Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Use when reasoning about the ICDM (IEEE International Conference on Data Mining) review machinery - the triple-blind mechanics, the Accept / Accept-as-Short / Reject outcome space, the mixed data-mining reviewer pool, PC Co-Chair decision-making, the traditional no-rebuttal posture, and how to read an ICDM decision packet.
Use when deciding whether a project is a strong ICDM (IEEE International Conference on Data Mining) fit, choosing among its Research, Applied, and Blue Sky tracks, and comparing ICDM with KDD, SDM, CIKM, WSDM, WWW, ICDE, or the ML flagships by contribution type, sponsor community, and the data-mining routing calendar seen from ICDM's June deadline.
Use when deciding what counts as evidence for an ICDT (International Conference on Database Theory) result — matching-bound complexity analysis as the primary evidence, worked examples and counterexamples that establish separations, and, only for papers with an algorithmic contribution, a proportional and honestly-scoped experimental evaluation that does not pretend to be the contribution.
Use when reasoning about how an ICDT (International Conference on Database Theory) submission is evaluated, covering the two-submission-cycle model, the first-cycle revision (Accept / Revise / Reject) decision, anonymous review since 2024, the cross-cycle resubmission restriction, proof-correctness scrutiny by a database-theory PC, and how ICDT's process differs from PODS and from the co-located EDBT systems track.
Use when deciding whether a database-theory project belongs at ICDT (International Conference on Database Theory) or should be routed to PODS, the co-located EDBT systems track, a pure-TCS venue (LICS, ICALP, STACS), or a journal (ACM TODS, LMCS, TheoretiCS), and when distinguishing ICDT from its sibling database-theory flagship PODS by calendar, publisher, and community.
Use when positioning an ICLR paper against prior work, concurrent OpenReview submissions, arXiv papers, benchmark lineages, and adjacent learning-representation claims. Use when a reviewer cites a paper you missed, when a public comment disputes your novelty, or when separating "shares a component with" from "solves the same representation-learning problem" so the claim survives permanent public scrutiny.
Use when deciding whether a project is a strong ICLR submission, should be reframed for ICLR, or should be routed to NeurIPS, ICML, AAAI, AISTATS, ACL, CVPR, KDD, or another venue. Use when a project lacks a clear representation-learning insight, when an application result needs a learning contribution to fit ICLR, or when weighing ICLR's deep-learning center of gravity against a better-matched venue.
Use when explaining or diagnosing the ICML review process, including OpenReview, reciprocal reviewing, reviewer/AC behavior, review dimensions, author response, one-round discussion, LLM-review policy, ethics flags, and public review records.
Use when deciding whether a manuscript fits ICML, choosing the main research track versus the ICML Position Papers track or another venue (NeurIPS, ICLR, AISTATS, UAI, COLT, MLSys, TMLR, JMLR), or rerouting an ML paper based on its contribution type, strength of evidence, theory-versus-empirical balance, and interest to the broad ICML machine-learning community. Use before committing effort to an ICML submission.
Use when designing or auditing the experimental section of an ICRA paper — real-robot versus simulation-only evidence, trial counts and success-rate reporting, task distributions and resets, baseline fairness on shared hardware, sim-to-real transfer claims, failure-mode analysis, and the statistics robotics reviewers actually expect.