Install
$ agentstack add skill-brycewang-stanford-awesome-journal-skills-aaai-related-work ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
AAAI Related Work
Use this to make the novelty claim robust under AAAI's broad AI review. The related-work section must help reviewers distinguish the paper from both archival work and contemporaneous non-archival work.
Positioning checks
- Identify the closest archival AI papers and current arXiv/workshop work.
- Separate method novelty, task novelty, evaluation novelty, and system integration novelty.
- Cite contemporaneous non-archival work carefully when it affects priority or reviewer
expectations.
- Do not submit substantially similar work to multiple archival venues at the same time.
- Explain how the paper differs from AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors in assumptions,
evidence, scope, and contribution.
- Avoid using AI systems as citable scientific sources under AAAI policy.
Novelty paragraph
Use this structure:
Closest prior work solves under .
It does not address .
This paper contributes and verifies it through .
The claim is limited to .
Positioning across AAAI's breadth
AAAI spans search, planning, knowledge representation, constraint satisfaction, multi-agent systems, learning, NLP, vision, and robotics, so the closest prior work may live in a subfield your reviewer does not. Make the contrast explicit for a non-specialist instead of assuming shared background.
| Neighbor venue | Reviewer expectation | Differentiation to spell out | | --- | --- | --- | | IJCAI | broad-AI overlap | what your result adds beyond their framing | | NeurIPS/ICML | ML method or theory depth | why AAAI breadth, not just a benchmark gain | | ICLR | representation-learning lens | non-learning mechanism or guarantee you contribute | | AAAI prior years | incremental-track suspicion | the new assumption, evidence, or scope |
Reviewer-pushback patterns
- "This looks concurrent with arXiv paper Y." Fix: cite Y, state it is non-archival and contemporaneous,
and name the specific setting or evidence you add; do not bury or ignore it.
- "Isn't this the same as your workshop paper?" Fix: clarify the archival delta and confirm no
substantially similar work is under review elsewhere, satisfying the dual-submission rule.
- "Citation looks AI-generated." Fix: verify every reference against a real source; AAAI policy bars
AI systems as citable scientific sources and hallucinated citations are a credibility risk.
Worked vignette
A reasoning-over-knowledge-graphs paper sits near both a KR archival line and a recent NeurIPS embedding paper. Using the axes: against KR work the difference is evidence (learned vs. hand-built rules); against the NeurIPS neighbor it is scope (logical soundness, not just link prediction). One contemporaneous arXiv preprint is cited as non-archival with a one-line delta, and the dual-submission box is checked clean.
Output format
[Closest work]
[Difference axis] problem / method / theory / data / evaluation / system / impact
[Must-cite items]
[Multiple-submission risk] none / clarify / withdraw / reroute
[Revision text]
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: brycewang-stanford
- Source: brycewang-stanford/Awesome-Journal-Skills
- License: MIT
- Homepage: https://www.copaper.ai
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.