code-reproduction
Skill3 starsChanged 8 months agoMITView on GitHub
Use when reproducing a paper, repo, benchmark, or reported result and you need an explicit plan for environment setup, execution, discrepancy logging, and final reproduction status.
In this repository43 other files it gives its agents.
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- daskSkill
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- exploratory-data-analysisSkill
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- matplotlibSkill
- modalSkill
- networkxSkill
- paper-to-codeSkill
- peer-reviewSkill
- plotlySkill
- polarsSkill
- pytorch-lightningSkill
- pyzoteroSkill
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- research-planningSkill
- reviewer-responseSkill
- scholar-evaluationSkill
- scientific-critical-thinkingSkill
- scientific-schematicsSkill
- scientific-slidesSkill
- scientific-visualizationSkill
- scientific-writingSkill
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- shapSkill
- stable-baselines3Skill
- statistical-analysisSkill
- statsmodelsSkill
- sympySkill
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The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 4 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈4
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 35
Run the minimum viable path first, then expand to full results.
- Distinctive: line 42
never claim success without a concrete comparison target
- Distinctive: line 43
treat undocumented setup steps as findings, not personal fixes
- Distinctive: line 44
keep a clear boundary between upstream behavior and your modifications
≈ “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
Purpose
Reproduce reported results without overstating success and without losing track of discrepancies.
When To Use
- implementing or rerunning a paper's released code
- checking whether a baseline can be reproduced before extending it
- validating a public repo before building new work on top of it
- preparing an artifact report or reproducibility note
Inputs
- target paper, repo, or benchmark claim
- environment requirements and hardware constraints
- available scripts, checkpoints, configs, and datasets
Outputs
- reproduction plan and execution order
- environment summary and setup log
- discrepancy report between expected and observed behavior
- reproduction status: exact, partial, failed, or blocked
- list of missing assumptions or undocumented steps
Workflow
- Extract the exact claims to reproduce before running anything.
- Reconstruct the environment and document every assumption.
- Run the minimum viable path first, then expand to full results.
- Compare observed outputs against reported numbers or behaviors.
- Log mismatches with likely causes instead of hiding them.
Constraints
- distinguish exact replication from faithful reimplementation
- never claim success without a concrete comparison target
- treat undocumented setup steps as findings, not personal fixes
- keep a clear boundary between upstream behavior and your modifications
Does it work?
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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.

