aviskaar/code-replication
skills/code-replication/SKILL.md
Use this skill when tasked with replicating results from a research paper. Guides systematic reproduction of experiments, documentation of discrepancies, and validation of claims.
$ install --global
skillsauthnpx skillsauth add aviskaar/open-org code-replicationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- code-replication
- description:
- Use this skill when tasked with replicating results from a research paper. Guides systematic reproduction of experiments, documentation of discrepancies, and validation of claims.
- license:
- Apache-2.0
- author:
- aviskaar
- version:
- 1.0
- tags:
- replication, reproducibility, experiments, code, validation
Code Replication
Reproduce results from a research paper with methodical documentation.
Replication Protocol
Phase 1 — Paper Audit
Before writing any code:
- Extract every hyperparameter mentioned in the paper and appendix.
- Note any details that are ambiguous or missing.
- Check for an official codebase (GitHub, project page, supplementary materials).
- Identify the exact dataset split, preprocessing steps, and evaluation protocol.
Document findings in a replication-notes.md file.
Phase 2 — Environment Setup
- Record exact package versions (use
pip freezeorconda env export). - Prefer the same framework the paper used when possible.
- Use a fixed random seed for all experiments. Document it.
- Isolate the environment (virtualenv, conda, or container).
Phase 3 — Minimal Reproduction
Start with the smallest possible experiment:
- Use the smallest dataset subset that exercises the full pipeline.
- Confirm the training loop runs without errors before full-scale training.
- Check intermediate outputs (loss curves, gradient norms) against any figures in the paper.
Phase 4 — Full Replication
- Run the main experiment with the paper's reported settings.
- Record all results in a structured log (seed, config, metric, timestamp).
- Run at least 3 seeds; report mean ± std.
Phase 5 — Gap Analysis
Compare your results against the paper:
| Metric | Paper | Reproduced | Δ | |--------|-------|------------|---| | ... | ... | ... | ...|
Categorize any gap:
- Reproduced: within noise (< 0.5% relative difference)
- Close: explainable by known ambiguities
- Discrepancy: notable gap — document likely causes
- Failed: results do not match — flag for investigation
Phase 6 — Documentation
Produce a REPLICATION.md with:
- Paper citation
- Environment details
- Deviations from the paper (with justification)
- Results table
- Conclusion: successfully reproduced / partially reproduced / failed
Principles
- Never alter evaluation code to match paper numbers — fix the model, not the metric.
- Document every ambiguity you encountered and how you resolved it.
- A failed replication is a valid and valuable research outcome.
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