webconf-experiments
Use when designing or auditing the empirical section of a Web Conference (WWW) paper — matching evidence to the claim's scale, choosing datasets with provenance and freshness, blocking temporal and popularity leakage, running honest baselines from the sibling circuit, and deciding when live-platform or user-study evidence is required.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill webconf-experiments --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.
# Web Conference Experiments The empirical bar at this venue is not "more datasets" — it is **evidence whose scale, freshness, and realism match the claim**. A method claiming web-scale efficiency must show the scaling curve; a measurement claiming platform-general behavior must show more than one platform; a system claiming deployability must show cost under realistic load. Audit the claim-evidence match before adding anything. ## Claim-to-evidence contract | Claim type | Minimum honest evidence | Habitual shortfall | |---|---|---| | "Outperforms" (quality) | Tuned recent baselines, repeated runs, variance, significance | Untuned baselines from 3-year-old code | | "Scales" (efficiency) | Cost curves across ≥2 orders of magnitude, hardware stated | One big-dataset wall-clock number | | "Generalizes" (external validity) | ≥2 platforms/domains or a stated single-platform scope | Silent single-platform universality | | "Measures" (phenomenon) | Construct definition, sampling frame, bot/spam handling, error bars | Convenience crawl treated as census | | "Deploys" (system) | Load, latency percentiles, failure behavior; A/B where claimed | Demo-grade throughput on toy traffic | ## Datase
- Claim-to-evidence contract
- Dataset selection with provenance
- Leakage: the venue's most-caught methodological bug
- Baselines and ablations
- Statistics that survive review
- When offline evidence is not enough
- Predictable objections and their cheap preemptions
- Audit checklist
- Output format
What does the webconf-experiments skill do?
Use when designing or auditing the empirical section of a Web Conference (WWW) paper — matching evidence to the claim's scale, choosing datasets with provenance and freshness, blocking temporal and popularity leakage, running honest baselines from the sibling circuit, and deciding when live-platform or user-study evidence is required.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill webconf-experiments --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 984 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.