Individual skill

aaai-writing-style

Use when revising an AAAI manuscript for broad-AI-audience fit, a first-page contribution statement legible to non-specialist Phase-1 reviewers, two-column readability, concise novelty claims, reproducibility-checklist alignment, hedged limitations and ethics, and policy-aware framing of AI-system and capability claims.

AAAI-Skills/skills/aaai-writing-style/SKILL.md

Read pinned source

Source commit 932eb23331721b16e967c59f093781be1928b498

What was checked

Matches the observed repository HEAD
Yes
Scanner rules are current
Yes
Repository scan complete
No
Skill scan complete
Yes
Observed behavior
Network or broad shell
Last checked
Nov 9, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings)
1,405
Repository last updated
Sep 27, 2026
License
MIT

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files
Read excerpt

# AAAI Writing Style

Use this to make a technically sound draft readable to a broad AI program committee. AAAI rewards
clear AI contribution, not only subfield-specific benchmark wins.

## AAAI framing

- State the AI problem, the new capability or insight, and the evidence in the first page.
- Make clear whether the contribution is method, theory, system, benchmark, dataset, evaluation,
  human-AI interaction, social impact, or alignment.
- Explain why the result matters outside a single dataset or implementation.
- Keep claims aligned with the reproducibility checklist and supplementary evidence.
- Discuss limitations and ethical considerations when the method affects people, safety, privacy,
  fairness, security, or social impact.
Back to top