Use this Skill for research version control: Git workflow for analysis code, DVC data pipelines, GitHub Actions for reproducibility CI, and .env secrets management.
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Use this Skill for research version control: Git workflow for analysis code, DVC data pipelines, GitHub Actions for reproducibility CI, and .env secrets management.
Version Control for Research — Git, DVC, GitHub Actions, and Secrets
TL;DR — Apply software-engineering best practices to research code:
structured Git branching (main/dev/feature), semantic versioning for paper
submissions, DVC experiment tracking, dvc.yaml pipelines, GitHub Actions CI
for reproducibility checks, pre-commit hooks (black, flake8, nbstripout),
and safe .env secrets management via python-dotenv.
When to Use This Skill
Use this Skill whenever you need to:
Set up a Git repository structure for a research analysis project from scratch
Apply a branching strategy that separates stable code from active development
Keep API keys, database passwords, and tokens out of version control
Track large data files and ML model artifacts with DVC alongside Git commits
Define a reproducible analysis pipeline with dvc.yaml and a params.yaml config
Run DVC experiments with parameter sweeps and compare results
Set up GitHub Actions to automatically re-run analyses and validate outputs on push
Configure pre-commit hooks to enforce code style and strip Jupyter notebook outputs
Tag Git commits at the point of paper submission with semantic version numbers
Task
When to apply
Git branching setup
Start of every new research project
.gitignore + .env
Before the first commit
DVC pipeline
When analysis has ≥ 2 sequential steps or large data files
GitHub Actions CI
When collaboration or long-term reproducibility is required
pre-commit hooks
Any project with Python code or Jupyter notebooks
Semantic version tag
At paper submission, revision, and acceptance
Background & Key Concepts
Git Branching for Research
Research projects benefit from a simplified Git flow:
Branch
Purpose
Rules
main
Stable, reproducible code tied to paper versions
Only merge via PR; tag at submission
dev
Active analysis development
Direct commits allowed; merged to main when stable
feature/<name>
Isolated experiment or new analysis
Branch from dev; merge back when complete
Naming convention for feature branches: feature/exp-transformer-ablation,
feature/add-sensitivity-analysis, feature/fix-cohort-filtering.
Semantic Versioning for Analysis Code
Use SemVer to tag the state of the codebase at key
research milestones:
Tag
Meaning
Trigger
v1.0.0
First paper submission
git tag -a v1.0.0 -m "Submission to Nature Methods"
v1.1.0
Major revision (new analyses added)
After reviewer-requested additions
v1.0.1
Minor fix (typo in script, updated figure)
Bug fix not affecting results
v2.0.0
Second paper using the same codebase
Incompatible structural changes
.env and Secrets Management
Never commit secrets to Git. Use a .env file that is .gitignore-listed and
load values at runtime with python-dotenv:
# Initialize Git and DVC in the project folder
git init research-project
cd research-project
dvc init
git add .dvc .dvcignore
git commit -m "chore: initialize Git and DVC"# Install pre-commit hooks defined in .pre-commit-config.yaml
pre-commit install
# Create initial branch structure
git checkout -b dev
git checkout main
Core Workflow
Step 1 — .gitignore, .env Setup, and dotenv Loading
import os
import pathlib
from dotenv import load_dotenv
# ── .gitignore template for research projects ─────────────────────────────────
GITIGNORE_CONTENT = """
# Data and outputs (tracked with DVC instead)
data/
outputs/
results/
models/
*.pkl
*.h5
*.parquet
# Secrets — NEVER commit these
.env
.env.local
*.pem
*_credentials.json
# Python artifacts
__pycache__/
*.py[cod]
.venv/
venv/
*.egg-info/
dist/
build/
# Jupyter notebooks (outputs stripped by nbstripout pre-commit hook)
.ipynb_checkpoints/
# OS and editor artifacts
.DS_Store
Thumbs.db
.idea/
.vscode/
*.swp
# DVC cache (managed by DVC itself)
.dvc/tmp/
""".lstrip()
# ── .env.example (committed; no real values) ──────────────────────────────────
ENV_EXAMPLE_CONTENT = """
# Copy this file to .env and fill in real values.
# export OPENAI_API_KEY="<paste-your-key>"
OPENAI_API_KEY=
# export ANTHROPIC_API_KEY="<paste-your-key>"
ANTHROPIC_API_KEY=
# export QUALTRICS_API_TOKEN="<paste-your-token>"
QUALTRICS_API_TOKEN=
# export QUALTRICS_DATA_CENTER="<your-datacenter-id>"
QUALTRICS_DATA_CENTER=
# export DATABASE_URL="<connection-string>"
DATABASE_URL=
# export DVC_REMOTE_URL="<storage-url>"
DVC_REMOTE_URL=
""".lstrip()
defwrite_gitignore(path: str = ".gitignore") -> None:
"""Write a research-appropriate .gitignore file."""withopen(path, "w", encoding="utf-8") as fh:
fh.write(GITIGNORE_CONTENT)
print(f"Written: {path}")
defwrite_env_example(path: str = ".env.example") -> None:
"""Write a .env.example template (safe to commit)."""withopen(path, "w", encoding="utf-8") as fh:
fh.write(ENV_EXAMPLE_CONTENT)
print(f"Written: {path}")
defload_env_safely(env_file: str = ".env") -> dict[str, str]:
"""
Load environment variables from .env file using python-dotenv.
The .env file must NOT be committed to Git. This function loads
variables into os.environ and returns a sanitized summary.
Args:
env_file: Path to the .env file (default: '.env' in the working directory).
Returns:
Dictionary mapping variable names to presence status ('SET' or 'NOT SET').
"""
env_path = pathlib.Path(env_file)
ifnot env_path.exists():
print(f"Warning: {env_file} not found. Using system environment variables.")
else:
load_dotenv(dotenv_path=env_path, override=False)
# Check presence of expected keys without exposing values
expected_keys = [
"OPENAI_API_KEY",
"ANTHROPIC_API_KEY",
"QUALTRICS_API_TOKEN",
"DATABASE_URL",
"DVC_REMOTE_URL",
]
status = {
k: "SET"if os.getenv(k, "") != ""else"NOT SET"for k in expected_keys
}
return status
if __name__ == "__main__":
write_gitignore()
write_env_example()
env_status = load_env_safely()
print("\nEnvironment variable status:")
for k, v in env_status.items():
print(f" {k}: {v}")
# Safe usage in analysis scripts:
api_key = os.getenv("OPENAI_API_KEY", "")
ifnot api_key:
print("\nOpenAI key not set — AI-assisted features will be skipped.")
Step 2 — dvc.yaml with Three Pipeline Stages and params.yaml
import textwrap
from pathlib import Path
DVC_YAML = textwrap.dedent("""
stages:
preprocess:
desc: "Load raw data, validate, clean, and split into train/test"
cmd: python scripts/preprocess.py
deps:
- data/raw/study_data.csv
- scripts/preprocess.py
- src/data_utils.py
params:
- params.yaml:
- preprocess.missing_threshold
- preprocess.test_size
- preprocess.random_state
outs:
- data/processed/train.csv
- data/processed/test.csv
- data/processed/feature_names.json
train:
desc: "Train the primary classification model"
cmd: python scripts/train.py
deps:
- data/processed/train.csv
- data/processed/feature_names.json
- scripts/train.py
- src/model_utils.py
params:
- params.yaml:
- train.model_type
- train.n_estimators
- train.max_depth
- train.min_samples_leaf
- train.random_state
- train.cv_folds
outs:
- models/model.pkl
- models/preprocessor.pkl
metrics:
- results/train_metrics.json:
cache: false
evaluate:
desc: "Evaluate model on held-out test set; generate SHAP plots"
cmd: python scripts/evaluate.py
deps:
- models/model.pkl
- models/preprocessor.pkl
- data/processed/test.csv
- scripts/evaluate.py
- src/explainability.py
params:
- params.yaml:
- evaluate.decision_threshold
- evaluate.shap_n_samples
outs:
- results/predictions.csv
- results/shap_summary.png
metrics:
- results/test_metrics.json:
cache: false
plots:
- results/roc_curve.csv:
cache: false
x: fpr
y: tpr
x_label: "False Positive Rate"
y_label: "True Positive Rate"
title: "ROC Curve"
""").lstrip()
PARAMS_YAML = textwrap.dedent("""
preprocess:
missing_threshold: 0.20 # Drop columns with > 20% missing values
test_size: 0.20 # Fraction of data held out as test set
random_state: 42
train:
model_type: random_forest
n_estimators: 200
max_depth: 10
min_samples_leaf: 5
random_state: 42
cv_folds: 5
evaluate:
decision_threshold: 0.50
shap_n_samples: 500 # Number of test samples for SHAP computation
""").lstrip()
PRE_COMMIT_CONFIG = textwrap.dedent("""
repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.6.0
hooks:
- id: trailing-whitespace
- id: end-of-file-fixer
- id: check-yaml
- id: check-added-large-files
args: ["--maxkb=500"]
- id: check-merge-conflict
- repo: https://github.com/psf/black
rev: 24.3.0
hooks:
- id: black
language_version: python3
args: ["--line-length=99"]
- repo: https://github.com/PyCQA/flake8
rev: 7.0.0
hooks:
- id: flake8
args: ["--max-line-length=99", "--extend-ignore=E203,W503"]
- repo: https://github.com/kynan/nbstripout
rev: 0.7.1
hooks:
- id: nbstripout
files: \\.ipynb$
""").lstrip()
defwrite_pipeline_files(root: str = ".") -> None:
"""
Write dvc.yaml, params.yaml, and .pre-commit-config.yaml to the project root.
Args:
root: Project root directory path.
"""
p = Path(root)
files = {
"dvc.yaml": DVC_YAML,
"params.yaml": PARAMS_YAML,
".pre-commit-config.yaml": PRE_COMMIT_CONFIG,
}
for filename, content in files.items():
fpath = p / filename
withopen(fpath, "w", encoding="utf-8") as fh:
fh.write(content)
print(f"Written: {fpath}")
print("\nNext steps:")
print(" pre-commit install # install hooks into .git/hooks")
print(" dvc repro # run the full pipeline")
print(" dvc exp run --set-param train.n_estimators=300 # experiment")
print(" dvc exp show # compare experiments")
if __name__ == "__main__":
write_pipeline_files()
Step 3 — GitHub Actions CI yaml for Reproducibility Check
import textwrap
from pathlib import Path
GITHUB_ACTIONS_CI = textwrap.dedent("""
name: Reproducibility CI
on:
push:
branches:
- main
- dev
pull_request:
branches:
- main
jobs:
reproduce-analysis:
name: Reproduce analysis pipeline
runs-on: ubuntu-latest
steps:
- name: Checkout repository
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: "3.11"
cache: "pip"
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -r requirements.txt
pip install dvc
- name: Configure DVC remote (read-only token)
env:
AWS_ACCESS_KEY_ID: ${{ secrets.DVC_AWS_ACCESS_KEY_ID }}
AWS_SECRET_ACCESS_KEY: ${{ secrets.DVC_AWS_SECRET_ACCESS_KEY }}
run: |
dvc remote modify myremote access_key_id $AWS_ACCESS_KEY_ID
dvc remote modify myremote secret_access_key $AWS_SECRET_ACCESS_KEY
- name: Pull DVC data
run: dvc pull
- name: Reproduce pipeline
run: dvc repro
- name: Validate test metrics
run: |
python - <<'PYEOF'
import json, sys
with open("results/test_metrics.json") as f:
metrics = json.load(f)
roc_auc = metrics.get("roc_auc", 0.0)
threshold = 0.70
print(f"Test ROC-AUC: {roc_auc:.4f} (threshold: {threshold})")
if roc_auc < threshold:
print(f"ERROR: ROC-AUC {roc_auc:.4f} is below threshold {threshold}")
sys.exit(1)
print("Metrics check PASSED.")
PYEOF
- name: Upload results as artifact
uses: actions/upload-artifact@v4
with:
name: analysis-results
path: results/
retention-days: 30
lint-and-format:
name: Code quality checks
runs-on: ubuntu-latest
steps:
- name: Checkout repository
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: "3.11"
- name: Install pre-commit
run: pip install pre-commit
- name: Run pre-commit hooks
run: pre-commit run --all-files
""").lstrip()
defwrite_github_actions_ci(
output_path: str = ".github/workflows/ci.yml",
) -> None:
"""
Write the GitHub Actions CI workflow YAML file.
Creates the .github/workflows/ directory if it does not exist.
Args:
output_path: Path for the workflow YAML file.
"""
path = Path(output_path)
path.parent.mkdir(parents=True, exist_ok=True)
withopen(path, "w", encoding="utf-8") as fh:
fh.write(GITHUB_ACTIONS_CI)
print(f"Written: {path}")
print("\nAdd DVC secrets to your GitHub repository:")
print(" Settings → Secrets and variables → Actions → New repository secret")
print(" DVC_AWS_ACCESS_KEY_ID")
print(" DVC_AWS_SECRET_ACCESS_KEY")
if __name__ == "__main__":
write_github_actions_ci()
Advanced Usage
Git Tags for Paper Submissions
Use annotated tags to record the exact state of the codebase at key submission
milestones. Annotated tags store the tagger's name, email, date, and message.
# Tag at first submission
git tag -a v1.0.0 -m "Initial submission to Journal of Neuroscience (2026-03-18)"
git push origin v1.0.0
# Tag at revision submission
git tag -a v1.1.0 -m "Revision R1: added sensitivity analysis and Fig. S3"
git push origin v1.1.0
# List all tags
git tag -l --sort=-version:refname
# Check out the submission state (detached HEAD — read only)
git checkout v1.0.0
# Return to dev branch
git checkout dev
DVC Experiment Workflow
# Run pipeline with default params
dvc exp run
# Run experiment with modified parameters
dvc exp run --set-param train.n_estimators=500 --set-param train.max_depth=15
dvc exp run --name exp-deeper-tree --set-param train.max_depth=20
# View all experiments in tabular format
dvc exp show
# Compare two experiments
dvc exp diff exp-deeper-tree
# Promote the best experiment to a named branch
dvc exp branch exp-deeper-tree feature/deeper-tree-model
git checkout feature/deeper-tree-model
Protecting Secrets in GitHub Actions
Never echo or print secrets in CI steps. Use these safe patterns:
import os
from dotenv import load_dotenv
defget_required_secret(key: str) -> str:
"""
Retrieve a required secret from environment variables.
Raises ValueError with a descriptive message if the variable is not set,
instead of silently using an empty string.
Args:
key: Environment variable name.
Returns:
Secret value as string.
Raises:
ValueError: If the variable is not set or is empty.
"""
load_dotenv()
value = os.getenv(key, "")
ifnot value:
raise ValueError(
f"Required secret '{key}' is not set. "f"Add it to your .env file (local) or GitHub Actions secrets (CI)."
)
return value
defget_optional_secret(key: str, default: str = "") -> str:
"""
Retrieve an optional secret, returning default if not set.
Use for non-critical API keys where the feature should be skipped
gracefully if the key is absent.
Args:
key: Environment variable name.
default: Fallback value (default empty string).
Returns:
Secret value or default.
"""
load_dotenv()
return os.getenv(key, default)
# Safe usage pattern:# export OPENAI_API_KEY="<paste-your-key>"try:
openai_key = get_required_secret("OPENAI_API_KEY")
print("OpenAI key loaded successfully.")
except ValueError as exc:
print(f"Skipping AI features: {exc}")
openai_key = ""
Directory Structure for a Reproducible Research Project
my-research-project/
├── .dvc/ # DVC internal files (committed)
├── .github/
│ └── workflows/
│ └── ci.yml # GitHub Actions CI (this Skill)
├── data/
│ ├── raw/ # Original data tracked by DVC (not in Git)
│ └── processed/ # Pipeline outputs tracked by DVC
├── models/ # Trained models tracked by DVC
├── notebooks/ # Jupyter notebooks (outputs stripped by nbstripout)
├── results/ # Figures, metrics JSON (key outputs tracked by DVC)
├── scripts/
│ ├── preprocess.py
│ ├── train.py
│ └── evaluate.py
├── src/
│ ├── data_utils.py
│ ├── model_utils.py
│ └── explainability.py
├── tests/ # Unit tests for analysis functions
├── .env # Secrets — NOT committed (.gitignore-listed)
├── .env.example # Template — committed; no real values
├── .gitignore
├── .pre-commit-config.yaml
├── dvc.yaml # Pipeline definition
├── params.yaml # Analysis parameters
├── requirements.txt # Python dependencies with pinned versions
└── README.md
Troubleshooting
Problem
Likely cause
Fix
dvc repro reruns unchanged stages
params.yaml modification detected
Only change params you intend to; use --downstream to limit scope
pre-commit fails on black
Formatting issues in committed code
Run black . manually; then re-stage and commit
nbstripout does not strip outputs
Hook not installed
Run pre-commit install in the repo root
GitHub Actions dvc pull fails
Missing DVC remote credentials
Add DVC_AWS_ACCESS_KEY_ID and DVC_AWS_SECRET_ACCESS_KEY as repo secrets
git tag push fails
No push access or tag already exists
Check git remote -v; delete conflicting tag with git tag -d <tag>
Example 1 — .gitignore, .env.example, and dotenv Loading
# Run once at project setup to write the standard research .gitignore# and .env.example, then check which secrets are loaded.
write_gitignore(".gitignore")
write_env_example(".env.example")
# Simulate loading from a populated .env file
env_status = load_env_safely(".env")
print("\nSecrets status:")
for k, v in env_status.items():
marker = "[OK]"if v == "SET"else"[MISSING]"print(f" {marker}{k}")
Example 2 — Full dvc.yaml with 3 Stages and params.yaml
# Write the complete pipeline files to the current directory
write_pipeline_files(".")
# After running this, execute in the terminal:# dvc repro -> run all three stages# dvc exp run --set-param train.max_depth=15 -> experiment# dvc exp show -> compare results table# git add dvc.yaml params.yaml .pre-commit-config.yaml# git commit -m "feat: add DVC pipeline and pre-commit hooks"
Example 3 — GitHub Actions CI yaml for Full Reproducibility Check
# Write the CI workflow to .github/workflows/ci.yml
write_github_actions_ci(".github/workflows/ci.yml")
# After writing, commit and push to trigger the workflow:# git add .github/workflows/ci.yml# git commit -m "ci: add reproducibility CI workflow"# git push origin main# Tag the submission state:# git tag -a v1.0.0 -m "Submission to PLOS Computational Biology, 2026-03-18"# git push origin v1.0.0
