Computer science venues reward different contribution shapes. Use this skill to choose a realistic target and adapt the paper's evidence, tone, and framing.
Read First
references/repository-contract.mdreferences/venue-strategy-policy.mdreferences/research-positioning-policy.md
Workflow
- Identify field and subfield: ML, NLP, HCI, systems, SE, security, databases, IR, theory, PL, robotics, graphics, education, or interdisciplinary CS.
- List plausible venues and tracks with deadlines, format, review model, and artifact expectations.
- Compare audience fit, novelty bar, methodological norms, page limits, and risk.
- Map contribution type to venue expectations.
- Record the decision in
docs/venue/venue-strategy.md. - Update paper/research design if venue fit requires stronger baselines, clearer theory, user study, artifact, or threat analysis.
Venue Fit Checks
- Does the venue value this contribution type?
- Are the baselines and datasets credible for this venue?
- Does the paper need artifact evaluation or reproducibility badges?
- Is the novelty claim too incremental for the main track?
- Would a workshop, demo, findings track, or journal be more honest?
Do Not
- Choose a venue only by prestige.
- Ignore track-specific review criteria.
- Reframe after experiments without checking whether evidence still supports the claim.