vldb-topic-selection
Use when deciding whether a project belongs at VLDB and in which PVLDB category, applying the data-management-primitive test, choosing among Regular, EA&B, Scalable Data Science, and Vision papers, and routing against SIGMOD, ICDE, CIDR, EDBT, PODS, KDD, systems venues, and The VLDB Journal.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill vldb-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.
# VLDB Topic Selection Use this before a line is written. Two decisions hide in "let's send it to VLDB": whether the work is a data-management contribution at all, and which PVLDB category gives it the friendliest reviewer expectations. ## The primitive test VLDB rewards work whose core object is a **data-management primitive**: storage layout, index, query optimization or execution, transaction and consistency machinery, data integration and cleaning, streaming state, or the data infrastructure under ML. Two probes: - Strip the application narrative. Is what remains a reusable mechanism for managing data at scale? If what remains is a model architecture or an application result, the primitive is missing. - Would the evaluation chapter naturally measure throughput, latency, scalability, or result quality on data systems? If the natural evaluation is task accuracy alone, an ML or applied venue fits better. ## Category routing inside PVLDB | Your situation | Category | Watch out | |---|---|---| | New mechanism + built system + systems evidence | Regular Research (12 pp) | The default; full evaluation burden | | Rigorous measurement of existing systems, no new system | EA&B (12 pp) |
- The primitive test
- Category routing inside PVLDB
- Neighborhood routing
- Commitment checklist
- Re-route triggers mid-project
- Output format
What does the vldb-topic-selection skill do?
Use when deciding whether a project belongs at VLDB and in which PVLDB category, applying the data-management-primitive test, choosing among Regular, EA&B, Scalable Data Science, and Vision papers, and routing against SIGMOD, ICDE, CIDR, EDBT, PODS, KDD, systems venues, and The VLDB Journal.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill vldb-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.