Individual skill
kdd-reproducibility
Use when hardening the reproducibility story of a KDD paper, where reproducibility is an explicit decision factor for area chairs. Covers claim-to-evidence tiers for mining pipelines, seeds and splits on large graphs and streams, baseline-tuning disclosure, compute reporting, and honest limits for ADS deployment results.
KDD-Skills/skills/kdd-reproducibility/SKILL.md
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# KDD Reproducibility
Use this before the paper freezes. The KDD 2026 CFP lists **reproducibility of
results** among the factors area chairs weigh in acceptance recommendations — at this
venue it is a scored dimension, not a virtue. The bar is shaped by what KDD papers
claim: pipelines over large, messy, sometimes proprietary data, where the reader must
be able to tell exactly which parts they can rerun, which they can rebuild, and which
they must take on documented trust.
## Three-tier honesty model
Declare every result in the paper as one of:
1. **Rerunnable** — public data, shipped code, fixed seeds; a reader regenerates the