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A skill your agent uses when designing or auditing ACM SIGMETRICS evaluations, covering theorem-plus-validation rigor, stating and testing modeling assumptions, analysis-vs-simulation agreement…
Install Sigmetrics Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmetrics-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigmetrics-experiments --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/SIGMETRICS-Skills/skills/sigmetrics-experiments .claude/skills/sigmetrics-experiments && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "sigmetrics-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMETRICS-Skills/skills/sigmetrics-experiments into .claude/skills/sigmetrics-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmetrics-experiments", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMETRICS-Skills/skills/sigmetrics-experimentsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmetrics-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigmetrics-experiments --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/SIGMETRICS-Skills/skills/sigmetrics-experiments .agents/skills/sigmetrics-experiments && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "sigmetrics-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMETRICS-Skills/skills/sigmetrics-experiments into .agents/skills/sigmetrics-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmetrics-experiments", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmetrics-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigmetrics-experiments --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/SIGMETRICS-Skills/skills/sigmetrics-experiments .cursor/skills/sigmetrics-experiments && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "sigmetrics-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMETRICS-Skills/skills/sigmetrics-experiments into .cursor/skills/sigmetrics-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmetrics-experiments", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path SIGMETRICS-Skills/skills/sigmetrics-experiments--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmetrics-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigmetrics-experiments --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/SIGMETRICS-Skills/skills/sigmetrics-experiments .gemini/skills/sigmetrics-experiments && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "sigmetrics-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMETRICS-Skills/skills/sigmetrics-experiments into .gemini/skills/sigmetrics-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmetrics-experiments", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigmetrics-experimentsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmetrics-experiments -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/SIGMETRICS-Skills/skills/sigmetrics-experiments .github/skills/sigmetrics-experiments && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "sigmetrics-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMETRICS-Skills/skills/sigmetrics-experiments into .github/skills/sigmetrics-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmetrics-experiments", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmetrics-experiments -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigmetrics-experiments --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/SIGMETRICS-Skills/skills/sigmetrics-experiments .opencode/skills/sigmetrics-experiments && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "sigmetrics-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGMETRICS-Skills/skills/sigmetrics-experiments into .opencode/skills/sigmetrics-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigmetrics-experiments", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
sigmetrics-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 590 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when designing or auditing ACM SIGMETRICS evaluations, covering theorem-plus-validation rigor, stating and testing modeling assumptions, analysis-vs-simulation agreement…
- Auditing ACM SIGMETRICS evaluations
- SKILL.md covers Evaluation audit, Claim-to-evidence design table, Validation floor for analytic… and Provenance floor for…, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering theorem-plus-validation rigor
What it does
Sigmetrics Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing ACM SIGMETRICS evaluations, covering theorem-plus-validation rigor, stating and testing modeling assumptions, analysis-vs-simulation agreement, real workloads and traces, fairly tuned baselines, statistics and confidence intervals for stochastic systems, learning guarantees, measurement provenance, and matching evidence to the shape of each performance claim.
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Data & Analytics, covering Statistics. The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的… The licence is MIT.
When your agent uses it
- Auditing ACM SIGMETRICS evaluations
- Covering theorem-plus-validation rigor
- Stating and testing modeling assumptions
- Analysis-vs-simulation agreement
Example prompts
- “/sigmetrics-experiments”
What it can do on your machine
Read from SKILL.md and the folder at commit 932eb23. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Sigmetrics Experiments loads about 1.4k tokens when it runs. Until then it costs about 104 tokens; SKILL.md has 590 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from brycewang-stanford/Awesome-Journal-Skills at commit 932eb23, republished under its MIT licence (© brycewang-stanford). 590 words, ~1,408 tokens.
Download SKILL.mdSave it as
.claude/skills/sigmetrics-experiments/SKILL.md (or your agent's skills folder).- name
- sigmetrics-experiments
- description
- Use when designing or auditing ACM SIGMETRICS evaluations, covering theorem-plus-validation rigor, stating and testing modeling assumptions, analysis-vs-simulation agreement, real workloads and traces, fairly tuned baselines, statistics and confidence intervals for stochastic systems, learning guarantees, measurement provenance, and matching evidence to the shape of each performance claim.
SIGMETRICS Experiments
Use this before submission when the evidence is not yet locked. SIGMETRICS reviewers are performance-evaluation specialists; the evaluation is where a good model or measurement is won or lost. The organizing principle is evidence proportional to the claim — an analytic claim needs a proof and validation, a measurement claim needs a methodology a skeptic accepts, and a learning claim needs a guarantee, not only a benchmark score.
Evaluation audit
- Match evidence to the claim shape. A claim about a bound needs a proof plus a simulation that shows the analytic curve is right; a claim about a real system needs measured data and a documented methodology; a claim about a learner needs a regret/convergence guarantee; a claim about tail behavior needs tail metrics (p99), not means.
- State and test the modeling assumptions. Fit the arrival/service distributions to the real workload (QQ-plots, goodness-of-fit); a theorem whose M/G/1 assumption is never checked against the target system invites the "unrealistic model" reject.
- Show analysis-vs-simulation agreement. Overlay the analytic prediction on simulated measurements; if they disagree, the model or the proof is wrong, and it is better to find that yourself.
- Use real workloads/traces, sampled or selected by a stated criterion, and describe their provenance. Toy inputs invite the "does this hold on real systems?" reject.
- Choose fair baselines, including the strongest prior policy/algorithm and a simple-but-reasonable alternative, tuned with a documented, equal budget. An untuned baseline is a scored weakness.
- Report statistics for stochastic systems: confidence intervals over independent runs, the number of runs and the source of variance, warm-up/steady-state handling for simulations, and effect sizes for comparisons. A single run with no interval is not evidence.
- For learning contributions, report the guarantee (regret/convergence/sample complexity) and validate it empirically; leaderboard numbers alone route to an ML venue.
Claim-to-evidence design table
| Performance claim | Matching evidence | Reject pattern avoided |
|---|---|---|
| "Our policy bounds the tail" | Proof under stated assumptions + simulation matching the analytic p99 with CIs | "A p99 plot with no analytic comparison" |
| "The model captures the system" | Distribution fit to a real trace + goodness-of-fit | "Assumed M/G/1, never checked against the workload" |
| "We beat the prior policy" | Both tuned with equal budget; effect sizes + CIs on real workloads | "Untuned baseline; toy inputs" |
| "The algorithm has low regret" | Regret bound (proof) + empirical regret curve vs. the bound | "Benchmark score with no guarantee" |
| "Scales to realistic load" | Metrics across realistic arrival rates/sizes with variance reported | "Only light load tested" |
Show full SKILL.md (187 more words)Show less
Validation floor for analytic results
[Proof] full derivation (appendix within the reviewed pages); every case and assumption stated
[Simulation] seeded, steady-state-aware; overlay the analytic prediction; report CIs and #runs
[Agreement] quantify the gap between analysis and simulation; explain any discrepancy
[Robustness] stress an assumption (heavy tails, estimation error) and bound the degradationProvenance floor for measurement studies
- Record the trace/data source, the collection window, sanitization/anonymization, and access terms; archive the processed dataset (or document access), not just the collection script.
- State inclusion/exclusion criteria and the resulting sample, with the filtering script in the artifact.
- Report how outliers, gaps, and measurement artifacts were handled — silent inclusion skews every downstream metric.
Vignette: evaluating a scheduling policy
Suppose the paper claims a new policy provably reduces p99 latency versus a size-aware optimal. The matching plan: prove the bound under stated assumptions; fit the service-time distribution to a real trace and show the fit; simulate both policies with logged seeds and steady-state handling, overlay the analytic p99 on the simulated p99 with confidence intervals; run a trace-driven evaluation with both policies tuned equally, reporting effect sizes; and quantify the degradation under fetch-size estimation error — every number traceable to a logged run in the artifact.
Statistical reporting floor
- Confidence intervals and the number of independent runs for every stochastic measurement.
- Steady-state / warm-up handling stated for simulations.
- The compute and workload scale actually used, not vague feasibility language.
Output format
[Evaluation readiness] strong / adequate / weak
[Claim -> evidence map] <claim: proof? / validation? / measurement? / guarantee?>
[Assumption check] <assumption -> fit to real workload? goodness-of-fit shown?>
[Analysis-vs-measurement] <agreement shown with CIs? discrepancy explained? yes/no>
[Baseline fairness] <baseline -> tuned? equal budget? documented?>
[Decision-critical next run] <one experiment, proof case, or validation to add>© brycewang-stanford, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in SIGMETRICS-Skills/skills/sigmetrics-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Sigmetrics Experiments next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Sigmetrics Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.4k | Automated safety check: Pass | MIT | |
| Sandbox Benchvercel/next.js | 143k | — | ~4.1k | Automated safety check: Pass | MIT | |
| Statistical Analysisspacering-net/codeg | 3.9k | 3 repos | ~5k | Automated safety check: Pass | MIT | |
| StatsmodelszLanqing/codex-claude-academic-skills | 4.7k | 15 repos | ~4.9k | Automated safety check: Pass | BSD-3-Clause | |
| AI Daily DigestvigorX777/ai-daily-digest | 1.6k | — | ~1.3k | Automated safety check: Pass | None | |
| Statistical Powerspacering-net/codeg | 3.9k | 1 repos | ~3.6k | Automated safety check: Notes | MIT |
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Questions about Sigmetrics Experiments
What does Sigmetrics Experiments do?
A skill your agent uses when designing or auditing ACM SIGMETRICS evaluations, covering theorem-plus-validation rigor, stating and testing modeling assumptions, analysis-vs-simulation agreement…. Sigmetrics Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing ACM SIGMETRICS evaluations, covering theorem-plus-validation rigor, stating and testing modeling assumptions, analysis-vs-simulation agreement, real workloads and traces, fairly tuned baselines, statistics and confidence intervals for stochastic systems, learning guarantees, measurement provenance, and matching evidence to the shape of each performance claim.
When should I use Sigmetrics Experiments?
Sigmetrics Experiments fits situations like: auditing ACM SIGMETRICS evaluations; covering theorem-plus-validation rigor; stating and testing modeling assumptions; analysis-vs-simulation agreement.
How do I install Sigmetrics Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmetrics-experiments -a claude-code`. Or copy the skill folder (SIGMETRICS-Skills/skills/sigmetrics-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/sigmetrics-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Sigmetrics Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmetrics-experiments -a codex`. Or copy the skill folder (SIGMETRICS-Skills/skills/sigmetrics-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/sigmetrics-experiments in your project. Codex loads it when a task matches its description.
Can I use Sigmetrics Experiments in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmetrics-experiments -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sigmetrics-experiments, .gemini/skills/sigmetrics-experiments, .github/skills/sigmetrics-experiments and .opencode/skills/sigmetrics-experiments in your project.
What does Sigmetrics Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Sigmetrics Experiments is instructions for the agent only.
Does Sigmetrics Experiments access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Sigmetrics Experiments safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Sigmetrics Experiments use?
Sigmetrics Experiments is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Sigmetrics Experiments use?
About 1.4k tokens (SKILL.md is roughly 5.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Sigmetrics Experiments?
Skills that share tags, products or a category with Sigmetrics Experiments: Sandbox Bench (vercel/next.js, 143k stars), Statistical Analysis (spacering-net/codeg, 3.9k stars), Statsmodels (zLanqing/codex-claude-academic-skills, 4.7k stars) and AI Daily Digest (vigorX777/ai-daily-digest, 1.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Sigmetrics Experiments?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,228 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.
Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.