Scholar Open
joshzyj/open-scholar-skill
Implement open science practices for a social science study.
Agent skill
Reproducible Pipelines
by brycewang-stanford in brycewang-stanford/Auto-Empirical-Research-Skills
This skill covers reproducible research pipelines and replication packages.
Install Reproducible Pipelines
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill reproducible-pipelines -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills reproducible-pipelines --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/Auto-Empirical-Research-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/11-James-Traina-compound-science/skills/reproducible-pipelines .claude/skills/reproducible-pipelines && 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 "reproducible-pipelines" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/11-James-Traina-compound-science/skills/reproducible-pipelines into .claude/skills/reproducible-pipelines/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "reproducible-pipelines", 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/Auto-Empirical-Research-Skills/tree/main/skills/11-James-Traina-compound-science/skills/reproducible-pipelinesType 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/Auto-Empirical-Research-Skills --skill reproducible-pipelines -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills reproducible-pipelines --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/11-James-Traina-compound-science/skills/reproducible-pipelines .agents/skills/reproducible-pipelines && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "reproducible-pipelines" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/11-James-Traina-compound-science/skills/reproducible-pipelines into .agents/skills/reproducible-pipelines/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "reproducible-pipelines", 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/Auto-Empirical-Research-Skills --skill reproducible-pipelines -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills reproducible-pipelines --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/11-James-Traina-compound-science/skills/reproducible-pipelines .cursor/skills/reproducible-pipelines && 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 "reproducible-pipelines" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/11-James-Traina-compound-science/skills/reproducible-pipelines into .cursor/skills/reproducible-pipelines/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "reproducible-pipelines", 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/Auto-Empirical-Research-Skills.git --path skills/11-James-Traina-compound-science/skills/reproducible-pipelines--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/Auto-Empirical-Research-Skills --skill reproducible-pipelines -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills reproducible-pipelines --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/11-James-Traina-compound-science/skills/reproducible-pipelines .gemini/skills/reproducible-pipelines && 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 "reproducible-pipelines" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/11-James-Traina-compound-science/skills/reproducible-pipelines into .gemini/skills/reproducible-pipelines/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "reproducible-pipelines", 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/Auto-Empirical-Research-Skills reproducible-pipelinesInstalls 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/Auto-Empirical-Research-Skills --skill reproducible-pipelines -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/11-James-Traina-compound-science/skills/reproducible-pipelines .github/skills/reproducible-pipelines && 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 "reproducible-pipelines" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/11-James-Traina-compound-science/skills/reproducible-pipelines into .github/skills/reproducible-pipelines/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "reproducible-pipelines", 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/Auto-Empirical-Research-Skills --skill reproducible-pipelines -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/Auto-Empirical-Research-Skills reproducible-pipelines --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/11-James-Traina-compound-science/skills/reproducible-pipelines .opencode/skills/reproducible-pipelines && 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 "reproducible-pipelines" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/11-James-Traina-compound-science/skills/reproducible-pipelines into .opencode/skills/reproducible-pipelines/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "reproducible-pipelines", 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
reproducible-pipelines- GitHub stars
- 4.6k
- Token cost
- ~3.3k tokens
- SKILL.md length
- 784 words
- Files
- 4 (incl. references)
- Skills in repo
- 383
- Repo updated
- First seen
- Licence
- Custom licence
At a glance
This skill covers reproducible research pipelines and replication packages.
- The user is setting up a research project directory structure
- SKILL.md covers When to Use This Skill, Where to Start, Project Directory Structure and Workflow Managers, plus 2 more sections
- Calls make and pip
- Configuring workflow managers (Make
What it does
Reproducible Pipelines is an agent skill from brycewang-stanford/Auto-Empirical-Research-Skills. This skill covers reproducible research pipelines and replication packages. Use when the user is setting up a research project directory structure, configuring workflow managers (Make, Snakemake, DVC), managing computational environments, preparing replication packages for journal submission, or debugging reproducibility failures. Triggers on "reproducible", "replication package", "Makefile", "Snakemake", "DVC", "pipeline", "workflow manager", "data versioning", "conda environment", "Docker", "seed management"…
Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/environment-and-seeds.md`, `references/replication-package.md` and `references/stata-and-crosslang.md`).
It sits in Research & Science, covering Reproducible research and Econometrics and empirical research. It works with Docker. The repository describes itself as: 🔬 A curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines. | 精选 23,000+ AI Agent 技能库，覆盖8大社会科学学科的实证研究。CoPaper.AI…
When your agent uses it
- The user is setting up a research project directory structure
- Configuring workflow managers (Make
- Managing computational environments
- Preparing replication packages for journal submission
Example prompts
- “reproducible”
- “replication package”
- “Makefile”
- “/reproducible-pipelines”
Requirements
- Python 3
- Docker
What it can do on your machine
Read from SKILL.md and the folder at commit 9fa87d8. 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
Shell commands in SKILL.md call:
makepip
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.
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
Reproducible Pipelines loads about 3.3k tokens when it runs, and up to ~7.2k if it reads all its reference files. Until then it costs about 155 tokens; SKILL.md has 784 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
Its licence (Custom licence) doesn't allow us to republish the file, so here is its outline and opening line. It has 784 words (~3,262 tokens).
“Reference for building reproducible research pipelines: from project directory structure to automated workflows to journal-ready replication packages. Every computational result should be regenerable from raw data by running a single command.”
- name
- reproducible-pipelines
- argument-hint
- <pipeline tool or reproducibility concern>
Files
SKILL.md and 3 other files (references) in skills/11-James-Traina-compound-science/skills/reproducible-pipelines of brycewang-stanford/Auto-Empirical-Research-Skills.
- SKILL.md
- references/environment-and-seeds.md
- references/replication-package.md
- references/stata-and-crosslang.md
Open the folder on GitHubat commit 9fa87d8
Compare with similar skills
Reproducible Pipelines 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 |
|---|---|---|---|---|---|---|
| Reproducible Pipelines this skillbrycewang-stanford/Auto-Empirical-Research-Skills | 4.6k | — | ~3.3k | Automated safety check: Pass | Custom licence | |
| Scholar Openjoshzyj/open-scholar-skill | 168 | — | ~14k | Automated safety check: Pass | Custom licence | |
| Capture Environmentpedrohcgs/claude-code-my-workflow | 1.7k | — | ~2.8k | Automated safety check: Notes | MIT | |
| Repro EnforcerClawBio/ClawBio | 1.2k | 3 repos | ~413 | Automated safety check: Pass | MIT | |
| Replication Packagepedrohcgs/claude-code-my-workflow | 1.7k | — | ~2.8k | Automated safety check: Notes | MIT | |
| Audit Reproducibilitypedrohcgs/claude-code-my-workflow | 1.7k | — | ~6.4k | Automated safety check: Notes | MIT |
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Related
Works with
Categories
Questions about Reproducible Pipelines
What does Reproducible Pipelines do?
This skill covers reproducible research pipelines and replication packages. Reproducible Pipelines is an agent skill from brycewang-stanford/Auto-Empirical-Research-Skills. This skill covers reproducible research pipelines and replication packages.
When should I use Reproducible Pipelines?
Reproducible Pipelines fits situations like: the user is setting up a research project directory structure; configuring workflow managers (Make; managing computational environments; preparing replication packages for journal submission.
How do I install Reproducible Pipelines in Claude Code?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill reproducible-pipelines -a claude-code`. Or copy the skill folder (skills/11-James-Traina-compound-science/skills/reproducible-pipelines in brycewang-stanford/Auto-Empirical-Research-Skills) into .claude/skills/reproducible-pipelines in your project. Claude Code loads it when a task matches its description.
How do I install Reproducible Pipelines in Codex?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill reproducible-pipelines -a codex`. Or copy the skill folder (skills/11-James-Traina-compound-science/skills/reproducible-pipelines in brycewang-stanford/Auto-Empirical-Research-Skills) into .agents/skills/reproducible-pipelines in your project. Codex loads it when a task matches its description.
Can I use Reproducible Pipelines 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/Auto-Empirical-Research-Skills --skill reproducible-pipelines -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/reproducible-pipelines, .gemini/skills/reproducible-pipelines, .github/skills/reproducible-pipelines and .opencode/skills/reproducible-pipelines in your project.
What does Reproducible Pipelines need to run?
Going by SKILL.md and its folder, Reproducible Pipelines needs the command-line tools its instructions call (make and pip). Our summary lists: Python 3; Docker.
Does Reproducible Pipelines access the network?
SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Reproducible Pipelines 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 Reproducible Pipelines use?
Reproducible Pipelines has a licence file (the repository's licence) that doesn't match a standard licence. Read it on GitHub before reusing the skill.
How many tokens does Reproducible Pipelines use?
About 3.3k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.9k tokens, read only when the agent opens those files.
What are the alternatives to Reproducible Pipelines?
Skills that share tags, products or a category with Reproducible Pipelines: Scholar Open (joshzyj/open-scholar-skill, 168 stars), Capture Environment (pedrohcgs/claude-code-my-workflow, 1.7k stars), Repro Enforcer (ClawBio/ClawBio, 1.2k stars) and Replication Package (pedrohcgs/claude-code-my-workflow, 1.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Reproducible Pipelines?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Auto-Empirical-Research-Skills, which has 4,556 GitHub stars. The repository holds 383 skills in this directory. The repository was last updated on October 5, 2026.
Source: brycewang-stanford/Auto-Empirical-Research-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.