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 making a CoRL robot-learning paper reproducible — pinning simulator and driver versions, releasing training configs, demonstration data and checkpoints, documenting…
Install Corl Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill corl-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills corl-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/CoRL-Skills/skills/corl-reproducibility .claude/skills/corl-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 "corl-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CoRL-Skills/skills/corl-reproducibility into .claude/skills/corl-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "corl-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/CoRL-Skills/skills/corl-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 corl-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills corl-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/CoRL-Skills/skills/corl-reproducibility .agents/skills/corl-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 "corl-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CoRL-Skills/skills/corl-reproducibility into .agents/skills/corl-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "corl-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 corl-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills corl-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/CoRL-Skills/skills/corl-reproducibility .cursor/skills/corl-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 "corl-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CoRL-Skills/skills/corl-reproducibility into .cursor/skills/corl-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "corl-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 CoRL-Skills/skills/corl-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 corl-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills corl-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/CoRL-Skills/skills/corl-reproducibility .gemini/skills/corl-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 "corl-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CoRL-Skills/skills/corl-reproducibility into .gemini/skills/corl-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "corl-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 corl-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 corl-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/CoRL-Skills/skills/corl-reproducibility .github/skills/corl-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 "corl-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CoRL-Skills/skills/corl-reproducibility into .github/skills/corl-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "corl-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 corl-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 corl-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/CoRL-Skills/skills/corl-reproducibility .opencode/skills/corl-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 "corl-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CoRL-Skills/skills/corl-reproducibility into .opencode/skills/corl-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "corl-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
corl-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.6k tokens
- SKILL.md length
- 567 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when making a CoRL robot-learning paper reproducible — pinning simulator and driver versions, releasing training configs, demonstration data and checkpoints, documenting…
- Making a CoRL robot-learning paper reproducible — pinning simulator and driver versions
- SKILL.md covers The rerunnable half:…, The auditable half: hardware, Data and checkpoint release and During review vs after…, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Releasing training configs
What it does
Corl Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when making a CoRL robot-learning paper reproducible — pinning simulator and driver versions, releasing training configs, demonstration data and checkpoints, documenting hardware setups that cannot be rerun, seed policy, evaluation scripts, and honest availability statements for code, data, and robot platforms.
Its SKILL.md is about 1.6k 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
- Making a CoRL robot-learning paper reproducible — pinning simulator and driver versions
- Releasing training configs
- Demonstration data and checkpoints
- Documenting hardware setups that cannot be rerun
Example prompts
- “/corl-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 (its code samples are yaml).
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
corl.org
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
Corl Reproducibility loads about 1.6k tokens when it runs. Until then it costs about 84 tokens; SKILL.md has 567 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). 567 words, ~1,561 tokens.
Download SKILL.mdSave it as
.claude/skills/corl-reproducibility/SKILL.md (or your agent's skills folder).- name
- corl-reproducibility
- description
- Use when making a CoRL robot-learning paper reproducible — pinning simulator and driver versions, releasing training configs, demonstration data and checkpoints, documenting hardware setups that cannot be rerun, seed policy, evaluation scripts, and honest availability statements for code, data, and robot platforms.
CoRL Reproducibility
Robot-learning papers have a split reproducibility problem: the training half is software and can in principle be rerun anywhere, while the hardware half is a physical setup nobody else owns. Strong CoRL papers treat these halves differently — the software half is made rerunnable, the hardware half is made auditable — and say plainly which is which.
The rerunnable half: simulation and training
The silent reproducibility killers in this field are version-shaped:
- Simulator versions change physics. Contact solvers, default damping, and collision margins shift between releases of MuJoCo, Isaac, PyBullet, and friends; a policy's success rate is a function of the simulator build. Pin the exact version and any physics-relevant flags.
- Environment wrappers drift. Task-suite repositories (benchmark forks, custom reward shims) move under you; record the commit hash of every env repo, not just your own.
- GPU nondeterminism. cuDNN autotuning and atomics make bit-identical training runs unrealistic — so define reproducibility at the distribution level: same configs + fresh seeds should land inside your reported seed spread.
# repro-manifest.yaml — ship at the repo root; one block per results table
table_3:
code_commit: "" # your repo @ exact hash
env_suite: {repo: "", commit: "", sim: "mujoco==X.Y.Z", physics_flags: []}
training: {config: "configs/table3.yaml", seeds: [0,1,2,3,4], gpu: "1xA100-80GB", hours: 14}
data: {demos: "dataset-v2 (1,204 episodes)", url_or_status: "", license: ""}
checkpoints: {released: true, path: "ckpts/table3/", selection_rule: "last epoch, no eval peeking"}
evaluation: {script: "eval/run.py", episodes_per_task: 50, init_state_list: "eval/states.json"}
expected_tolerance: "per-task success within ±4 pts of Table 3 mean"The selection_rule line matters more than it looks: checkpoint selection via
test-set peeking is the field's quiet irreproducibility engine, and stating the
rule is the cheapest credibility purchase available.
The auditable half: hardware
Nobody will re-run your robot, so the goal is that a skeptical expert could verify the experiment happened as described and rebuild an equivalent rig:
| What to document | Why an auditor needs it |
|---|---|
| Robot model, end-effector, firmware/driver versions | Behavior differs across firmware, not just robots |
| Sensor models, mounting poses, calibration procedure | Camera extrinsics silently dominate visuomotor results |
| Control interface: frequency, action space, safety filters | "30 Hz end-effector deltas" vs "torque control" are different papers |
| Deployed compute + inference latency | Policy behavior is latency-dependent |
| Scene inventory: objects (make/size), fixtures, lighting | Enables an equivalent-rig rebuild and honest comparison |
| Raw episode logs and unedited evaluation video | The audit trail for every printed success rate |
Log every evaluation episode at capture time (timestamped video plus a CSV of outcomes); retrofitting an audit trail after reviews ask for it is impossible.
Show full SKILL.md (234 more words)Show less
Data and checkpoint release
- Demonstration datasets are results: release episode counts, collection method (teleop rig, scripted, crowdsourced), operator count, and filtering rules.
- Released checkpoints let others reproduce evaluation even when training is too expensive to repeat — for large policies this is often the highest-value artifact you can ship.
- If data or weights cannot be released (proprietary platform, human-subject footage), say so in the paper with the reason, and release what remains: configs, eval scripts, sim environments, and metrics logs. A precise partial-release statement outperforms a vague "code available upon request."
During review vs after acceptance
- During review everything must be anonymous (see
corl-submission): anonymized repo mirrors, no lab-identifying video, no cloud buckets with named projects. - The 2026 camera-ready pipeline has a sharp constraint: PMLR does not accept video as supplementary material, so post-acceptance videos, code, and data live on external hosting (project site, GitHub, archive), linked from the main text (corl.org author instructions, read 2026-07-08). Plan the public homes of artifacts before the October camera-ready deadline, and prefer DOI-stamped archives for anything you cite as permanent.
- No formal reproducibility checklist was verified for the 2026 cycle in this pack (待核实) — but reviewer expectations enforce one informally; use the manifest above regardless of what the form requires.
Availability statement patterns
Strong: "Code, training configs, evaluation scripts, and the 1,204-episode
teleop dataset: <URL>. Checkpoints for Tables 2-4: <URL>. Hardware
evaluations are documented in Appendix C (rig spec, logs, uncut video);
the platform itself cannot be redistributed."
Weak: "Code will be released upon acceptance." (unverifiable promise)
Broken: "Results reproducible with standard settings." (no artifact at all)Pre-submission audit
[ ] repro-manifest present; one block per headline table
[ ] Simulator/env/driver versions + commits pinned everywhere
[ ] Seed policy stated; checkpoint selection rule stated
[ ] Hardware rig spec complete enough for an equivalent rebuild
[ ] Episode-level logs + uncut eval video archived internally
[ ] Data/checkpoint release plan with named blockers, if any
[ ] Anonymous during review; external hosting plan ready for camera-readyVerify the live cycle's supplementary and camera-ready rules at https://www.corl.org/contributions/instruction-for-authors before promising any artifact channel — PMLR-side constraints and CoRL-side forms both change yearly.
© 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 CoRL-Skills/skills/corl-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Corl 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 |
|---|---|---|---|---|---|---|
| Corl Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.6k | 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
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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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Categories
Questions about Corl Reproducibility
What does Corl Reproducibility do?
A skill your agent uses when making a CoRL robot-learning paper reproducible — pinning simulator and driver versions, releasing training configs, demonstration data and checkpoints, documenting…. Corl Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when making a CoRL robot-learning paper reproducible — pinning simulator and driver versions, releasing training configs, demonstration data and checkpoints, documenting hardware setups that cannot be rerun, seed policy, evaluation scripts, and honest availability statements for code, data, and robot platforms.
When should I use Corl Reproducibility?
Corl Reproducibility fits situations like: making a CoRL robot-learning paper reproducible — pinning simulator and driver versions; releasing training configs; demonstration data and checkpoints; documenting hardware setups that cannot be rerun.
How do I install Corl Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill corl-reproducibility -a claude-code`. Or copy the skill folder (CoRL-Skills/skills/corl-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/corl-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Corl Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill corl-reproducibility -a codex`. Or copy the skill folder (CoRL-Skills/skills/corl-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/corl-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Corl 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 corl-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/corl-reproducibility, .gemini/skills/corl-reproducibility, .github/skills/corl-reproducibility and .opencode/skills/corl-reproducibility in your project.
What does Corl Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Corl Reproducibility is instructions for the agent only.
Does Corl Reproducibility access the network?
SKILL.md names 1 domain. As links in the text: corl.org. This is read from the text; nothing was executed.
Is Corl 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 Corl Reproducibility use?
Corl 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 Corl Reproducibility use?
About 1.6k tokens (SKILL.md is roughly 6.2k 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 Corl Reproducibility?
Skills that share tags, products or a category with Corl 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 Corl 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.