Setting Up Reproducible Analysis
This is the checklist you wish you'd had before that analysis you couldn't reproduce six months later. It walks through setting up an isolated worktree (with detection to avoid nesting them), pinning your environment with lockfiles, fixing random seeds globally, and making raw data immutable with checksums. The Step 0 worktree detection is especially thoughtful since it checks for existing isolation before creating more. Use it before running any analysis that matters, especially pre-registered work where reproducibility isn't optional. The emphasis on baseline verification (does the existing analysis still reproduce?) before adding new work is the kind of discipline that separates real science from notebook chaos.
- GitHub stars
- 202
- last updated
- Jun 1, 2026
Install
$ npx -y skills add k-dense-ai/science-superpowers --skill setting-up-reproducible-analysis --agent claude-codeInstalls into .claude/skills of the current project.
Files
Setting Up a Reproducible Analysis
Overview
Ensure the analysis happens in an isolated workspace that another person (or future you) can reproduce exactly: same code, same environment, same seed, same immutable input data.
Core principle: A result you cannot reproduce is not a result. Set up reproducibility before you run anything.
Announce at start: "I'm using the setting-up-reproducible-analysis skill to set up an isolated, reproducible workspace."
Step 0: Detect Existing Isolation
Before creating anything, check whether you're already in an isolated workspace.
GIT_DIR=$(cd "$(git rev-parse --git-dir)" 2>/dev/null && pwd -P)
GIT_COMMON=$(cd "$(git rev-parse --git-common-dir)" 2>/dev/null && pwd -P)
BRANCH=$(git branch --show-current)
Submodule guard: GIT_DIR != GIT_COMMON is also true in submodules. Verify you're not in one:
git rev-parse --show-superproject-working-tree 2>/dev/null
If GIT_DIR != GIT_COMMON (and not a submodule): already in a linked worktree. Skip to Step 2 (Environment). Don't create another.
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- Repository
- github.com/k-dense-ai/science-superpowers
- First seen
- Jun 11, 2026
More from k-dense-ai/science-superpowers
All 15- Subagent Driven Analysis
- Surveying Prior Work
- Using Science Superpowers
- Verifying Results Before Claiming
- Writing Science Skills
- Designing The Analysis
- Dispatching Parallel Investigations
- Executing Analysis
- Framing Research Questions
- Investigating Anomalous Results
- Preregistering Analysis
- Receiving Critical Review
- Reporting And Archiving Findings
- Requesting Red Team Review
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