reproducibility-check
Skill1 starsChanged 3 months agoMITView on GitHub
Verify that a repository actually builds, runs and produces its claimed output from a clean checkout. Use before submitting your own artifact for review, when evaluating someone else's, or when \"works on my machine\" needs disproving.
In this repository45 other files it gives its agents.
- AGENTS.mdAGENTS.md
- claude-handoffSkill
- codebase-designSkill
- code-reviewSkill
- diagnosing-bugsSkill
- domain-modelingSkill
- git-guardrails-claude-codeSkill
- grillingSkill
- grill-meSkill
- grill-with-docsSkill
- handoffSkill
- implementSkill
- improve-codebase-architectureSkill
- loop-meSkill
- migrate-to-shoehornSkill
- prototypeSkill
- researchSkill
- resolving-merge-conflictsSkill
- scaffold-exercisesSkill
- setup-pre-commitSkill
- setup-skillsSkill
- setup-ts-deep-modulesSkill
- tddSkill
- teachSkill
- to-questionnaireSkill
- to-specSkill
- to-ticketsSkill
- triageSkill
- wait-whatSkill
- wayfinderSkill
- wizardSkill
- writing-beatsSkill
- writing-for-agentsSkill
- writing-fragmentsSkill
- writing-shapeSkill
- eslint-rule-authorSkill
- minimal-reproSkill
- rn-list-performanceSkill
- first-contributionSkill
- pr-that-mergesSkill
- survive-reviewSkill
- triage-issueSkill
- contribution-logSkill
- release-checklistSkill
- review-research-softwareSkill
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 17
If you cannot clone it fresh because something is gitignored that shouldn't be, you have already found the bug.
- Distinctive: line 23
Check in a container with nothing preinstalled.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Most reproducibility failures are not exotic. They are an undeclared dependency, a hard-coded path, or a file that was never committed.
Work from a clean checkout, always
Clone into a new directory. Not your working copy — your working copy has untracked files, cached builds and environment variables that the reader will not have.
git clone <url> /tmp/repro-check && cd /tmp/repro-checkIf you cannot clone it fresh because something is gitignored that shouldn't be, you have already found the bug.
The four failure classes
- Undeclared dependency — it works for you because it is installed globally. Check in a container with nothing preinstalled.
- Hard-coded path —
/Users/you/...orC:\Users\...anywhere in the source or config. Grep for the home directory literal. - Uncommitted file — a config, a fixture, a small data file.
git statusin your working copy after a successful run often reveals it. - Unpinned version — "latest" resolved differently today than it did when the results were produced. Check for a lockfile.
Pin the environment
Record the versions that actually worked: language runtime, package manager, OS,
and any system libraries. A Dockerfile or a devcontainer is the strongest form of
this, because it is executable rather than aspirational.
Verify the output, not just the exit code
A script that finishes without error has not necessarily reproduced anything. Check the produced artifact against the claimed result — the number in the paper, the committed reference output, the expected file.
Where the output is numeric and not bit-identical across platforms, state the tolerance rather than pretending to exactness.
Report as a sequence someone can replay
The useful output of this check is a list of commands, in order, from clone to verified result. If you cannot write that list, the artifact is not yet reproducible — regardless of whether it worked for you.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

