This repository collects reusable Codex/agent skills for remote-sensing academic workflows. The skills are focused on manuscript writing, polishing, paper-to-slides conversion, paper reading, reviewer-style assessment, literature search, and publication-grade scientific figures.
skills/
_shared/
remote-sensing-nature-academic-search/
remote-sensing-nature-figure/
remote-sensing-nature-paper2ppt/
remote-sensing-nature-polishing/
remote-sensing-nature-reader/
remote-sensing-nature-reviewer/
Each skill contains a SKILL.md router, a manifest.yaml when needed, and supporting static/, references/, scripts/, assets/, or evals/ folders.
skills/_shared/ contains shared writing, terminology, ethics, and journal-format fragments used by several skills. Keep it next to the skill folders so relative paths such as ../_shared/core/terminology-ledger.md continue to work.
Copy the skill folders into your local Codex skills directory:
mkdir -p ~/.codex/skills
cp -R skills/remote-sensing-nature-* ~/.codex/skills/Then restart Codex or refresh your skill registry.
| Skill | Use it for | Usage guide |
|---|---|---|
remote-sensing-nature-polishing |
Academic English polishing, Nature-leaning prose, Chinese-to-English manuscript repair, and LaTeX layout fixes | USAGE.md |
remote-sensing-nature-paper2ppt |
Turning a paper, PDF, abstract, or reading notes into a real Chinese academic PPTX deck | USAGE.md |
remote-sensing-nature-reviewer |
Simulated Nature-style pre-submission reviewer reports | USAGE.md |
remote-sensing-nature-academic-search |
Remote-sensing literature search, citation verification, BibTeX/RIS management, and DOI/arXiv lookup | USAGE.md |
remote-sensing-nature-figure |
Publication-grade Python/R scientific figures for remote-sensing papers | USAGE.md |
remote-sensing-nature-reader |
Bilingual, source-grounded Markdown paper readers from PDF/HTML/DOI/arXiv/text | USAGE.md |
Use remote-sensing-nature-reader to read the first 8 pages of this PDF and create a Chinese-English Markdown reader.
Use remote-sensing-nature-paper2ppt to make a Chinese group-meeting PPT from this remote-sensing benchmark paper.
Use remote-sensing-nature-figure with Python to create a Nature-style multi-panel figure from this CSV.
Use remote-sensing-nature-polishing to polish this abstract for a remote-sensing AI paper.
Most skills use a router structure:
- Read
SKILL.md. - Load
manifest.yaml. - Load only the core fragments and workflow fragments needed for the current request.
- Use deeper
references/files only when the task requires them.
This keeps each invocation focused while preserving detailed guidance for complex tasks.
- The skills are designed for remote sensing, Earth observation, geospatial AI, GIScience, photogrammetry, MLLMs, multimodal benchmarks, and related academic workflows.
- They emphasize traceability and non-invention: do not fabricate citations, results, repository IDs, reviewer comments, figure panels, or line numbers.
- Some workflows may require local tools such as Python, matplotlib, python-pptx, PyMuPDF, pypdf, or R packages depending on the selected backend.