/paper-analyze
Deep analysis of a single paper with figure extraction from arXiv source
$ npx -y skills add Xiangyue-Zhang/auto-deep-researcher-24x7 --skill paper-analyze --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/paper-analyze
Context preview
The summary Claude sees to decide when to auto-load this skill.
Deep analysis of a single paper with figure extraction from arXiv source
SKILL.md
paper-analyze.SKILL.mdname: paper-analyze description: "Deep analysis of a single paper with figure extraction from arXiv source"
paper-analyze
Perform deep analysis of a single academic paper.
Usage
Claude Code: /paper-analyze <arxiv_id or url> Codex: $paper-analyze
Behavior
1. Fetch paper metadata from arXiv API 2. Attempt to download arXiv source package (.tar.gz) 3. Extract actual figures from source (not screenshots) 4. Read the full paper (PDF if source unavailable) 5. Generate structured analysis
Figure Extraction
Priority order: 1. arXiv source package → extract .png/.jpg/.pdf figures 2. PDF extraction as fallback 3. Name files with arxiv_id prefix to avoid collisions
Output Format
# [Paper Title] **arXiv**: [id] | **Authors**: ... | **Year**: ... ## Problem What specific problem does this paper address? ## Motivation Why is this problem important? What gap exists? ## Method Detailed technical approach with key equations/algorithms.  ## Experiments - Datasets, baselines, metrics - Key results table - Ablation findings ## Insights - What can we learn and apply? - Strengths and limitations - Connections to our research
🔥 An autonomous AI agent that runs your deep learning experiments 24/7 while you sleep. Zero-cost monitoring, Leader-Worker architecture, constant-size memory.
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