Use when targeting IEEE International Conference on Data Mining (ICDM) 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.
SkillScore95Maintained
Automatic
◆Research pack
Last verified 9/2/2026
What it changes
Passed 21 of 22 test cases with the skill, and 6 of 22 without it.
Without skill27%
With skill95%
Checks it passed that the model alone missed
The response rates the fit as High
The output contains [Contribution type]
The output includes the bracketed label [Fit]
and 15 more in the full report
15 fixed6 passed either way1 still failing0 broke
Pass-rate lift
+68 pp
Tokens
+46%
Agent turns
0%
Response time
-23%
Model gemini-3.6-flashMeasured 9/2/2026v121 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 15 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 15
Prompt
Our manuscript presents a novel distributed secondary indexing engine built on top of RocksDB to accelerate transaction log querying in distributed databases. Evaluate whether ICDM is the right target venue and suggest proper routing.
What was checked, without and with the skill
Checked
Without skill
With skill
The response identifies ICDE, SIGMOD, or VLDB as appropriate routing destinations for database system contributions
Passed
Passed
The output includes the line starting with [Re-route suggestion]
Missed
Passed
case-05 · gemini-3.6-flash · 9/2/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md106 linessingle file
IEEE International Conference on Data Mining (ICDM)
Conference positioning
IEEE International Conference on Data Mining (ICDM) is a top computer-science conference venue for data mining algorithms, pattern discovery, graph mining, anomaly detection, and applied analytics. It rewards a data-mining methods paper with careful baselines and defensible discovery claims. 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 ICDM / IEEE International Conference on Data Mining as the target venue.
A manuscript in data mining algorithms 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 algorithms, pattern discovery, graph mining, anomaly detection, and applied 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 ICDM'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 algorithms, pattern discovery, graph mining, anomaly detection, and applied analytics.
Official anchor domain: www.computer.org. Quote annual rules only after opening that source and the current-year CFP/author kit.
Close-neighbor routing guardrail
Route to ICDM when the paper is a data-mining method, pattern-discovery result, or applied
mining study suited to a broad IEEE data-mining audience.
Compare KDD for larger data-mining impact and applied discovery, SDM for
mathematical/statistical rigor, CIKM for information/knowledge management, and
SIGMOD/VLDB/ICDE for database systems.
What distinguishes this venue from its closest siblings
Use when targeting European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 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 AI/data mining.
Use when targeting IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) 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 software analysis.