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Imc experiments

Skill brycewang-stanford/Awesome-Journal-Skills/IMC-Skills/skills/imc-experiments

Use when designing or auditing ACM IMC measurements, covering representative vantage points, honest ground truth, longitudinal design for a moving Internet, safe and ethical active measurement, coverage-bias quantification, provenance pinning, and matching the measurement to the shape of each claim about the real Internet.From its SKILL.md

Install
npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill imc-experiments

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

SKILL.md

5.3 KB, ~1.1k tokens by cl100k_base, as published. Nobody here has run it

IMC Measurements

Use this before submission when the measurement design is not yet locked. IMC reviewers are measurement empiricists; the study is where a good question is won or lost. The organizing principle is evidence proportional to a claim about the real Internet — the measurement must observe the thing the paper asserts, from vantage points and over a period a skeptic would accept, and it must be collected safely and ethically.

Design audit

  • Match the measurement to the claim shape. A claim about reachability in the wild needs many real vantage points; a claim about deployment needs coverage of the population; a claim about behavior over time needs a dated longitudinal window; a claim about security needs validated ground truth. A snapshot from two hosts cannot support a wild-Internet claim.
  • Choose vantage points for representativeness, not convenience. Name locations, ASes, and probe types; argue why they support the claim; and quantify the coverage bias you cannot remove (e.g., volunteer probes over-representing certain regions).
  • Get ground truth right. For detection/labeling claims, state where the labels come from and validate a subsample against an independent source; report the residual error.
  • Design for a moving Internet. Instrument for churn, diurnal effects, load-balancing, CDN and anycast behavior, and path changes. Decide before collecting what a change over time would mean.
  • Make active measurement safe and ethical (see the safety block) — this is inseparable from the design at IMC, not a compliance afterthought.
  • Pin provenance so the measurement can be re-run as a method and its data reproduced as an analysis (imc-reproducibility).

Claim-to-evidence design table

Claim about the InternetMatching evidenceReject pattern avoided
"Reachable/blocked in the wild"Many real vantage points across networks/regions, dated"Two hosts stood in for the Internet"
"Widely deployed"Population-scale coverage with stated sampling"Convenience sample, claimed universal"
"Behavior changes over time"Dated longitudinal window with stability analysis"Single snapshot presented as a trend"
"We detect/measure X accurately"Validated ground-truth subsample + error bounds"No ground truth; labels unvalidated"
"This vantage point is representative"Coverage-bias quantified vs. the target population"Bias assumed away"

Ethical and safe active measurement

At IMC this is design, and it is a review gate (imc-submission):

[Do no harm]   rate-limit probes; avoid overloading targets or intermediary networks
[Opt-out]      honor blocklists and abuse contacts; provide a clear opt-out and project page
[Human data]   for traffic/DNS/user data, obtain IRB approval/exemption; minimize and anonymize
[Belmont]      argue respect for persons (consent where owed), beneficence (risk vs. benefit),
               justice (who bears risk vs. benefits)
[Disclosure]   for vulnerability/exposure findings, plan responsible disclosure and a timeline
[Consent]      for volunteer vantage points, obtain informed consent and protect participants

Provenance floor for measurement studies

  • Record every vantage point (location, AS, probe type) and quantify coverage.
  • Record timing: measurement dates, durations, cadence, and the analysis window.
  • Record targets: seed/target lists with capture dates and how they were sourced.
  • Record tools: exact versions, configs, rate limits, and opt-out handling.
  • Archive the captured data, not just the query — the Internet will have changed by re-run.

Vignette: measuring protocol deployment

Suppose the paper claims a protocol is widely deployed. The matching plan: scan (or query a platform for) a population-scale target set with stated inclusion criteria; run from vantage points whose coverage you quantify; date every scan and repeat over a window to show stability; validate a subsample of "deployed" classifications against an independent signal; handle churn and duplicates explicitly; and document the probing safety design in the Ethics section — every number traceable to a dated, provenanced capture in the released dataset.

Reporting floor

  • Confidence intervals for rate/proportion estimates; say what they represent and the sample size.
  • Coverage and its bias, stated quantitatively, for every vantage-point-based claim.
  • Measurement dates and windows for every temporal claim.
  • The compute/probe budget and any rate limits actually used.

Output format

[Measurement readiness] strong / adequate / weak
[Claim -> evidence map] <claim: vantage points / window / ground truth / statistic>
[Representativeness] <vantage points named? coverage bias quantified?>
[Ethics/safety] <active-measurement safety + IRB + disclosure handled? yes/no>
[Provenance] <vantage points / timing / targets / tool versions pinned? yes/no>
[Decision-critical next run] <one measurement to add or extend>

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

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