kdd-topic-selection
Use when deciding whether a project belongs at KDD and in which track — Research vs Applied Data Science vs Datasets and Benchmarks vs AI for Sciences — or whether it routes to ICDM, SDM, WSDM, CIKM, WWW, VLDB, or an ML flagship. Covers the deployment-evidence fork, data-regime framing, and SIGKDD fit signals before writing.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill kdd-topic-selection --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# KDD Topic Selection Use this before any writing. KDD routing is a **two-stage decision** — first whether the work is KDD-shaped at all, then which track — and the second stage is the one teams get wrong, because the Research and ADS tracks want different papers written about the same system. A submission may enter exactly one of them per cycle. ## Stage 1: is it KDD-shaped? KDD rewards contributions where **the data regime is the protagonist**: scale, drift, heterogeneity, noise, sparsity, graph structure, streams, or the friction of real deployment. Signals: - The method's value claim references a property of data (works at 10^9 edges, under drift, with 0.1% labels) rather than only a property of models. - There is a mining/discovery/prediction task with measurable output — not purely a learning-theory statement or a pure systems benchmark. - Someone outside the authors' subfield could use the result on their data. Anti-signals: novelty lives entirely in architecture or loss design (ML flagships); the contribution is query processing or storage (VLDB/SIGMOD); the core is retrieval ranking on web corpora (SIGIR/WSDM/WWW may fit better, though overlap is real). ## Stage 2: the tra
- Stage 1: is it KDD-shaped?
- Stage 2: the track fork
- Decision vignette
- Neighbor-venue routing
- Fit-signal reference
- Timing interacts with routing
- Commit checklist before writing
- Output format
What does the kdd-topic-selection skill do?
Use when deciding whether a project belongs at KDD and in which track — Research vs Applied Data Science vs Datasets and Benchmarks vs AI for Sciences — or whether it routes to ICDM, SDM, WSDM, CIKM, WWW, VLDB, or an ML flagship. Covers the deployment-evidence fork, data-regime framing, and SIGKDD fit signals before writing.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill kdd-topic-selection --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.