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atc-experiments

Use when designing or auditing the evaluation of an ATC (ACM SIGOPS Annual Technical Conference, formerly USENIX ATC) systems paper — matching evidence to the claim with real testbeds, fair baselines, end-to-end plus microbenchmark results, tail-latency and variance reporting, workload realism, and…

Install / Use

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill atc-experiments

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

87/100

Supported Platforms

Universal

Our assessment of atc-experiments

atc-experiments scores 87/100 on our quality scale, 58th of 109 Finance & Accounting skills we index.

Its SKILL.md is 5.1 KB long, well organised into 9 sections with 2 code examples: a solid amount of guidance for an agent.

With 1,158 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
26/30
Structure
18/20
Description
15/15
Adoption
13/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 21 days ago, so atc-experiments is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands.

Automated pattern scan on 2026-10-06. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

atc-experiments compared with similar skills

All 4 of these similar skills score higher than atc-experiments; compare them before choosing.

SkillScoreStarsUpdatedFormat
atc-experiments (this skill)by brycewang-stanford871.2k21d agoSKILL.md
algorithmic-artby anthropics100177.9k13d agoSKILL.md
pptxby anthropics100177.9k13d agoSKILL.md
designby nextlevelbuilder100130.2k15d agoSKILL.md
ui-ux-pro-maxby nextlevelbuilder100130.2k15d agoSKILL.md

Frequently asked questions

How do I install atc-experiments?
Run npx skills add brycewang-stanford/Awesome-Journal-Skills --skill atc-experiments. The install tabs above show the steps for each supported agent.
Which AI agents does atc-experiments work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is atc-experiments safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is atc-experiments still maintained?
The repository was last updated 21 days ago, so atc-experiments is actively maintained.

name: atc-experiments description: Use when designing or auditing the evaluation of an ATC (ACM SIGOPS Annual Technical Conference, formerly USENIX ATC) systems paper — matching evidence to the claim with real testbeds, fair baselines, end-to-end plus microbenchmark results, tail-latency and variance reporting, workload realism, and honest cost accounting.

ATC Experiments

Match the evidence to the claim. ATC is the systems community's implementation-and-measurement venue: reviewers read for measured behavior on a real system, not asymptotics or accuracy on a dataset. In round two, 3-4 reviewers close to your subarea will open the artifact and probe whether the numbers are end-to-end, fair, and honest about cost. Design the evaluation so their first three objections are already answered.

Match evidence to claim shape

| If your claim is... | The evidence ATC expects | |---|---| | "Faster / lower latency" | End-to-end latency including tails (p99/p999) and throughput at a matched operating point, on a described testbed | | "Lower overhead / cheaper" | The resource cost (CPU, memory, writes, energy) measured, at matched function — not just the headline win | | "Scales" | Measurements across a real range of load/nodes/cores with the scaling curve and where it bends | | "More reliable / correct" | Fault-injection or crash/recovery experiments, not just steady-state runs | | "Useful in practice" (experience) | Production-derived workloads and lessons; what broke and what generalizes |

Real testbeds and workloads

  • Describe the testbed so results are reproducible: CPU/NIC/SSD models, core counts, memory, kernel/OS versions, network topology, and any co-location. A result without its testbed is not a systems result.
  • Use realistic workloads. Production-derived traces, standard benchmarks, or documented generators beat hand-picked inputs. State how the workload was obtained and why it is representative; if it is synthetic, justify the parameters.
  • Warm-up and steady state. Say how you handled cold start, warm-up windows, and measurement duration — systems reviewers know where transient effects hide.

Fair baselines

  • Compare against the strongest reasonable alternative, configured well (a strawman baseline is caught immediately). If you tuned your system, tune the baseline.
  • Compare at a matched cost or operating point: same memory budget, same flash-write budget, same load. An unmatched comparison is the classic systems-reviewer objection.
  • If no baseline exists, say so and use the unmodified system or an ablation of your own design as the reference.

End-to-end plus microbenchmarks

ATC reviewers want both:

  • End-to-end results show the contribution matters for the whole system under a real workload.
  • Microbenchmarks isolate the mechanism, attributing the win (or cost) to your design rather than to unrelated system effects. A paper with only end-to-end numbers cannot explain why; one with only microbenchmarks cannot show it matters.

Tails, variance, and honest reporting

  • Report tail latency (p99, often p999), not just means — the tail is where systems pain lives.
  • Report variance across repeated runs (multiple trials, min/max or CIs). A single run is a data point, not a result.
  • Report the cost beside the gain, at the matched operating point (see atc-writing-style). A win with an unstated cost reads as a hidden weakness.
  • State negative or neutral regions honestly — "where the working set fits, our policy neither helps nor hurts" builds more trust than a uniformly rosy curve.

Provenance you cannot reconstruct later

Pin these at collection time — they cannot be recovered at the deadline (see atc-reproducibility):

[Hardware]   CPU/NIC/SSD models, core/memory counts, firmware where it matters
[Software]   kernel/OS versions, library and compiler versions, config flags
[Workload]   trace source and date, generator version and seeds, request mix
[Method]     warm-up window, measurement duration, number of runs, aggregation
[Code]       commit SHAs for the system and every baseline

Special cases

  • Concurrency/nondeterminism: report the distribution and the scheduling/affinity settings, not a lucky run.
  • Energy/power claims: name the measurement instrument and boundary (wall vs. component).
  • Security/isolation claims: state the threat model and what the measurement does and does not cover.
  • Experience papers: the "evaluation" is the deployment itself — scale, duration, incidents, and transferable lessons; ATC's Deployed Systems lane values this even without a new mechanism.

Output format

[Claim -> evidence] each claim mapped to the experiment that supports it; gaps flagged
[Testbed] hardware/software/workload described enough to reproduce? yes/no
[Baselines] strongest alternative, well-configured, at a matched operating point? yes/no
[Depth] end-to-end AND microbenchmarks present? tails + variance reported?
[Honesty] costs reported beside gains? neutral/negative regions stated?
[Provenance] hardware/software/workload/method/code pinned at collection time? yes/no

Related Skills

View on GitHub
GitHub Stars1.2k
CategoryFinance
Updated21d ago
Forks153

Languages

Stata

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions