Skill
easypaper-interactive-…
Interactively build EasyPaper metadata from a research-materials folder using Claude Code's file-investigation tools. Output is…
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Skill
/easypaper-academic-writing-rules
Enforce academic writing and LaTeX drafting conventions for EasyPaper outputs.
Install
$ npx -y skills add tsinghua-fib-lab/agentsociety --skill easypaper-academic-writing-rules --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
/easypaper-academic-writing-rules
Context preview
The summary Claude sees to decide when to auto-load this skill.
Enforce academic writing and LaTeX drafting conventions for EasyPaper outputs.
SKILL.md
easypaper-academic-writing-rules.SKILL.mddescription: Enforce academic writing and LaTeX drafting conventions for EasyPaper outputs.
Use this skill when polishing or reviewing manuscript quality before final submission.
Style objectives
- Keep formal academic register.
- Keep terminology consistent across sections.
- Prefer concise, evidence-first writing.
- Preserve LaTeX-safe formatting and citation syntax.
Practical rules
1. Avoid conversational fillers and unsupported claims. 2. Keep paragraph scope focused on one core claim. 3. Ensure quantitative claims include context and metrics. 4. Keep citation keys valid and references complete. 5. Keep section transitions explicit and coherent.
Review checklist
- Introduction states problem, gap, and contribution.
- Method and experiment sections are reproducible.
- Results and conclusion align with reported evidence.
- Venue-specific limits are respected.
Ships withagentsociety
AgentSociety 2 is a modern, LLM-native agent simulation platform designed for social science research and experimental design. It provides a flexible framework for creating and managing intelligent agents in simulated environments.
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