conext-experiments
Use when designing or auditing the evaluation of an ACM CoNEXT paper — matching evidence to claim shape with real testbeds and deployments, honest and tuned baselines, measurement statistics and uncertainty, trace and config provenance, and contamination-aware ablations for ML-for-networking work.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill conext-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.
# CoNEXT Experiments Build the evaluation a networking reviewer will actually interrogate. CoNEXT's evidence culture is **systems-and-measurement**: claims are backed on the **real target platform** — a testbed, deployment, or trace — with honest baselines and reported uncertainty, not simulation standing in for hardware or a single number with no variance. Because a one-shot major revision is decided on a list of **minimum necessary changes**, an evaluation gap you leave now often becomes a mandatory fix under a tight window later. ## Match evidence to claim shape | Claim shape | Evidence CoNEXT expects | |---|---| | A mechanism is faster/cheaper on real hardware | A run on the **real target** (switch, NIC, kernel, testbed) under its real constraints, vs. a tuned baseline, with effect sizes | | A phenomenon exists in the wild | A **measurement campaign** with documented vantage points, capture dates, and a reproducible extraction methodology | | An architecture scales | **Scalability evidence** (real deployment or faithful emulation) across the relevant range, not a point claim | | An operator intervention helps | Evidence at **operationally relevant scale**, with the counterfactu
- Match evidence to claim shape
- Real testbeds and deployments over simulation
- Honest, tuned baselines
- Measurement statistics and uncertainty
- Trace and config provenance (pin it now)
- Contamination-aware ML-for-networking ablations
- Pre-submission evaluation audit
- Output format
What does the conext-experiments skill do?
Use when designing or auditing the evaluation of an ACM CoNEXT paper — matching evidence to claim shape with real testbeds and deployments, honest and tuned baselines, measurement statistics and uncertainty, trace and config provenance, and contamination-aware ablations for ML-for-networking work.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill conext-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 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.