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aaai-related-work

Agent skill frombrycewang-stanford/Awesome-Journal-Skills

Use when positioning an AAAI paper's novelty against archival work, contemporaneous arXiv or workshop papers, and AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors across the broad AI scope, while staying inside AAAI's dual-submission and AI-as-source policy constraints and writing a related-work section legible to non-specialist reviewers.

How to install aaai-related-work

install aaai-related-work
# install the Awesome-Journal-Skills pack with the skills CLI
npx skills add brycewang-stanford/Awesome-Journal-Skills

# or add only this skill to Claude Code: copy the folder

AAAI-Skills/skills/aaai-related-work/

# into

~/.claude/skills/aaai-related-work/ (all projects)

.claude/skills/aaai-related-work/ (one project)

AAAI-Skills/skills/aaai-related-work/SKILL.md

MITFull file
1# AAAI Related Work
2
3Use this to make the novelty claim robust under AAAI's broad AI review. The related-work section
4must help reviewers distinguish the paper from both archival work and contemporaneous non-archival
5work.
6
7## Positioning checks
8
9- Identify the closest archival AI papers and current arXiv/workshop work.
10- Separate method novelty, task novelty, evaluation novelty, and system integration novelty.
11- Cite contemporaneous non-archival work carefully when it affects priority or reviewer
12 expectations.
13- Do not submit substantially similar work to multiple archival venues at the same time.
14- Explain how the paper differs from AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors in assumptions,
15 evidence, scope, and contribution.
16- Avoid using AI systems as citable scientific sources under AAAI policy.
17
18## Novelty paragraph
19
20Use this structure:
21
22```text
23Closest prior work solves <problem> under <assumptions>.
24It does not address <specific missing setting/mechanism/evidence>.
25This paper contributes <new item> and verifies it through <evidence>.
26The claim is limited to <scope>.
27```
28
29## Positioning across AAAI's breadth
30
31AAAI spans search, planning, knowledge representation, constraint satisfaction, multi-agent systems,
32learning, NLP, vision, and robotics, so the closest prior work may live in a subfield your reviewer
33does not. Make the contrast explicit for a non-specialist instead of assuming shared background.
34…

Excerpt shown under the repository's MIT license.

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