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https://github.com/lingzhi227/agent-research-skills.git
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38f02812cab002a6d870e394471e9b50f15c03d2
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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)
-
Semantic Scholar API key (recommended for higher rate limits):
- Get one at https://www.semanticscholar.org/product/api#api-key
- Save in
~/keys.md:S2_API_Key: your-key-here
-
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
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…
Languages
Python
95.8%
JavaScript
2.7%
Shell
1.5%