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.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill kdd-reproducibility --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 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 number. This should cover the headline comparison table. 2. **Rebuildable** — method fully specified (pseudocode + hyperparameters + data schema), but data is proprietary or too large; a reader can reconstruct the pipeline on their own data. 3. **Attested** — post-launch/production measurements that no outsider can repeat; reproducibility here means the *measurement protocol* is fully specified. An ADS paper is usually tier 2-3; a Research Track paper claiming tier 1 while shipping tier 2 is what burns trust in review. ## Mining-pipeline disclosure checklist
- Three-tier honesty model
- Mining-pipeline disclosure checklist
- Config-as-artifact discipline
- Stochasticity on large data
- ADS attested-results protocol
- Vignette: the tier audit that changed a claim
- Pre-freeze reproducibility gate
- Output format
What does the kdd-reproducibility skill do?
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.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill kdd-reproducibility --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.