Agent skill · Code Review & Quality

paper-plan

Generate a structured paper outline from review conclusions and experiment results. Use when user says \"写大纲\", \"paper outline\", \"plan the paper\", \"论文规划\", or wants to create a paper plan before writing.

brycew6m878★ · +32/wk · 1 repos on radarProfile →
claude-codecan modify filesNOASSERTION
Install
npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill paper-plan --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 13 KB
Bundled scripts: none
Allowed tools: Bash(*)ReadWriteEditGrepGlobAgentWebSearchWebFetchmcp__codex__codexmcp__codex__codex-reply
Path: skills/42-wanshuiyin-ARIS/skills/paper-plan/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 3,244
Language: Stata
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Paper Plan: From Review Conclusions to Paper Outline Generate a structured, section-by-section paper outline from: **$ARGUMENTS** ## Constants - **REVIEWER_MODEL = `gpt-5.4`** — Model used via Codex MCP for outline review. Must be an OpenAI model. - **TARGET_VENUE = `ICLR`** — Default venue. User can override (e.g., `/paper-plan "topic" — venue: NeurIPS`). Supported: `ICLR`, `NeurIPS`, `ICML`, `

More from Auto-Empirical-Research-Skills
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About this skill
What does the paper-plan skill do?

Generate a structured paper outline from review conclusions and experiment results. Use when user says \"写大纲\", \"paper outline\", \"plan the paper\", \"论文规划\", or wants to create a paper plan before writing.

How do I install it?

Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill paper-plan --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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