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Agent skill

Replication Driven Research

by brycewang-stanford in brycewang-stanford/Auto-Empirical-Research-Skills

A skill your agent uses when starting empirical analysis, creating a data pipeline, generating results, or when data or model specifications change.

Custom licenceAuto-check passedTesting & QA

Install Replication Driven Research

skills CLI
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill replication-driven-research -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills replication-driven-research --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ 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/60-regisely-superpapers/skills/replication-driven-research .claude/skills/replication-driven-research && rm -rf skills-src

Use ~/.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/

Facts

Skill name
replication-driven-research
GitHub stars
4.5k
Token cost
~1.7k tokens
SKILL.md length
693 words
Files
1
Skills in repo
369
Repo updated
First seen
Licence
Custom licence

At a glance

A skill your agent uses when starting empirical analysis, creating a data pipeline, generating results, or when data or model specifications change.

  • Works in 7 steps: Verify or scaffold structure. If… → Document every dataset in… → Every result must have a generating… → …
  • Starting empirical analysis
  • SKILL.md covers Overview, When to Use, Canonical Directory Structure and Mandatory Steps, plus 4 more sections
  • Reaches sidra.ibge.gov.br

What it does

Replication Driven Research is an agent skill from brycewang-stanford/Auto-Empirical-Research-Skills. Use when starting empirical analysis, creating a data pipeline, generating results, or when data or model specifications change. Enforces end-to-end reproducibility — every number in the paper must be regenerable from raw data by a script with a fixed seed. Replaces TDD for the research domain.

Its SKILL.md is about 1.7k 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 Testing & QA, covering Test-driven development, Database administration and Data pipelines and ETL. 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

  • Starting empirical analysis
  • Creating a data pipeline
  • Generating results
  • Model specifications change

Example prompts

  • “/replication-driven-research”

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Verify or scaffold structure. If data/raw, code/, and output/ do not exist, propose scaffolding. Wait for user confirmation before…
  2. Document every dataset in data/manifest.md. Required fields per dataset: name, source (URL or API endpoint), description, collection date…
  3. Every result must have a generating script. No exceptions. Pasting numbers from a console or notebook into the paper is forbidden. Tables…
  4. Fix the seed in every script that uses randomness. Document the seed in the script header. Use the project-level default seed from…
  5. Run the pipeline end-to-end before declaring any result verified. Use a top-level run_all.sh (or Makefile) that executes scripts in the…
  6. Log every run. Each end-to-end execution writes to output/logs/YYYY-MM-DD_HH-MM-SS.log with: timestamp, seed, relevant package versions…
  7. Invalidate on input change. If data/raw or any script in code/ changes, all downstream outputs are stale. Re-run the full pipeline. Do not…

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

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • sidra.ibge.gov.br

    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

Replication Driven Research loads about 1.7k tokens when it runs. Until then it costs about 81 tokens; SKILL.md has 693 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~81
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k

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 693 words (~1,690 tokens).

“This skill is the research-domain analogue of test-driven development. The core philosophy is the same as TDD: evidence before claims, automated verification, invalidation on input change. No result is valid until the pipeline runs end-to-end without error. No number enters…”

— opening of SKILL.md by brycewang-stanford, Custom licence
name
replication-driven-research

Read the full SKILL.md on GitHub

Files

Just SKILL.md in skills/60-regisely-superpapers/skills/replication-driven-research of brycewang-stanford/Auto-Empirical-Research-Skills.

Open the folder on GitHubat commit 9fa87d8

Compare with similar skills

Replication Driven Research 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.

Replication Driven Research compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Replication Driven Research this skillbrycewang-stanford/Auto-Empirical-Research-Skills4.5k—~1.7kAutomated safety check: PassCustom licence
Pest Testingliberusoftware/real-estate-laravel1121 repos~1.8kAutomated safety check: PassMIT
Batch Processing Clinical Textmaziyarpanahi/openmed5.5k—~2.2kAutomated safety check: PassApache-2.0
Functionalcitypaul/.dotfiles739—~3.6kAutomated safety check: PassCustom licence
Prd V07 Test Planningmattgierhart/PRD-driven-context-engineering179—~3.5kAutomated safety check: NotesMIT
Ddia Systemswondelai/skills2.4k—~4.2kAutomated safety check: PassMIT

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Categories

Questions about Replication Driven Research

What does Replication Driven Research do?

A skill your agent uses when starting empirical analysis, creating a data pipeline, generating results, or when data or model specifications change. Replication Driven Research is an agent skill from brycewang-stanford/Auto-Empirical-Research-Skills. Use when starting empirical analysis, creating a data pipeline, generating results, or when data or model specifications change.

When should I use Replication Driven Research?

Replication Driven Research fits situations like: starting empirical analysis; creating a data pipeline; generating results; model specifications change.

How do I install Replication Driven Research in Claude Code?

Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill replication-driven-research -a claude-code`. Or copy the skill folder (skills/60-regisely-superpapers/skills/replication-driven-research in brycewang-stanford/Auto-Empirical-Research-Skills) into .claude/skills/replication-driven-research in your project. Claude Code loads it when a task matches its description.

How do I install Replication Driven Research in Codex?

Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill replication-driven-research -a codex`. Or copy the skill folder (skills/60-regisely-superpapers/skills/replication-driven-research in brycewang-stanford/Auto-Empirical-Research-Skills) into .agents/skills/replication-driven-research in your project. Codex loads it when a task matches its description.

Can I use Replication Driven Research 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 replication-driven-research -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/replication-driven-research, .gemini/skills/replication-driven-research, .github/skills/replication-driven-research and .opencode/skills/replication-driven-research in your project.

What does Replication Driven Research need to run?

SKILL.md names no scripts, command-line tools or credentials: Replication Driven Research is instructions for the agent only.

Does Replication Driven Research access the network?

SKILL.md names 1 domain. In commands or code: sidra.ibge.gov.br; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Replication Driven Research 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 Replication Driven Research use?

Replication Driven Research 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 Replication Driven Research use?

About 1.7k tokens (SKILL.md is roughly 6.8k 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 Replication Driven Research?

Skills that share tags, products or a category with Replication Driven Research: Pest Testing (liberusoftware/real-estate-laravel, 112 stars), Batch Processing Clinical Text (maziyarpanahi/openmed, 5.5k stars), Functional (citypaul/.dotfiles, 739 stars) and Prd V07 Test Planning (mattgierhart/PRD-driven-context-engineering, 179 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Replication Driven Research?

brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Auto-Empirical-Research-Skills, which has 4,517 GitHub stars. The repository holds 369 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.