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Same-epoch comparison of training runs across wandb, neptune, tensorboard, or mlflow. Aligns runs at the student's current step…
End-to-end paper reproduction from arxiv URL through smoke runs to replication experiments. Handles missing or partial official code, missing training scripts, missing hyperparameters, and private datasets via similar-public-dataset substitution. Use when the user asks to
$ npx -y skills add fcakyon/phd-skills --skill reproduce --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/reproduceContext preview
The summary Claude sees to decide when to auto-load this skill.
End-to-end paper reproduction from arxiv URL through smoke runs to replication experiments. Handles missing or partial official code, missing training scripts, missing hyperparameters, and private datasets via similar-public-dataset substitution. Use when the user asks to
name: reproduce description: End-to-end paper reproduction from arxiv URL through smoke runs to replication experiments. Handles missing or partial official code, missing training scripts, missing hyperparameters, and private datasets via similar-public-dataset substitution. Use when the user asks to reproduce, implement, replicate, or re-run a paper from scratch, or pastes an arxiv URL with reproduction intent.
Reproducing an ML paper often means filling gaps the authors didn't ship, training scripts, hyperparameter tables, augmentation specifics, exact dataset splits. This skill walks seven stages from "I have an arxiv link" to "I have a replication run with measurable delta vs the paper's number."
Each stage has a separate reference file under `references/` so this overview stays scannable.
The user just said any of:
| Stage | What | Reference | | ----- | -------------------------------------------------------------- | -------------------------------------------------------------- | | 1 | Paper acquisition (CLI + PDF → structured extract) | [references/01-paper-fetch.md](references/01-paper-fetch.md) | | 2 | Existing code discovery + inventory | [references/02-code-clone.md](references/02-code-clone.md) | | 3 | Gap analysis (extract every missing hyperparam from the prose) | [references/03-gap-analysis.md](references/03-gap-analysis.md) | | 4 | Implementation (uv venv, fill gaps, commit per gap) | [references/04-implement.md](references/04-implement.md) | | 5 | Dataset acquisition (HF datasets first; substitute if private) | [references/05-dataset.md](references/05-dataset.md) | | 6 | Smoke runs (forward pass → 1 step → 20 iters) | [references/06-smoke.md](references/06-smoke.md) | | 7 | Replication runs + comparison at paper's reported epochs | [references/07-replicate.md](references/07-replicate.md) |
Walk them in order. Each stage has its own success criteria; do not advance to the next until the current one passes.
For each paper reproduction, set up a dedicated workspace:
repro/<paper-arxiv-id>/ ├── paper.md # structured extract from stage 1 ├── inventory.md # what exists / missing from stage 2 ├── gaps_filled.md # hyperparam table with provenance from stage 3 ├── code/ # implementation from stage 4 (or cloned + extended) ├── data/ # dataset symlinks or actual data from stage 5 ├── dataset_substitution.md # if a public dataset stood in for a private one ├── smoke_logs/ # outputs from stage 6 └── results.md # replication outcomes from stage 7
This keeps reproductions self-contained and easy to revisit later.
For each reproduction, the final artifact is `results.md` with absolute deltas (not just %) and one of three labels per metric:
If the workspace is on a public repo, link the workspace README from the project's main reproduction-tracking doc.
Catch AI mistakes before they cost weeks of compute. Reproduce papers from arxiv. Debug runs evidence-first. Compare experiments at the right epoch. Launch with discipline.
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