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literature-notes

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Aktualisiert16. März 2026 um 19:56

One-paper-per-note structure and grouping by theme; output is structured notes usable for background/related work.

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Xmaster6y

Xmaster6y/chess-experiments

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Verwandte BerufeSOC

Basierend auf der SOC-Berufsklassifikation

Sonstige BiowissenschaftlerNatur- und Sozialwissenschaften·SOC 19-1029

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SKILL.md

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name	literature-notes
description	One-paper-per-note structure and grouping by theme; output is structured notes usable for background/related work.

Literature Notes Skill

When to Use

You have a list of papers (e.g. from literature-search) and need to summarize each in a consistent structure.
You need to group papers by theme (e.g. baselines, methods, datasets) for background or related work.

Instructions

Use references/note-structure.md for the required fields per paper.
Per paper: Capture citation, problem/setting, method/key ideas, datasets/metrics, main results, limitations, and relevance to this project.
Grouping: After notes are written, group papers into themes (e.g. "Baselines", "Prior methods for X", "Datasets") that map to background or related-work subsections.
Output: Structured notes (in docs/ or as specified) plus a short index of themes and which papers belong to each. Do not yet write prose; that is done by background-writing.

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gleiches Repository

experiment-implementation

Xmaster6y/chess-experiments

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

2026-03-171

lczerolens

Xmaster6y/chess-experiments

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.

2026-03-171

tdhook

Xmaster6y/chess-experiments

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.

2026-03-171

background-writing

Xmaster6y/chess-experiments

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

2026-03-161

claims-evidence

Xmaster6y/chess-experiments

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

2026-03-161

experimental-design

Xmaster6y/chess-experiments

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

2026-03-161

name	literature-notes
description	One-paper-per-note structure and grouping by theme; output is structured notes usable for background/related work.

Literature Notes Skill

When to Use

You have a list of papers (e.g. from literature-search) and need to summarize each in a consistent structure.
You need to group papers by theme (e.g. baselines, methods, datasets) for background or related work.

Instructions

Use references/note-structure.md for the required fields per paper.
Per paper: Capture citation, problem/setting, method/key ideas, datasets/metrics, main results, limitations, and relevance to this project.
Grouping: After notes are written, group papers into themes (e.g. "Baselines", "Prior methods for X", "Datasets") that map to background or related-work subsections.
Output: Structured notes (in docs/ or as specified) plus a short index of themes and which papers belong to each. Do not yet write prose; that is done by background-writing.