Expert-level guidance for writing publication-ready papers targeting NeurIPS, ICML, ICLR, ACL, AAAI, and COLM. This skill combines writing philosophy from top researchers (Nanda, Farquhar, Karpathy, Lipton, Steinhardt) with practical tools: LaTeX templates, citation verification APIs, and conference checklists.
Automate repetitive workflows and reduce manual effort
Example: Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example: Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example: Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Time estimate: 15-45 minutes depending on use case complexity
Prerequisites
Submit your Claude Code skill and start earning
epx add davila7/claude-code-templates --skill ml-paper-writingCompatible with
Supported agents
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionml-paper-writingExecute the skills CLI command in your project's root directory to begin installation:
epx add davila7/claude-code-templates --skill ml-paper-writingFetches ml-paper-writing from davila7/claude-code-templates and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
.cursor/skills/ml-paper-writing
Restart Cursor to activate ml-paper-writing. Access via /ml-paper-writingin your agent's command palette.
Learn more:View source on GitHubSkills CLI docsAbout CursorWhat are agent skills?
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
ml-paper-writing
zechenzhangagi/ai-research-skills · AI/ML · 2 shared tags
scientific-visualization
davila7/claude-code-templates · Productivity · same repo
ui-ux-pro-max
davila7/claude-code-templates · Frontend · same repo
manim
davila7/claude-code-templates · Productivity · same repo
openalex-database
davila7/claude-code-templates · Productivity · same repo
stop-slop
hvpandya/stop-slop · other · tag: writing
ml-paper-writing is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: ml-paper-writing is the kind of skill you can hand to a new teammate without a long onboarding doc.
ml-paper-writing is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: ml-paper-writing is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for ml-paper-writing matched our evaluation — installs cleanly and behaves as described in the markdown.
ml-paper-writing fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
ml-paper-writing has been reliable in day-to-day use. Documentation quality is above average for community skills.
ml-paper-writing is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in ml-paper-writing — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
ml-paper-writing reduced setup friction for our internal harness; good balance of opinion and flexibility.
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