Peer Review
K-Dense-AI/claude-scientific-writer
Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.
Agent skill
Sigcomm Reproducibility
by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when strengthening the reproducibility evidence of an ACM SIGCOMM paper — topology and testbed ledgers, traffic workload and trace provenance, configuration and version…
Install Sigcomm Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigcomm-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigcomm-reproducibility --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-reproducibility .claude/skills/sigcomm-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGCOMM-Skills/skills/sigcomm-reproducibility into .claude/skills/sigcomm-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigcomm-reproducibility", 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-reproducibilityType 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-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigcomm-reproducibility --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-reproducibility .agents/skills/sigcomm-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGCOMM-Skills/skills/sigcomm-reproducibility into .agents/skills/sigcomm-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigcomm-reproducibility", 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-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigcomm-reproducibility --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-reproducibility .cursor/skills/sigcomm-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGCOMM-Skills/skills/sigcomm-reproducibility into .cursor/skills/sigcomm-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigcomm-reproducibility", 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-reproducibility--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-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigcomm-reproducibility --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-reproducibility .gemini/skills/sigcomm-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGCOMM-Skills/skills/sigcomm-reproducibility into .gemini/skills/sigcomm-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigcomm-reproducibility", 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-reproducibilityInstalls 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-reproducibility -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-reproducibility .github/skills/sigcomm-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGCOMM-Skills/skills/sigcomm-reproducibility into .github/skills/sigcomm-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigcomm-reproducibility", 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-reproducibility -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-reproducibility --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-reproducibility .opencode/skills/sigcomm-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGCOMM-Skills/skills/sigcomm-reproducibility into .opencode/skills/sigcomm-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigcomm-reproducibility", 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-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.1k tokens
- SKILL.md length
- 434 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when strengthening the reproducibility evidence of an ACM SIGCOMM paper — topology and testbed ledgers, traffic workload and trace provenance, configuration and version…
- Strengthening the reproducibility evidence of an ACM SIGCOMM paper — topology and testbed ledgers
- SKILL.md covers Evidence map, The four ledgers, Degrees of reproducibility and Vignette: a wide-area…, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Traffic workload and trace provenance
What it does
Sigcomm Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening the reproducibility evidence of an ACM SIGCOMM paper — topology and testbed ledgers, traffic workload and trace provenance, configuration and version pinning, tail-percentile run counts and variance, legal data-release decisions, and consistency between the paper's claims and the artifact that backs them.
Its SKILL.md is about 1.1k 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 Reproducible research. 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
- Strengthening the reproducibility evidence of an ACM SIGCOMM paper — topology and testbed ledgers
- Traffic workload and trace provenance
- Configuration and version pinning
- Tail-percentile run counts and variance
Example prompts
- “/sigcomm-reproducibility”
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 Reproducibility loads about 1.1k tokens when it runs. Until then it costs about 88 tokens; SKILL.md has 434 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). 434 words, ~1,061 tokens.
Download SKILL.mdSave it as
.claude/skills/sigcomm-reproducibility/SKILL.md (or your agent's skills folder).- name
- sigcomm-reproducibility
- description
- Use when strengthening the reproducibility evidence of an ACM SIGCOMM paper — topology and testbed ledgers, traffic workload and trace provenance, configuration and version pinning, tail-percentile run counts and variance, legal data-release decisions, and consistency between the paper's claims and the artifact that backs them.
SIGCOMM Reproducibility
Use this before submission and again before the artifact deadline. SIGCOMM's culture treats a result as a claim a stranger should be able to rebuild; reproducibility here is largely about whether the network conditions behind a number are recorded well enough to recreate. Reopen the current Call for Artifacts to confirm the process for this edition.
Evidence map
- Map each reported number to a rebuildable location: a script, a logged run, a config, and the figure it feeds.
- For every measurement, record the topology (physical or emulated), the traffic (workload generator or trace, with provenance), the configuration (every parameter), and the environment (kernel, NIC, switch firmware, data-plane toolchain).
- For any stochastic result, report the run count, the seed or workload driver, and the variance behind each percentile — a lone 99th-percentile number with no replication is not reproducible.
- Decide early what can legally ship: production traces and topologies often cannot, so plan a substitute with matching statistical character rather than discovering the block at the deadline.
- Keep the paper and the artifact consistent; a figure the artifact cannot regenerate is a reproducibility gap reviewers and the AEC both notice.
The four ledgers
| Ledger | What it records | Failure it prevents |
|---|---|---|
| Topology | Nodes, links, capacities, buffer sizes, emulation vs. hardware | "Which topology produced Figure 6?" |
| Traffic | Workload distribution or trace, provenance, which figure used it | Numbers that cannot be tied to an input |
| Configuration | Every parameter and its value per experiment | Silent knob changes between runs |
| Run | Seeds, replication counts, timestamps, variance | Percentiles with no reproducible basis |
Maintaining these while you run is cheap; reconstructing them after the deadline is error-prone and often impossible.
Show full SKILL.md (161 more words)Show less
Degrees of reproducibility
Turnkey : one command rebuilds each figure from logged runs on a documented setup
Scripted : scripts exist but need hardware, private data, or documented manual steps
Descriptive : prose detailed enough that a competent networker could rebuild the pipeline
Fragile : results depend on unrecorded conditions -> a red flag to fix before submissionFor SIGCOMM, downscaled testbed results should be turnkey because evaluators actually run them; full-scale hardware results may stay scripted with the deviation documented. State the achieved level honestly rather than promising turnkey behavior that fails on a clean machine.
Vignette: a wide-area measurement result
A paper characterizes loss and latency on a production overlay, then proposes a routing tweak. Its reproducibility spine: the measurement window and vantage points, the sampling method, the anonymization applied before any release, a public-trace or synthetic substitute for the parts that cannot ship, and a driver that regenerates the loss-latency figures from logged samples — plus one honest sentence about which production condition the substitute cannot capture.
Legal and ethical release
- Real user or third-party-infrastructure measurement needs its handling addressed in the paper; anonymize before release and record what was removed.
- Prefer an archival host with a DOI for anything you can ship, and document the exact reason for anything you cannot.
Output format
[Claim inventory] <number -> rebuildable location>
[Ledger status] topology / traffic / configuration / run — complete or gaps
[Reproducibility level] turnkey / scripted / descriptive / fragile
[Data-release plan] shippable / substitute (+ provenance) / withheld (+ reason)
[Paper fixes] <must appear in the main PDF>
[Artifact fixes] <what the package still needs>© 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-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Sigcomm Reproducibility 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 Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.1k | Automated safety check: Pass | MIT | |
| Peer ReviewK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~3.1k | Automated safety check: Notes | MIT | |
| CHARLS Paper Reproduction Guidexjtulyc/MedgeClaw | 617 | 1 repos | ~1.8k | Automated safety check: Pass | None | |
| Compute Environment Setupaipoch/open-science | 5.5k | — | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Figure Styleaipoch/open-science | 5.5k | — | ~5.1k | Automated safety check: Pass | Apache-2.0 | |
| Add Bactopia Toolbactopia/bactopia | 522 | — | ~4.1k | Automated safety check: Pass | MIT |
Similar skills
CHARLS Paper Reproduction Guide
xjtulyc/MedgeClaw
Guides an agent through reproducing papers built on the CHARLS health and retirement survey, from variable mapping to cognition, depression and isolation scores.
Compute Environment Setup
aipoch/open-science
Prepares setup instructions and a named activation file for a user-managed software environment on an Open-Science SSH or Slurm compute host.
Figure Style
aipoch/open-science
Publication-grade correctness and legibility rules for final-deliverable scientific figures, not exploratory plots.
Add Bactopia Tool
bactopia/bactopia
Scaffold a complete Bactopia Tool across all three tiers -- module, subworkflow, and workflow entry point under workflows/bactopia-tools/.
Modeling Code and Result Contracts
yushui2022/MathModel-Skill
Generates result-evidence contracts, tables and runnable q1 to q3 modeling code scaffolds for a math modeling paper from a model route, a data plan and cleaned data.
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Questions about Sigcomm Reproducibility
What does Sigcomm Reproducibility do?
A skill your agent uses when strengthening the reproducibility evidence of an ACM SIGCOMM paper — topology and testbed ledgers, traffic workload and trace provenance, configuration and version…. Sigcomm Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening the reproducibility evidence of an ACM SIGCOMM paper — topology and testbed ledgers, traffic workload and trace provenance, configuration and version pinning, tail-percentile run counts and variance, legal data-release decisions, and consistency between the paper's claims and the artifact that backs them.
When should I use Sigcomm Reproducibility?
Sigcomm Reproducibility fits situations like: strengthening the reproducibility evidence of an ACM SIGCOMM paper — topology and testbed ledgers; traffic workload and trace provenance; configuration and version pinning; tail-percentile run counts and variance.
How do I install Sigcomm Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigcomm-reproducibility -a claude-code`. Or copy the skill folder (SIGCOMM-Skills/skills/sigcomm-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/sigcomm-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Sigcomm Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigcomm-reproducibility -a codex`. Or copy the skill folder (SIGCOMM-Skills/skills/sigcomm-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/sigcomm-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Sigcomm Reproducibility 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-reproducibility -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-reproducibility, .gemini/skills/sigcomm-reproducibility, .github/skills/sigcomm-reproducibility and .opencode/skills/sigcomm-reproducibility in your project.
What does Sigcomm Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Sigcomm Reproducibility is instructions for the agent only.
Does Sigcomm Reproducibility 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 Reproducibility 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 Reproducibility use?
Sigcomm Reproducibility 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 Reproducibility use?
About 1.1k tokens (SKILL.md is roughly 4.2k 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 Reproducibility?
Skills that share tags, products or a category with Sigcomm Reproducibility: Peer Review (K-Dense-AI/claude-scientific-writer, 2.4k stars), CHARLS Paper Reproduction Guide (xjtulyc/MedgeClaw, 617 stars), Compute Environment Setup (aipoch/open-science, 5.5k stars) and Figure Style (aipoch/open-science, 5.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Sigcomm Reproducibility?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,231 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.