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 USENIX FAST reproducibility and open-science evidence, covering device and firmware provenance, device-state disclosure, trace availability and replay…
Install Fast Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fast-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills fast-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/FAST-Skills/skills/fast-reproducibility .claude/skills/fast-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 "fast-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAST-Skills/skills/fast-reproducibility into .claude/skills/fast-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fast-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/FAST-Skills/skills/fast-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 fast-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills fast-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/FAST-Skills/skills/fast-reproducibility .agents/skills/fast-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 "fast-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAST-Skills/skills/fast-reproducibility into .agents/skills/fast-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fast-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 fast-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills fast-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/FAST-Skills/skills/fast-reproducibility .cursor/skills/fast-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 "fast-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAST-Skills/skills/fast-reproducibility into .cursor/skills/fast-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fast-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 FAST-Skills/skills/fast-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 fast-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills fast-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/FAST-Skills/skills/fast-reproducibility .gemini/skills/fast-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 "fast-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAST-Skills/skills/fast-reproducibility into .gemini/skills/fast-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fast-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 fast-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 fast-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/FAST-Skills/skills/fast-reproducibility .github/skills/fast-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 "fast-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAST-Skills/skills/fast-reproducibility into .github/skills/fast-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fast-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 fast-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 fast-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/FAST-Skills/skills/fast-reproducibility .opencode/skills/fast-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 "fast-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAST-Skills/skills/fast-reproducibility into .opencode/skills/fast-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fast-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
fast-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.5k tokens
- SKILL.md length
- 638 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when strengthening USENIX FAST reproducibility and open-science evidence, covering device and firmware provenance, device-state disclosure, trace availability and replay…
- Strengthening USENIX FAST reproducibility and open-science evidence
- SKILL.md covers Evidence map, Availability statement audit, Provenance pinning and Degrees of reproducibility…, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering device and firmware provenance
What it does
Fast Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening USENIX FAST reproducibility and open-science evidence, covering device and firmware provenance, device-state disclosure, trace availability and replay, claim-to-evidence mapping, honest degrees of reproducibility on hardware that ages and varies, and consistency between what the paper says and what the artifact contains.
Its SKILL.md is about 1.5k 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 USENIX FAST reproducibility and open-science evidence
- Covering device and firmware provenance
- Device-state disclosure
- Trace availability and replay
Example prompts
- “/fast-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
Fast Reproducibility loads about 1.5k tokens when it runs. Until then it costs about 92 tokens; SKILL.md has 638 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). 638 words, ~1,502 tokens.
Download SKILL.mdSave it as
.claude/skills/fast-reproducibility/SKILL.md (or your agent's skills folder).- name
- fast-reproducibility
- description
- Use when strengthening USENIX FAST reproducibility and open-science evidence, covering device and firmware provenance, device-state disclosure, trace availability and replay, claim-to-evidence mapping, honest degrees of reproducibility on hardware that ages and varies, and consistency between what the paper says and what the artifact contains.
FAST Reproducibility
Use this before submission and again before camera-ready. FAST's storage results live on real hardware that ages, throttles, and varies part-to-part, so reproducibility here is a distinct craft from software-only venues: a reader reproducing your work needs to know not just the code but the device, its firmware, and its state. The goal is that a competent reader with comparable hardware could rebuild your evidence and reach your conclusions.
Evidence map
- Map each claim and reported number to a verifiable location — a paper section, a table generated from a logged run, or a script in the artifact.
- For a system, give enough of the design, parameters, mkfs/mount options, and build environment that a reader could rebuild and run it.
- For measurements, report the device provenance (models, firmware, interface), the host (CPU, RAM, kernel), and the device state (steady-state/aged, fill, TRIM) — the storage-only provenance that software artifacts omit.
- Keep the availability statement truthful and specific: what code and traces are shared, where they will live after acceptance, and — if something cannot be shared — exactly why.
- Keep the paper and the artifact consistent: a bytes-written or latency number in the PDF that no script in the artifact regenerates is the contradiction reviewers read as carelessness.
Availability statement audit
| Claim in the paper | Weak answer | FAST-ready answer |
|---|---|---|
| "We evaluate on SSDs A, B, C" | "Standard SSDs" | Exact models, capacities, and firmware versions in a testbed table |
| "Driven by trace T" | Trace named, not shared | Archived trace (or documented access) + the replay script and its settings |
| "We reduce write amplification" | Estimated in prose | Device-counter (SMART/log) dumps + the script that computes WA from them |
| "Steady-state results" | Unstated preconditioning | The preconditioning/aging protocol as a runnable script |
| "Our system is available" | "Code on request" | Anonymized, buildable code with a README and a small runnable demo |
"Available on request" is treated as not available; convert every such line into a concrete, anonymized artifact or an explicit, justified exception (e.g. drives under NDA, privacy-limited production traces).
Provenance pinning
[Devices] model, capacity, interface, FIRMWARE version; host CPU/RAM, kernel, mkfs/mount options
[State] preconditioning/aging protocol, fill level, TRIM/discard; FOB vs. steady-state disclosed
[Traces] archive the replayed trace or document access; ship the replay tool + timing settings
[Workloads] YCSB/filebench/fio job files with exact parameters and seeds
[Compute] run duration and repeats; note thermal/throttling conditions that affect timing
[Counters] how bytes-written / WA / GC were read from the device, so a reader can re-derive themShow full SKILL.md (307 more words)Show less
Degrees of reproducibility (state the one you achieved)
- Turnkey: one documented command regenerates a table/figure from logged data (best for analysis and plots).
- Scripted-on-hardware: scripts run end-to-end but require comparable devices and time (endurance runs, aging, long trace replays); document the hardware and expected runtime.
- Descriptive: prose and provenance detailed enough that a reader with the right hardware could rebuild the pipeline (acceptable for results that need specific or proprietary devices).
For FAST, aim turnkey for anything that runs from logged data, and be honest that device-bound results are scripted-on-hardware: state which drives, which firmware, and how long. Promising turnkey behavior that silently needs a specific SSD is worse than stating the hardware dependency.
The hardware-variance caveat (state it plainly)
Storage numbers are device- and firmware-specific; a reader on a different drive should expect the trend, not the exact factor. Say so, report per device rather than one blended number where it matters, and bound generalization — this is not a weakness to hide but the honest storage posture reviewers reward.
Vignette: a file-system aging study
Consider a study whose results depend on a fragmented, aged volume. Its reproducibility spine: the aging workload as a runnable script (so the fragmentation state is reconstructable), the exact mkfs/mount options and kernel, the device models and firmware, the measurement scripts that read from the device and file-system counters, and the analysis notebooks that turn logs into the paper's figures — plus one honest sentence about which numbers require the specific drives used.
Consistency and camera-ready pass
- Before submission: every scored number traces to the artifact; the availability statement matches reality; the artifact is anonymized (no owner strings, hostnames, cluster paths, or personal trace URLs).
- Before camera-ready: swap anonymized links for a permanent, licensed, DOI-issuing archive
(Zenodo/figshare/Software Heritage) and align the statement with the USENIX artifact badges you are
pursuing (
fast-artifact-evaluation).
Output format
[Claim inventory] <claim -> evidence location>
[Availability] concrete / vague / missing
[Provenance gaps] <device+firmware / device state / trace archival / counters / seeds>
[Reproducibility level] turnkey / scripted-on-hardware / descriptive, stated honestly
[Hardware dependency] <which results need which drives/firmware, stated?>
[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 FAST-Skills/skills/fast-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Fast 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 |
|---|---|---|---|---|---|---|
| Fast Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.5k | 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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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 Fast Reproducibility
What does Fast Reproducibility do?
A skill your agent uses when strengthening USENIX FAST reproducibility and open-science evidence, covering device and firmware provenance, device-state disclosure, trace availability and replay…. Fast Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening USENIX FAST reproducibility and open-science evidence, covering device and firmware provenance, device-state disclosure, trace availability and replay, claim-to-evidence mapping, honest degrees of reproducibility on hardware that ages and varies, and consistency between what the paper says and what the artifact contains.
When should I use Fast Reproducibility?
Fast Reproducibility fits situations like: strengthening USENIX FAST reproducibility and open-science evidence; covering device and firmware provenance; device-state disclosure; trace availability and replay.
How do I install Fast Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fast-reproducibility -a claude-code`. Or copy the skill folder (FAST-Skills/skills/fast-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/fast-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Fast Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fast-reproducibility -a codex`. Or copy the skill folder (FAST-Skills/skills/fast-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/fast-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Fast 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 fast-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/fast-reproducibility, .gemini/skills/fast-reproducibility, .github/skills/fast-reproducibility and .opencode/skills/fast-reproducibility in your project.
What does Fast Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Fast Reproducibility is instructions for the agent only.
Does Fast 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 Fast 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 Fast Reproducibility use?
Fast 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 Fast Reproducibility use?
About 1.5k tokens (SKILL.md is roughly 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 Fast Reproducibility?
Skills that share tags, products or a category with Fast 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 Fast 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.