socc-experiments
Use when designing or auditing ACM SoCC evaluations, covering real or realistic deployments over simulation, production or representative workloads and traces, tail-latency and cost as first-class metrics, fair tuned baselines, scale and multi-tenancy behavior, reproducible measurement pipelines, and matching evidence to the shape of each cloud claim.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill socc-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.
# SoCC Experiments Use this before submission when the evaluation is not yet locked. SoCC reviewers come from **both SIGMOD and SIGOPS**, and the evaluation is where a good cloud idea is won or lost. The organizing principle is **measured evidence proportional to the claim** — the study must test the thing the paper asserts, on a system and workloads a skeptic from either community would accept, with **tail latency and cost** reported, not just the mean. ## Evaluation audit - **Prefer a real or realistic deployment over simulation.** A cloud claim about a system needs the system running on a testbed or cluster; simulation alone invites the "does it hold on real hardware?" reject. Where full scale is impossible, run a faithful scaled deployment and say so. - **Use production or representative workloads/traces,** with stated provenance and selection, and release the replay harness. Synthetic-only microbenchmarks invite the "does this reflect real load?" objection. - **Report tail and cost, not just averages.** p95/p99 (p99.9 where it bites), the latency distribution, and a concrete cost model (instance-seconds or $) are the outcomes operators are billed against. - **Choose fair basel
- Evaluation audit
- Claim-to-evidence design table
- Tail, cost, and scale floor
- Workload and trace provenance floor
- Vignette: evaluating a storage QoS mechanism
- Statistical and measurement reporting floor
- Output format
What does the socc-experiments skill do?
Use when designing or auditing ACM SoCC evaluations, covering real or realistic deployments over simulation, production or representative workloads and traces, tail-latency and cost as first-class metrics, fair tuned baselines, scale and multi-tenancy behavior, reproducible measurement pipelines, and matching evidence to the shape of each cloud claim.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill socc-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.