Peer Review
spacering-net/codeg
Structured manuscript/grant review with checklist-based evaluation.
A skill your agent uses when explaining or planning around ACM SIGCOMM peer review — double-blind HotCRP reviewing, the early-reject cut for consensus rejections, the rebuttal for discussion-phase…
Install Sigcomm Review Process
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigcomm-review-process -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigcomm-review-process --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-review-process .claude/skills/sigcomm-review-process && 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-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGCOMM-Skills/skills/sigcomm-review-process into .claude/skills/sigcomm-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigcomm-review-process", 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-review-processType 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-review-process -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigcomm-review-process --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-review-process .agents/skills/sigcomm-review-process && 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-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGCOMM-Skills/skills/sigcomm-review-process into .agents/skills/sigcomm-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigcomm-review-process", 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-review-process -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigcomm-review-process --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-review-process .cursor/skills/sigcomm-review-process && 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-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGCOMM-Skills/skills/sigcomm-review-process into .cursor/skills/sigcomm-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigcomm-review-process", 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-review-process--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-review-process -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigcomm-review-process --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-review-process .gemini/skills/sigcomm-review-process && 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-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGCOMM-Skills/skills/sigcomm-review-process into .gemini/skills/sigcomm-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigcomm-review-process", 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-review-processInstalls 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-review-process -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-review-process .github/skills/sigcomm-review-process && 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-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGCOMM-Skills/skills/sigcomm-review-process into .github/skills/sigcomm-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigcomm-review-process", 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-review-process -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-review-process --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-review-process .opencode/skills/sigcomm-review-process && 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-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGCOMM-Skills/skills/sigcomm-review-process into .opencode/skills/sigcomm-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigcomm-review-process", 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-review-process- GitHub stars
- 1.2k
- Token cost
- ~1k tokens
- SKILL.md length
- 470 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when explaining or planning around ACM SIGCOMM peer review — double-blind HotCRP reviewing, the early-reject cut for consensus rejections, the rebuttal for discussion-phase…
- Planning around ACM SIGCOMM peer review — double-blind HotCRP reviewing
- SKILL.md covers The outcome space is wider…, What reviewers weigh, Who reviews here and Stage-by-stage realism, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- The early-reject cut for consensus rejections
What it does
Sigcomm Review Process is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when explaining or planning around ACM SIGCOMM peer review — double-blind HotCRP reviewing, the early-reject cut for consensus rejections, the rebuttal for discussion-phase papers, the shepherd-run one-shot revision that ends in accept or reject only, shepherding of accepted papers, and how the outcome space shapes author strategy.
Its SKILL.md is about 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 Peer 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
- Planning around ACM SIGCOMM peer review — double-blind HotCRP reviewing
- The early-reject cut for consensus rejections
- The rebuttal for discussion-phase papers
- The shepherd-run one-shot revision that ends in accept
Example prompts
- “/sigcomm-review-process”
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 Review Process loads about 1k tokens when it runs. Until then it costs about 90 tokens; SKILL.md has 470 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). 470 words, ~1,041 tokens.
Download SKILL.mdSave it as
.claude/skills/sigcomm-review-process/SKILL.md (or your agent's skills folder).- name
- sigcomm-review-process
- description
- Use when explaining or planning around ACM SIGCOMM peer review — double-blind HotCRP reviewing, the early-reject cut for consensus rejections, the rebuttal for discussion-phase papers, the shepherd-run one-shot revision that ends in accept or reject only, shepherding of accepted papers, and how the outcome space shapes author strategy.
SIGCOMM Review Process
Use this to reason about review-stage strategy. Reopen the current CFP and submission page before making process claims; the mechanisms below are the SIGCOMM 2026 rendering and can change per edition.
The outcome space is wider than accept/reject
SIGCOMM layers several author-facing stages, and reading which one you are in drives every subsequent move:
- Early reject. Submissions with a consensus to reject are notified before the formal date, so authors can redirect the work to another venue sooner. It is a courtesy, not a soft accept; there is no reply that reopens it.
- Rebuttal. Papers that advance to the discussion phase receive reviews and a chance to respond. This is the moment to correct factual errors and answer the decision-critical objection — not to relitigate taste.
- One-shot revision. A small set of papers get a merits summary plus a required-changes list, resubmit about a month later, and are re-reviewed by the same reviewers where possible under a PC shepherd. The name is literal: the revised paper can only be accepted or rejected, so the issue list is a contract, not a suggestion.
- Accept (with shepherding). Accepted papers may be assigned a shepherd who confirms the reviews are addressed through camera-ready and approves whether any appendix is necessary.
What reviewers weigh
| Review dimension | What raises it | What sinks it |
|---|---|---|
| Contribution | A networking mechanism or architecture that generalizes | A single-site tuning win with no transferable idea |
| Evaluation realism | Testbed, trace, or deployment evidence with reported tails | Simulation-only or mean-only results for a fabric claim |
| Baselines | Comparisons against the strongest deployed alternative | Straw-man baselines the mechanism is built to beat |
| Measurement rigor | Documented workload, topology, run counts, variance | Percentiles with no replication or unstated conditions |
| Clarity | A stated design principle the paper defends | A behavior described but no invariant named |
Show full SKILL.md (171 more words)Show less
Who reviews here
The PC mixes networking-systems builders, measurement researchers, and theory-leaning networking people; expect at least one reviewer to interrogate the evaluation setup line by line and another to ask whether the mechanism generalizes beyond the tested topology. Because SIGCOMM is the broad flagship, a paper is often read against the strongest prior work in its exact subarea, so a missing state-of-the-art baseline is caught rather than skimmed past.
Stage-by-stage realism
- Reviews: triage by what the discussion and the eventual shepherd would weigh, not by reviewer tone; one unresolved evaluation-realism objection outweighs several style notes.
- Rebuttal: short and precise beats exhaustive; answer the objection that decides the paper and concede what cannot be fixed now.
- Revision: treat the required-changes list as the grading rubric; a revision that addresses adjacent points but dodges a listed issue is the recognizable one-shot failure.
- Shepherding: the shepherd is an ally who signs off the final version, including appendix necessity — engage early through HotCRP comments rather than surfacing surprises at the camera-ready deadline.
Output format
[Current stage] submitted / reviews / rebuttal / revision / decision / camera-ready
[Outcome class] early reject / rebuttal / one-shot revision / accept-with-shepherd
[Decision actors] <reviewers / discussion / shepherd / chairs>
[Likely leverage] <contribution / evaluation realism / baselines / measurement / clarity>
[Forbidden moves] <identity leak / new unsupported results / dodging the issue list>
[Next response move] <one action>© 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-review-process of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Sigcomm Review Process 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 Review Process this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1k | Automated safety check: Pass | MIT | |
| Peer Reviewspacering-net/codeg | 3.9k | 17 repos | ~5.9k | Automated safety check: Notes | MIT | |
| Scholar Evaluationspacering-net/codeg | 3.9k | 11 repos | ~3.2k | Automated safety check: Pass | MIT | |
| Academic Paper Writing PipelineImbad0202/academic-research-skills | 51k | — | ~16k | Automated safety check: Pass | Custom licence | |
| Academic Paper ReviewerImbad0202/academic-research-skills | 51k | — | ~11k | Automated safety check: Pass | Custom licence | |
| Peer ReviewK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~3.1k | Automated safety check: Notes | MIT |
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spacering-net/codeg
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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.
Academic Paper Reviewer
Imbad0202/academic-research-skills
Simulates a journal peer review of a manuscript with a five-seat reviewer panel, an editorial synthesizer and several review modes.
Peer Review
K-Dense-AI/claude-scientific-writer
Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.
Academic Research Pipeline
Imbad0202/academic-research-skills
Orchestrates a ten-stage academic workflow from research to finished manuscript, including integrity checks, two rounds of peer review and revision.
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Categories
Questions about Sigcomm Review Process
What does Sigcomm Review Process do?
A skill your agent uses when explaining or planning around ACM SIGCOMM peer review — double-blind HotCRP reviewing, the early-reject cut for consensus rejections, the rebuttal for discussion-phase…. Sigcomm Review Process is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when explaining or planning around ACM SIGCOMM peer review — double-blind HotCRP reviewing, the early-reject cut for consensus rejections, the rebuttal for discussion-phase papers, the shepherd-run one-shot revision that ends in accept or reject only, shepherding of accepted papers, and how the outcome space shapes author strategy.
When should I use Sigcomm Review Process?
Sigcomm Review Process fits situations like: planning around ACM SIGCOMM peer review — double-blind HotCRP reviewing; the early-reject cut for consensus rejections; the rebuttal for discussion-phase papers; the shepherd-run one-shot revision that ends in accept.
How do I install Sigcomm Review Process in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigcomm-review-process -a claude-code`. Or copy the skill folder (SIGCOMM-Skills/skills/sigcomm-review-process in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/sigcomm-review-process in your project. Claude Code loads it when a task matches its description.
How do I install Sigcomm Review Process in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigcomm-review-process -a codex`. Or copy the skill folder (SIGCOMM-Skills/skills/sigcomm-review-process in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/sigcomm-review-process in your project. Codex loads it when a task matches its description.
Can I use Sigcomm Review Process 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-review-process -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-review-process, .gemini/skills/sigcomm-review-process, .github/skills/sigcomm-review-process and .opencode/skills/sigcomm-review-process in your project.
What does Sigcomm Review Process need to run?
SKILL.md names no scripts, command-line tools or credentials: Sigcomm Review Process is instructions for the agent only.
Does Sigcomm Review Process 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 Review Process 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 Review Process use?
Sigcomm Review Process 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 Review Process use?
About 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 Review Process?
Skills that share tags, products or a category with Sigcomm Review Process: Peer Review (spacering-net/codeg, 3.9k stars), Scholar Evaluation (spacering-net/codeg, 3.9k stars), Academic Paper Writing Pipeline (Imbad0202/academic-research-skills, 51k stars) and Academic Paper Reviewer (Imbad0202/academic-research-skills, 51k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Sigcomm Review Process?
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.