Nature Paper Card
Yuan1z0825/nature-skills
Builds a structured deep-reading card for one scientific paper, covering methods, how experiments support claims, limitations and research ideas, with a script to prepare the source.
A skill your agent uses when designing or auditing ACM SIGCOMM experiments — climbing the evidence ladder from microbenchmarks to testbed and trace replay to deployment, choosing baselines that…
Install Sigcomm Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigcomm-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigcomm-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/SIGCOMM-Skills/skills/sigcomm-experiments .claude/skills/sigcomm-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 "sigcomm-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGCOMM-Skills/skills/sigcomm-experiments into .claude/skills/sigcomm-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigcomm-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/SIGCOMM-Skills/skills/sigcomm-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 sigcomm-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigcomm-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/SIGCOMM-Skills/skills/sigcomm-experiments .agents/skills/sigcomm-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 "sigcomm-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGCOMM-Skills/skills/sigcomm-experiments into .agents/skills/sigcomm-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigcomm-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 sigcomm-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigcomm-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/SIGCOMM-Skills/skills/sigcomm-experiments .cursor/skills/sigcomm-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 "sigcomm-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGCOMM-Skills/skills/sigcomm-experiments into .cursor/skills/sigcomm-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigcomm-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 SIGCOMM-Skills/skills/sigcomm-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 sigcomm-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigcomm-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/SIGCOMM-Skills/skills/sigcomm-experiments .gemini/skills/sigcomm-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 "sigcomm-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGCOMM-Skills/skills/sigcomm-experiments into .gemini/skills/sigcomm-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigcomm-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 sigcomm-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 sigcomm-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/SIGCOMM-Skills/skills/sigcomm-experiments .github/skills/sigcomm-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 "sigcomm-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGCOMM-Skills/skills/sigcomm-experiments into .github/skills/sigcomm-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigcomm-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 sigcomm-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 sigcomm-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/SIGCOMM-Skills/skills/sigcomm-experiments .opencode/skills/sigcomm-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 "sigcomm-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGCOMM-Skills/skills/sigcomm-experiments into .opencode/skills/sigcomm-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigcomm-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
sigcomm-experiments- GitHub stars
- 1.2k
- Token cost
- ~988 tokens
- SKILL.md length
- 413 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 SIGCOMM experiments — climbing the evidence ladder from microbenchmarks to testbed and trace replay to deployment, choosing baselines that…
- Auditing ACM SIGCOMM experiments — climbing the evidence ladder from microbenchmarks to testbed and trace replay to deployment
- SKILL.md covers Experiment audit, The evidence ladder, What experiments are for here and Vignette: evaluating a routing…, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Choosing baselines that match the deployed state of the art
What it does
Sigcomm Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing ACM SIGCOMM experiments — climbing the evidence ladder from microbenchmarks to testbed and trace replay to deployment, choosing baselines that match the deployed state of the art, reporting tail percentiles and variance over repeated trials, mapping break points, and holding the fair variable fixed.
Its SKILL.md is about 990 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 Research & Science, covering Literature review. 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 SIGCOMM experiments — climbing the evidence ladder from microbenchmarks to testbed and trace replay to deployment
- Choosing baselines that match the deployed state of the art
- Reporting tail percentiles and variance over repeated trials
- Mapping break points
Example prompts
- “/sigcomm-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
Sigcomm Experiments loads about 988 tokens when it runs. Until then it costs about 88 tokens; SKILL.md has 413 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). 413 words, ~988 tokens.
Download SKILL.mdSave it as
.claude/skills/sigcomm-experiments/SKILL.md (or your agent's skills folder).- name
- sigcomm-experiments
- description
- Use when designing or auditing ACM SIGCOMM experiments — climbing the evidence ladder from microbenchmarks to testbed and trace replay to deployment, choosing baselines that match the deployed state of the art, reporting tail percentiles and variance over repeated trials, mapping break points, and holding the fair variable fixed.
SIGCOMM Experiments
Use this before submission when the evaluation is not yet locked. At SIGCOMM the experiments are where a mechanism is believed or disbelieved, and the reviewer culture reads a setup for realism, fairness, and honesty about where the mechanism breaks.
Experiment audit
- Map each performance claim to a specific microbenchmark, testbed run, trace replay, deployment measurement, or documented break point.
- Choose baselines that fight back: the strongest deployed alternative, tuned fairly, not a straw man the mechanism was built to beat.
- Report the distribution. Give the relevant percentiles (P50, P99, and the tail that hurts) with variance over repeated trials, and hold the fair variable — throughput, offered load, fairness — fixed while you compare.
- Document the setup completely: topology, buffer sizes, workload or trace and its provenance, parameters, environment versions, run counts, and seeds.
- Map the break point: the workload or condition where the mechanism degrades to the baseline. A paper that never shows where its idea stops working invites the reviewer to assume it stops everywhere interesting.
- Audit for leakage between motivation and evaluation, unrepresentative traffic, and any mismatch between the claimed operating regime and the one actually tested.
Show full SKILL.md (222 more words)Show less
The evidence ladder
| Rung | Evidence | What it proves | Limit |
|---|---|---|---|
| Microbenchmark | Isolated mechanism in a controlled setup | The mechanism does what the principle claims | Says nothing about real traffic |
| Testbed / emulation | Real switches or a faithful emulator under a workload | Behavior under contention and topology | Scale and workload realism bounded |
| Trace replay | A production or representative trace driving the testbed | Behavior under realistic traffic structure | Trace provenance must be defensible |
| Deployment | The mechanism in real operation | It survives the messy real world | Hard to isolate cause; strongest evidence |
Climb as high as the contribution's claim requires. A fabric or transport claim generally needs at least testbed-plus-trace evidence; simulation-only or mean-only results read as under-evaluated for such claims.
What experiments are for here
Not: "we beat every baseline on average across many workloads"
But: "under the traffic that causes the pain we measured, this mechanism
cuts the tail that hurts, at matched throughput, and here is exactly
where it stops helping."One decisive experiment under the workload that motivated the paper outweighs five extra datasets that never stress the mechanism.
Vignette: evaluating a routing mechanism
A paper proposes backlog-aware rerouting. The matching plan: a microbenchmark showing the rerouting reacts at the intended timescale; a testbed replaying a production RPC trace to show the tail-FCT win at matched throughput; sweeps over incast ratio and buffer size to show the win is not a single-point artifact; and an adversarial workload that removes flowlet gaps to map the break point where the mechanism degrades to ECMP — each result tied to a numbered claim.
Output format
[Evaluation readiness] strong / adequate / weak
[Claim -> evidence map] <claim: microbenchmark / testbed / trace / deployment>
[Baseline check] <strongest deployed alternative, tuned fairly? yes/no>
[Tail reporting] <percentiles + variance + matched fair variable>
[Break point] <where the mechanism degrades, and to what>
[Decision-critical next run] <one experiment>© 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 SIGCOMM-Skills/skills/sigcomm-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Sigcomm 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 |
|---|---|---|---|---|---|---|
| Sigcomm Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~988 | Automated safety check: Pass | MIT | |
| Nature Paper CardYuan1z0825/nature-skills | 47k | 2 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Systematic Review ScreenerImbad0202/academic-research-skills | 51k | — | ~8.4k | Automated safety check: Pass | Custom licence | |
| Literature Reviewneflibata-feng/MyArxiv-Agent | 126 | 20 repos | ~5.9k | Automated safety check: Notes | MIT | |
| Preprint Search on bioRxivLigphiDonk/Oh-my--paper | 738 | 12 repos | ~3.7k | Automated safety check: Pass | MIT | |
| Academic Paper Writing PipelineImbad0202/academic-research-skills | 51k | — | ~16k | Automated safety check: Pass | Custom licence |
Similar skills
Systematic Review Screener
Imbad0202/academic-research-skills
Screens records for systematic, scoping and rapid reviews against fixed eligibility rules, using two blinded AI reviewers and a third adjudicator, with traceable PRISMA counts.
Literature Review
neflibata-feng/MyArxiv-Agent
Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.).
Preprint Search on bioRxiv
LigphiDonk/Oh-my--paper
Searches bioRxiv life sciences preprints by keyword, author, date range or category with a Python script, returning JSON metadata and optional PDF downloads.
Academic Paper Writing Pipeline
Imbad0202/academic-research-skills
Runs a 12-agent pipeline that plans, drafts, cites, reviews and formats academic papers, with modes for revision, rebuttals, abstracts and citation checks.
Deep Research Workflow
TokenRhythm/opensquilla
Runs multi-round research in three stages with a persisted state file, evidence tracking and a long-form report with per-claim citations.
More from brycewang-stanford/Awesome-Journal-Skills
All 2,387 skills in this repoAaag Data Analysis
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when running and reporting the analysis for an Annals of the American Association of Geographers manuscript — spatial statistics and modeling, remote-sensing accuracy, or…
Aaag Literature Positioning
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when positioning an Annals of the American Association of Geographers manuscript in the literature — engaging geographic scholarship across the relevant area and the…
Aaag Rebuttal
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when responding to an Annals of the American Association of Geographers decision letter (major/minor revision) — building a point-by-point response to the subject editor and…
Aaag Research Design
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when defending the research design of an Annals of the American Association of Geographers manuscript — spatial/quantitative analysis and GIScience, remote-sensing and…
Aaag Review Process
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when you need to understand how the Annals of the American Association of Geographers evaluates a manuscript — double-anonymous review routed through a subject editor by…
Aaag Submission
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when running the final pre-submission preflight for the Annals of the American Association of Geographers via ScholarOne Manuscripts — area/article-type selection…
Related
Categories
Questions about Sigcomm Experiments
What does Sigcomm Experiments do?
A skill your agent uses when designing or auditing ACM SIGCOMM experiments — climbing the evidence ladder from microbenchmarks to testbed and trace replay to deployment, choosing baselines that…. Sigcomm Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing ACM SIGCOMM experiments — climbing the evidence ladder from microbenchmarks to testbed and trace replay to deployment, choosing baselines that match the deployed state of the art, reporting tail percentiles and variance over repeated trials, mapping break points, and holding the fair variable fixed.
When should I use Sigcomm Experiments?
Sigcomm Experiments fits situations like: auditing ACM SIGCOMM experiments — climbing the evidence ladder from microbenchmarks to testbed and trace replay to deployment; choosing baselines that match the deployed state of the art; reporting tail percentiles and variance over repeated trials; mapping break points.
How do I install Sigcomm Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigcomm-experiments -a claude-code`. Or copy the skill folder (SIGCOMM-Skills/skills/sigcomm-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/sigcomm-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Sigcomm Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigcomm-experiments -a codex`. Or copy the skill folder (SIGCOMM-Skills/skills/sigcomm-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/sigcomm-experiments in your project. Codex loads it when a task matches its description.
Can I use Sigcomm 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 sigcomm-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/sigcomm-experiments, .gemini/skills/sigcomm-experiments, .github/skills/sigcomm-experiments and .opencode/skills/sigcomm-experiments in your project.
What does Sigcomm Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Sigcomm Experiments is instructions for the agent only.
Does Sigcomm 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 Sigcomm 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 Sigcomm Experiments use?
Sigcomm 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 Sigcomm Experiments use?
About 988 tokens (SKILL.md is roughly 4k 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 Sigcomm Experiments?
Skills that share tags, products or a category with Sigcomm Experiments: Nature Paper Card (Yuan1z0825/nature-skills, 47k stars), Systematic Review Screener (Imbad0202/academic-research-skills, 51k stars), Literature Review (neflibata-feng/MyArxiv-Agent, 126 stars) and Preprint Search on bioRxiv (LigphiDonk/Oh-my--paper, 738 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Sigcomm 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.