nsdi-experiments
Use when designing or auditing the evaluation of an NSDI submission — choosing traces, testbeds, and deployment evidence, sizing scale and failure-injection experiments, picking baselines that fight back, and reporting tail behavior so networked-systems reviewers can see where the design wins, loses, and breaks.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill nsdi-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.
# NSDI Experiments NSDI's phrase is "practical evaluation," and its reviewer culture decodes that as: realistic traffic, honest baselines, visible tails, and at least one experiment where the system is *hurt on purpose*. Design the evaluation as a set of questions the paper must answer, then build the smallest experiment matrix that answers them. ## The evidence ladder Climb as high as the project honestly can, and say plainly which rung you are on: 1. **Synthetic microbenchmarks** — isolate mechanism costs. Necessary, never sufficient; a microbenchmark-only evaluation reads as a workshop draft. 2. **Trace-driven or production-derived workloads on a testbed** — the venue's workhorse rung. Provenance of the trace matters as much as its size: say where the call graphs, flow mixes, or arrival processes come from and what was scaled. 3. **Real deployment** — production or long-running operational use. This is operational-track territory and the strongest evidence NSDI recognizes; do not imply it with wording if you are on rung 2 (`nsdi-topic-selection`). ## Questions before benchmarks Write the evaluation section's subsection titles as questions first — *Does the lease mechanism help u
- The evidence ladder
- Questions before benchmarks
- Baselines that fight back
- Tails, variance, and time
- Testbed and trace hygiene
- Budgeting machine time against the two-deadline calendar
- Audit checklist
- Output format
What does the nsdi-experiments skill do?
Use when designing or auditing the evaluation of an NSDI submission — choosing traces, testbeds, and deployment evidence, sizing scale and failure-injection experiments, picking baselines that fight back, and reporting tail behavior so networked-systems reviewers can see where the design wins, loses, and breaks.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill nsdi-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.