use ~/.claude/skills/deep-research/ paths that require global installation.

Step 2: Install the /research command + check dependencies

git clone https://github.com/lingzhi227/claude-skills.git /tmp/claude-skills
/tmp/claude-skills/install.sh
rm -rf /tmp/claude-skills

This installs the /research slash command and optionally sets up PyMuPDF for PDF extraction.

Step 3: Configure (optional)

  1. Semantic Scholar API key (recommended for higher rate limits):

  2. Output directory: Research outputs go to ~/deep-research-output/ by default.

Usage

In Claude Code:

/research transformer architectures for long-context reasoning

Or just ask naturally: "Do a literature review on protein folding with LLMs"

Requirements

  • Python 3
  • PyMuPDF (pip install PyMuPDF) — optional, for PDF text extraction
S
Description
literature-review: Conduct comprehensive literature reviews using multi-perspective dialogue simulation. Generate diverse expert personas, conduct grounded Q&A conversations, and…; literature-search: Search academic literature using Semantic Scholar, arXiv, and OpenAlex APIs. Returns structured JSONL with title, authors, year, venue, abstract, citations,…; figure-generation: Generate publication-quality scientific figures using matplotlib/seaborn with a three-phase pipeline (query expansion, co…
Readme 463 KiB
Languages
Python 95.8%
JavaScript 2.7%
Shell 1.5%