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Cs ai conference workflow

Skill brycewang-stanford/Awesome-Journal-Skills/Computer-Science-Conference-Skills/skills/cs-ai-conference-workflow

Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. | 覆盖主流期刊的 Claude Code/Codex 期刊技能包,从选题、识别策略到表格规范与审稿回复全流程,助你快速发论文。

Install
npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill cs-ai-conference-workflow

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What its author says it does

Copied from the file, not written here

Use when deciding which computer-science or AI conference skill to invoke next, comparing fit across the 155-conference CS roadmap, or routing an AI/ML/CS manuscript before venue-specific re-framing.

SKILL.md

7.9 KB, ~1.8k tokens by cl100k_base, as published. Nobody here has run it

CS/AI conference workflow (cs-ai-conference-workflow)

Purpose

This is the router for the computer-science conference pack. It puts AI conferences first, then routes by contribution type across ML, data mining, vision, NLP, robotics, HCI, systems, security, software engineering, programming languages, databases, and theory. It does not replace a single-conference profile; it selects the right profile and forces an official-cycle check before submission.

Resources to Load When Needed

  • If the target conference family is unclear, read ../../resources/worked-examples/venue-routing.md.
  • If two sibling conferences look plausible, read ../../resources/exemplars/selection-patterns.md. This file now carries the high-confusion sibling contrasts surfaced by clone-audit review (robotics, graphics/vision, NLP chapters, systems/networking, security, SE/PL/theory).
  • Before submission-ready advice, use ../../resources/conference-roster.md and ../../resources/official-source-map.md to open the current official CFP, author kit, and submission policy for the chosen conference.

Ask six things first

  1. Contribution type: algorithm, theory, system, dataset, benchmark, empirical study, user study, security finding, programming-language result, database system, or application.
  2. Evidence shape: proof, benchmark, ablation, artifact, deployment, user study, field study, exploit/defense, theorem, or system measurement.
  3. Audience: broad AI, subfield specialists, systems builders, security reviewers, HCI/design researchers, theory community, or domain users.
  4. Review constraints: double-blind/anonymity, OpenReview visibility, rebuttal length, artifact policy, ethics, AI-use disclosure, and supplementary-material rules.
  5. Cycle risk: deadline, page limit, author registration, conflict declarations, dual-submission policy, and camera-ready obligations.
  6. Fallback path: sibling conference, journal, workshop, findings track, or arXiv-only revision.

Quick routing

Manuscript signaturePrefer skills
AI/ML firstneural-information-processing-systems / international-conference-on-machine-learning / international-conference-on-learning-representations / aaai-conference-on-artificial-intelligence / international-joint-conference-on-artificial-intelligence
Data mining and web AIacm-sigkdd-conference-on-knowledge-discovery-and-data-mining / the-web-conference / acm-international-conference-on-web-search-and-data-mining / acm-conference-on-recommender-systems
Vision and multimodal mediacomputer-vision-and-pattern-recognition / international-conference-on-computer-vision / european-conference-on-computer-vision / acm-international-conference-on-multimedia
NLP, speech, and IRannual-meeting-of-the-association-for-computational-linguistics / conference-on-empirical-methods-in-natural-language-processing / interspeech / acm-sigir-conference-on-research-and-development-in-information-retrieval
Robotics and embodied AIieee-international-conference-on-robotics-and-automation / ieee-rsj-international-conference-on-intelligent-robots-and-systems / robotics-science-and-systems / conference-on-robot-learning
HCI and visualizationacm-chi-conference-on-human-factors-in-computing-systems / acm-symposium-on-user-interface-software-and-technology / acm-conference-on-computer-supported-cooperative-work-and-social-computing / ieee-visualization-conference
Systems, networking, architecture, and HPCacm-symposium-on-operating-systems-principles / usenix-symposium-on-operating-systems-design-and-implementation / acm-sigcomm / international-symposium-on-computer-architecture
Security and privacyieee-symposium-on-security-and-privacy / usenix-security-symposium / acm-conference-on-computer-and-communications-security / network-and-distributed-system-security-symposium
Software engineering, PL, and formal methodsinternational-conference-on-software-engineering / acm-international-conference-on-the-foundations-of-software-engineering / acm-sigplan-conference-on-programming-language-design-and-implementation / acm-sigplan-symposium-on-principles-of-programming-languages
Databases and theoryacm-sigmod-international-conference-on-management-of-data / international-conference-on-very-large-data-bases / acm-symposium-on-theory-of-computing / ieee-symposium-on-foundations-of-computer-science

Sibling-Conference Disambiguation

Confusable targetsDecision rule
NeurIPS vs ICML vs ICLRUse NeurIPS for broad ML/AI reach, ICML for machine-learning method/theory discipline, and ICLR for representation/deep-learning/open-review fit.
KDD vs ICDM vs SDMKDD leans data-mining impact and applied discovery, ICDM broad IEEE data-mining methods, SDM mathematical/statistical data-mining rigor.
CVPR vs ICCV vs ECCVAll require a vision contribution; current cycle, scope, and reviewer community decide, not acronym prestige alone.
ACL/EMNLP vs NAACL/EACLSeparate core NLP method, empirical analysis, resource construction, and chapter-cycle fit before choosing.
CHI vs UIST vs CSCW vs IUI vs VISUser study, UI systems, social computing, intelligent-interface, and visualization claims need different evidence.
S&P vs USENIX Security vs CCS vs NDSSPick by threat model, attack/defense evidence, ethics posture, systems/security community, and current CFP scope.
ICSE/FSE vs ASE vs ISSTA vs SANER/ICSMEBroad SE, automation, testing/analysis, reengineering, and maintenance-history papers are not interchangeable.
SIGMOD vs VLDB vs ICDEData-management systems, PVLDB-style database research, and IEEE data-engineering work have different submission mechanics; verify the current cycle.

Decision rules

  • AI first: if the manuscript is fundamentally about machine learning, language, vision, data mining, agents, or responsible AI, start in the AI/ML rows before considering general CS venues.
  • Contribution type beats prestige: a theorem paper belongs in COLT/STOC/FOCS/LICS-style venues; a built system belongs in SOSP/OSDI/NSDI/SIGCOMM/ASPLOS-style venues; a user-facing AI interface belongs in CHI/IUI/CSCW rather than only NeurIPS.
  • Evidence must match venue culture: top AI wants strong baselines and ablations; systems wants artifacts and workloads; security wants threat models and ethics; HCI wants study design and participant context; theory wants complete proofs.
  • Official-cycle requirements are volatile: always invoke the single-conference skill and re-check the current CFP/author kit before advising submission.
  • These are conferences, not journals (verified 2026-06-22): no venue here has a standing editor-in-chief — its leadership is the per-edition Program/General Chairs, who rotate yearly, so look them up on the current-cycle committees page rather than assuming a name. None charges an article-processing fee; the cost model is registration plus open-access proceedings (PMLR, OpenReview, ACM/IEEE DL). Treat any "fee" question as a registration or narrow extra-page/submission-volume question, not a journal APC.
  • Rebuttal posture matters: prepare concise, evidence-based responses and never reveal identity or add new off-policy material during author response.

Output format

[Top conference skill] <skill-name>
[Alt 1] <skill-name> (reason)
[Alt 2] <skill-name> (reason)
[Do not submit to] <venue> (one-line mismatch reason)
[Biggest current gap] novelty / evidence / proof / artifact / user study / ethics / format / official requirements
[Next step] invoke <skill-name> for single-venue fit and current-cycle checks

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.