Paper Deep Reading Teaching Explainer
PassAudited by VirusTotal on May 4, 2026.
Overview
Type: OpenClaw Skill Name: paper-deep-reading-teaching-explainer Version: 10.1.8 The skill bundle is a comprehensive and highly structured framework designed for deep-reading research papers and generating educational storyboards. The Python scripts (e.g., assemble_storyboard_pdf.py, build_paper_deep_reading_bundle.py) are transparent utility tools for file manipulation, PDF generation using the Pillow library, and directory structure validation. No evidence of data exfiltration, malicious network activity, or harmful prompt injection was found; the extensive instructions in SKILL.md are strictly focused on enforcing pedagogical quality, reproducibility, and a staged workflow for academic analysis.
Findings (0)
Artifact-based informational review of SKILL.md, metadata, install specs, static scan signals, and capability signals. ClawScan does not execute the skill or run runtime probes.
If used, the helper script will read local images and write a PDF output, so incorrect paths could process the wrong files.
The skill documents a local Python helper for assembling generated images into a PDF. This is purpose-aligned, but it is still local file-processing code that should be run only on intended image folders.
Use `scripts/assemble_storyboard_pdf.py` or an equivalent local implementation with PIL/Pillow or ReportLab.
Run the PDF assembly step only after reviewing the input directory and output path, and keep it inside the project workspace.
Paper details, summaries, claims, or visual prompts may be shared with the selected image-generation provider during visual steps.
The skill may route paper-derived storyboard prompts to an image-generation skill or external image API. This is disclosed and central to the visual workflow, but it creates a data-sharing boundary users should understand.
Codex / Claude Code / coding-agent environments: prefer the `imagegen` skill when available. If it is unavailable or insufficient, use ChatGPT Images 2.0 API or another user-approved image-generation API.
Use visual generation only for papers you are allowed to share with the chosen provider, and prefer explicitly approved tools or private/local options for confidential work.
Private draft comments or accidental instructions inside LaTeX sources could influence outputs or be summarized into downstream artifacts.
The skill may treat uploaded LaTeX source comments as usable context. This can improve paper understanding, but comments may contain private notes or irrelevant instructions if the source bundle is not sanitized.
LaTeX-only input: use `figure`/`table` environments, captions, labels, `\includegraphics` paths, equations, section text, appendix content, and source comments when useful.
Before uploading LaTeX source for this skill, remove private comments, credentials, internal notes, or prompt-like instructions that should not affect the report or visual prompts.