sigcomm-experiments
Use when designing or auditing ACM SIGCOMM experiments — climbing the evidence ladder from microbenchmarks to testbed and trace replay to deployment, choosing baselines that match the deployed state of the art, reporting tail percentiles and variance over repeated trials, mapping break points, and holding the fair variable fixed.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigcomm-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.
# SIGCOMM Experiments Use this before submission when the evaluation is not yet locked. At SIGCOMM the experiments are where a mechanism is believed or disbelieved, and the reviewer culture reads a setup for realism, fairness, and honesty about where the mechanism breaks. ## Experiment audit - Map each performance claim to a specific microbenchmark, testbed run, trace replay, deployment measuremen
What does the sigcomm-experiments skill do?
Use when designing or auditing ACM SIGCOMM experiments — climbing the evidence ladder from microbenchmarks to testbed and trace replay to deployment, choosing baselines that match the deployed state of the art, reporting tail percentiles and variance over repeated trials, mapping break points, and holding the fair variable fixed.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigcomm-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.