Peer Review
K-Dense-AI/claude-scientific-writer
Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.
A skill your agent uses when strengthening ACM SoCC reproducibility, covering the testbed and workload description, released code and traces, provenance pinning for measurement studies, reproducing…
Install Socc Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill socc-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills socc-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/SoCC-Skills/skills/socc-reproducibility .claude/skills/socc-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 "socc-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SoCC-Skills/skills/socc-reproducibility into .claude/skills/socc-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "socc-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/SoCC-Skills/skills/socc-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 socc-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills socc-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/SoCC-Skills/skills/socc-reproducibility .agents/skills/socc-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 "socc-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SoCC-Skills/skills/socc-reproducibility into .agents/skills/socc-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "socc-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 socc-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills socc-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/SoCC-Skills/skills/socc-reproducibility .cursor/skills/socc-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 "socc-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SoCC-Skills/skills/socc-reproducibility into .cursor/skills/socc-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "socc-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 SoCC-Skills/skills/socc-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 socc-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills socc-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/SoCC-Skills/skills/socc-reproducibility .gemini/skills/socc-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 "socc-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SoCC-Skills/skills/socc-reproducibility into .gemini/skills/socc-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "socc-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 socc-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 socc-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/SoCC-Skills/skills/socc-reproducibility .github/skills/socc-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 "socc-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SoCC-Skills/skills/socc-reproducibility into .github/skills/socc-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "socc-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 socc-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 socc-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/SoCC-Skills/skills/socc-reproducibility .opencode/skills/socc-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 "socc-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SoCC-Skills/skills/socc-reproducibility into .opencode/skills/socc-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "socc-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
socc-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 591 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when strengthening ACM SoCC reproducibility, covering the testbed and workload description, released code and traces, provenance pinning for measurement studies, reproducing…
- Strengthening ACM SoCC reproducibility
- SKILL.md covers Evidence map, Reproducibility statement audit, Provenance pinning and Degrees of reproducibility…, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering the testbed and workload description
What it does
Socc Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening ACM SoCC reproducibility, covering the testbed and workload description, released code and traces, provenance pinning for measurement studies, reproducing tail-latency and cost (not just the mean), claim-to-evidence mapping, honest degrees of reproducibility, and consistency between what the paper reports and what the artifact regenerates.
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 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 ACM SoCC reproducibility
- Covering the testbed and workload description
- Released code and traces
- Provenance pinning for measurement studies
Example prompts
- “/socc-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
Socc Reproducibility loads about 1.4k tokens when it runs. Until then it costs about 96 tokens; SKILL.md has 591 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). 591 words, ~1,412 tokens.
Download SKILL.mdSave it as
.claude/skills/socc-reproducibility/SKILL.md (or your agent's skills folder).- name
- socc-reproducibility
- description
- Use when strengthening ACM SoCC reproducibility, covering the testbed and workload description, released code and traces, provenance pinning for measurement studies, reproducing tail-latency and cost (not just the mean), claim-to-evidence mapping, honest degrees of reproducibility, and consistency between what the paper reports and what the artifact regenerates.
SoCC Reproducibility
Use this before submission and again before camera-ready. SoCC — the joint SIGMOD+SIGOPS cloud symposium — is read by reviewers who expect an inspectable measurement trail: the SIGOPS half wants to believe the system runs, and the SIGMOD half wants to believe the numbers. The goal is that a competent reader with a comparable testbed could rebuild your evidence and reach your conclusions — including the tail latency and cost, not just the average.
Evidence map
- Map each cloud claim and reported number to a verifiable location — a paper section, a figure generated from logged runs, or a script in the artifact.
- For systems, give enough of the mechanism, configuration, and testbed (node counts, instance types, OS/kernel, network) that a reader could re-deploy and re-measure.
- For measurement/trace studies, report the trace provenance, extraction date, filtering, the workload replay, metrics, and the analysis scripts.
- Reproduce tail and cost: percentiles (p95/p99/p99.9), the pricing model behind any cost claim, and the number of runs and variance — the mean alone is not a cloud result.
- Keep the paper and the artifact consistent: a number in the PDF that no script regenerates is the contradiction reviewers read as carelessness.
Reproducibility statement audit
| Claim in the paper | Weak answer | SoCC-ready answer |
|---|---|---|
| "We evaluate on a production trace" | "Trace available on request" | Anonymized (then released) trace + the replay harness and extraction date |
| "Our system improves throughput" | "Code will be released" | Anonymized, runnable system with a testbed description and a small demo |
| "We cut cost by X" | A single cost number | The pricing model, the instance-seconds logged, and the script that computes it |
| "p99 stays within target" | Mean latency only | Per-run tail percentiles with variance and run count |
| "Scales to N nodes" | One large run | A scaled reproduction path plus the full-scale logs |
"Available on request" is treated as not available; convert every such line into a concrete, anonymized (then released) artifact or an explicit, justified exception (e.g., a confidential production trace, with a synthetic generator provided instead).
Provenance pinning
[Measurement] pin commit SHAs; record trace extraction dates; archive the replayed trace or a
faithful generator, not just a query or a pointer
[Testbed] record node counts, instance types, OS/kernel versions, network, and the run count;
a cloud result that cannot be re-deployed cannot be reproduced
[Cost] state the pricing model and the source of every cost figure so a reader can recompute
[Tail] log per-request or per-run latency distributions, not only aggregates
[Randomness] log seeds for stochastic components; say what is and is not deterministicShow full SKILL.md (260 more words)Show less
Degrees of reproducibility (state the one you achieved)
- Turnkey: one documented command regenerates each figure (including tail and cost) from logged runs, at least at reduced scale.
- Scripted: scripts exist but require a specific testbed, documented manual steps, or restricted trace access.
- Descriptive: prose and configuration detailed enough that a competent reader could rebuild the deployment and measurement pipeline.
For SoCC, aim turnkey for anything an evaluator could rerun at small scale (a short trace replay, a tail/cost plot from logged runs); full-cluster or proprietary-trace results may stay scripted with access clearly documented. Stating the achieved level honestly beats promising turnkey behavior that fails on someone else's testbed.
Vignette: a scheduling measurement paper
Consider a paper measuring a new scheduler on a replayed production trace. Its reproducibility spine: the scheduler code with pinned SHAs; the replay harness and the (anonymized, then released) trace with its extraction date; the testbed description (nodes, instance types, kernel); the measurement scripts that turn raw logs into the throughput, p99, and cost figures; the run count and variance; and one honest sentence about the parts (a confidential production trace, the full cluster) that cannot be shared and what synthetic or scaled substitute is provided.
Consistency and camera-ready pass
- Before submission: every reported number traces to the artifact; the reproducibility statement matches reality; the artifact is anonymized (no cluster names, provider hints, or trace provenance that reveals identity).
- Before camera-ready: swap anonymized links for permanent, DOI-issuing archives, and align the
statement with the ACM badges you are pursuing if the edition offers evaluation
(
socc-artifact-evaluation).
Output format
[Claim inventory] <claim -> evidence location>
[Reproducibility statement] concrete / vague / missing
[Provenance gaps] <trace SHAs+dates / testbed description / cost model / tail logging / seeds>
[Tail + cost] reproducible, not just the mean? yes/no
[Reproducibility level] turnkey / scripted / descriptive, stated honestly
[Paper fixes] <must appear in the PDF>
[Artifact fixes] <additions before upload>© 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 SoCC-Skills/skills/socc-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Socc 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 |
|---|---|---|---|---|---|---|
| Socc Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.4k | 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 |
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Compute Environment Setup
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Figure Style
aipoch/open-science
Publication-grade correctness and legibility rules for final-deliverable scientific figures, not exploratory plots.
Add Bactopia Tool
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Scaffold a complete Bactopia Tool across all three tiers -- module, subworkflow, and workflow entry point under workflows/bactopia-tools/.
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Categories
Questions about Socc Reproducibility
What does Socc Reproducibility do?
A skill your agent uses when strengthening ACM SoCC reproducibility, covering the testbed and workload description, released code and traces, provenance pinning for measurement studies, reproducing…. Socc Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening ACM SoCC reproducibility, covering the testbed and workload description, released code and traces, provenance pinning for measurement studies, reproducing tail-latency and cost (not just the mean), claim-to-evidence mapping, honest degrees of reproducibility, and consistency between what the paper reports and what the artifact regenerates.
When should I use Socc Reproducibility?
Socc Reproducibility fits situations like: strengthening ACM SoCC reproducibility; covering the testbed and workload description; released code and traces; provenance pinning for measurement studies.
How do I install Socc Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill socc-reproducibility -a claude-code`. Or copy the skill folder (SoCC-Skills/skills/socc-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/socc-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Socc Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill socc-reproducibility -a codex`. Or copy the skill folder (SoCC-Skills/skills/socc-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/socc-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Socc 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 socc-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/socc-reproducibility, .gemini/skills/socc-reproducibility, .github/skills/socc-reproducibility and .opencode/skills/socc-reproducibility in your project.
What does Socc Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Socc Reproducibility is instructions for the agent only.
Does Socc 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 Socc 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 Socc Reproducibility use?
Socc 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 Socc Reproducibility 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 Socc Reproducibility?
Skills that share tags, products or a category with Socc 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 Socc 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.