Agent Skills
› brycewang-stanford/Awesome-Journal-Skills
› acm-sigkdd-conference-on-knowledge-discovery-and-data-mining
acm-sigkdd-conference-on-knowledge-discovery-and-data-mining
GitHub针对ACM SIGKDD会议投稿的适配与策略工具。用于评估论文契合度、重构叙事、检查证据缺口及规避拒稿风险，涵盖数据挖掘领域的新颖性、规模与影响力要求，辅助作者进行精准的 venue selection 和 submission strategy 制定。
触发场景
作者指定KDD为目标投稿 venue
数据挖掘类手稿需进行会议适配性评估
需将期刊或arXiv风格重构为CS会议叙事
需要诊断匿名性、工件或反驳策略
安装
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acm-sigkdd-conference-on-knowledge-discovery-and-data-mining -g -y
SKILL.md
Frontmatter
{
"name": "acm-sigkdd-conference-on-knowledge-discovery-and-data-mining",
"description": "Use when targeting ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) or deciding whether a computer-science manuscript fits this venue. Encodes conference fit, framing, evidence bar, submission-cycle checks, rebuttal posture, and desk-reject risks for data mining."
}
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
Conference positioning
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) is a top computer-science conference venue for data mining, applied data science, scalable learning, knowledge discovery, and impact-oriented analytics. It rewards a data-mining paper with novelty, scale, reproducibility, and clear real-world or scientific payoff. Treat this skill as a fit / venue-selection / re-framing tool for conference submission strategy, not as a substitute for the current year's CFP, author kit, ethics policy, or submission portal.
Because CS conferences change deadlines, templates, page limits, review workflow, artifact rules, AI-use policy, and rebuttal formats every cycle, always verify the live official instructions before making a submission-ready recommendation. Start from the official source anchor recorded for this venue in ../../resources/conference-roster.md and ../../resources/official-source-map.md.
When to trigger
- The author names KDD / ACM SIGKDD Conference on Knowledge Discovery and Data Mining as the target venue.
- A manuscript in data mining needs a conference-fit read before being formatted or submitted.
- The paper must be re-framed from journal style or arXiv style into a selective CS conference narrative.
- The author needs an evidence-gap, anonymity, artifact, rebuttal, or re-routing diagnosis for this venue.
Scope & topic fit
- Core fit: data mining, applied data science, scalable learning, knowledge discovery, and impact-oriented analytics.
- Best submissions make a precise contribution type visible: algorithm, theorem, system, dataset, benchmark, empirical finding, design artifact, tool, or socio-technical analysis.
- The paper should explain why the result matters to KDD's reviewers, not just why it is interesting to the authors' lab or product context.
- Position related work against the most recent conference-cycle papers in this venue and its closest siblings; stale comparisons are a common early-review weakness.
- If the contribution is interdisciplinary, state which part is CS research and which part is domain evidence.
Venue-specific calibration
- Reviewer lens: Read reviewers as data-centric ML and discovery specialists. Novelty should appear in mining method, scale, discovery validity, or applied impact.
- Contribution hook to foreground: the venue-specific contribution bar.
- Scope vocabulary to use naturally in the abstract and introduction: data mining, applied data science, scalable learning, knowledge discovery, and impact-oriented analytics.
- Distinctive fingerprint for reviewer calibration: data, mining, applied, scalable, learning, knowledge, discovery, impact-oriented, analytics, venue-specific, contribution.
- Official anchor domain: kdd.org. Quote annual rules only after opening that source and the current-year CFP/author kit.
Close-neighbor routing guardrail
- Use this profile only when the manuscript's central contribution is genuinely in data mining and the author can say why KDD reviewers are the primary audience, not merely a convenient deadline.
- Closest roster neighbors to compare before final routing:
conference-on-health-inference-and- learning(CHIL),machine-learning-for-health(ML4H),ieee-international-conference-on- data-mining(ICDM),siam-international-conference-on-data-mining(SDM). Break ties by contribution type, evidence shape, reviewer community, and the current official CFP from kdd.org.
KDD-specific routing detail
- Prefer KDD when the paper contributes a scalable discovery method, applied data-science system, mining benchmark, knowledge-discovery insight, or impact-oriented analytics result that survives strong baselines and real data complexity.
- Use ICAPS only when planning, scheduling, temporal reasoning, or planner search is the central contribution; KDD reviewers expect discovery, prediction, ranking, graph/mining, or data-centric evidence rather than solver engineering alone.
- Compare with WWW/WSDM/SIGIR for web/search/retrieval emphasis, ICDM/SDM for data-mining scope with different community fit, and ML flagships when the novelty is a general learning method rather than data-discovery or applied-scale evidence.
Method & evidence bar
- Compare against current strong baselines and explain exactly what changes in the algorithm, objective, data, or inference procedure.
- Report ablations that isolate the claimed mechanism; do not rely on aggregate benchmark wins alone.
- Document data, compute, hyperparameters, model selection, and failure cases so the result can be reviewed as science rather than demo output.
- For KDD, the evidence must support the venue-specific signature: a data-mining paper with novelty, scale, reproducibility, and clear real-world or scientific payoff.
- Include limitations, negative results, compute/resource reporting, data provenance, and ethics details when they affect the claim.
Structure & house style
- Frame the contribution as a reusable idea: method, theory, benchmark, dataset, system, or socio-technical finding.
- Separate main claims from exploratory results; reviewers at top AI venues punish overclaiming and hidden cherry-picking.
- Use the current official template exactly; do not guess page limits, font sizes, supplement rules, anonymity exceptions, or camera-ready requirements from old cycles.
- The introduction should answer: problem, why now, what is new, why this venue, and what evidence proves the claim.
- Put the strongest result in the main paper, not only in the appendix or supplement; reviewers should not have to reconstruct the contribution.
Official-cycle checklist
- Open the live official venue page: https://kdd.org/
- Re-check the current cycle's CFP, author kit, submission system, abstract/paper deadlines, page limits, supplementary-material rules, anonymity policy, dual-submission policy, ethics policy, AI-use policy, artifact/code/data expectations, rebuttal/author-response format, and camera-ready requirements.
- Confirm the review workflow and portal: OpenReview / CMT / HotCRP / PCS / START or society portal, as specified for the current cycle.
- Check whether accepted papers require in-person presentation, separate registration, artifact badges, proceedings copyright, or post-acceptance release forms.
- If the live official instructions conflict with this skill, the official instructions win.
Pre-submission self-check
- One sentence states why this manuscript belongs at KDD, using the venue's scope rather than generic "top conference" language.
- The claim is calibrated to the evidence: no broader than the datasets, proofs, systems, user studies, deployments, or threat model support.
- Related work includes the nearest current-cycle data mining papers and explains the technical delta.
- The paper satisfies the current official template, anonymity, ethics, artifact, and rebuttal requirements.
- The main paper is self-contained enough for reviewers to evaluate novelty and correctness without hunting through external links.
Common desk-reject triggers
- Leaderboard-only novelty with weak explanation of why the method works.
- Unclear data contamination, missing baselines, or evaluation that cannot be reproduced.
- Claims about safety, fairness, health, or society without matching evidence and limitations.
- Formatting, anonymity, dual-submission, external-link, or supplement violations under the current-year policy.
- A contribution framed for a neighboring field while giving KDD reviewers too little technical or empirical substance.
Re-routing decision
If the paper misses KDD's bar, compare against neural-information-processing-systems / international-conference-on-machine-learning / international-conference-on-learning-representations / aaai-conference-on-artificial-intelligence. Re-route based on contribution type, not prestige: theory to a theory venue, systems to a systems venue, application-heavy work to a domain venue, and early ideas to workshops or shorter tracks when the official CFP supports them.
Output format
[Fit] High / Medium / Low (one-line reason)
[Target] ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
[Contribution type] algorithm / theory / system / dataset / benchmark / empirical / design / security / other
[Main evidence gap] <single most important missing proof, experiment, study, artifact, or policy check>
[Official items to re-check] CFP / author kit / deadline / format / anonymity / ethics / AI-use / artifact / rebuttal / camera-ready
[Top rejection risk] <venue-specific risk>
[Re-route suggestion] <better-matched conference or journal if not a fit>
版本历史
- 1839142 当前 2026-07-05 12:40
