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reproducibility-check

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Mis à jour16 mars 2026 à 19:56

Apply the reproducibility checklist to assess whether experiments can be re-run reliably.

Installation

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Source

Xmaster6y

Xmaster6y/chess-experiments

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Métiers associésSOC

Basé sur la classification professionnelle SOC

Biochimistes et biophysiciensSciences de la vie, physiques et sociales·SOC 19-1021

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2 fichiers

SKILL.md

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name	reproducibility-check
description	Apply the reproducibility checklist to assess whether experiments can be re-run reliably.

Reproducibility Check Skill

When to Use

You are reviewing the project for reproducibility (e.g. review-paper command with experiments in scope).
The user wants to know if main results can be reproduced from the repo.

Instructions

Use references/checklist.md for the checklist items.
For each main result or experiment claimed in the paper, run through the checklist: entry point, configs, data, metrics, logging, randomness.
For each item, record: satisfied (with pointer, e.g. "configs/run_experiment.yaml", "scripts/run_experiment.py") or gap (what is missing).
Output: A short reproducibility report per result (or one table): checklist item → status + pointer or gap. Suggest concrete fixes for gaps (e.g. "add seed to config X").

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How to change code, tests, configs, and launch in this repo; output is concrete edits and run commands.

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Provides guidance for interpretability of Leela Chess Zero (lc0) neural networks. Use when loading lc0 models, encoding chess boards, running inference, visualizing heatmaps, probing concepts, evaluating on puzzles, or pairing with tdhook, captum, zennit, or nnsight for XAI.

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Provides guidance for interpreting and manipulating neural network internals using tdhook with TensorDict and PyTorch hooks. Use when needing attribution maps, activation analysis, probing, steering, activation patching, or weight-level interventions on PyTorch or TensorDict models.

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background-writing

Xmaster6y/chess-experiments

Turn grouped literature notes into background or related-work text; no claims beyond what the notes support.

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claims-evidence

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Map claims in the paper to results, configs, or code; avoid unsupported claims.

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experimental-design

Xmaster6y/chess-experiments

Patterns and checklists for designing and refining experiments in this research template.

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name	reproducibility-check
description	Apply the reproducibility checklist to assess whether experiments can be re-run reliably.

Reproducibility Check Skill

When to Use

You are reviewing the project for reproducibility (e.g. review-paper command with experiments in scope).
The user wants to know if main results can be reproduced from the repo.

Instructions

Use references/checklist.md for the checklist items.
For each main result or experiment claimed in the paper, run through the checklist: entry point, configs, data, metrics, logging, randomness.
For each item, record: satisfied (with pointer, e.g. "configs/run_experiment.yaml", "scripts/run_experiment.py") or gap (what is missing).
Output: A short reproducibility report per result (or one table): checklist item → status + pointer or gap. Suggest concrete fixes for gaps (e.g. "add seed to config X").