second-pass-grasp

Full-text deep-read of a paper's Introduction, Method, and Results sections to draft the structured bundle (problem, method, key results with hedge levels, limitations), each field carrying a precise source anchor. Use this after first-pass-skim has classified the paper and drafted candidate angles — this is the main content-extraction pass of the reading pipeline.

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Repositorio: yogsoth-ai/paper-reading
Última actividad en el origen: 6 de agosto de 2026 a las 14:34
Idioma detectado de SKILL.md: inglés
Estrellas: 1
Forks: 0

Opciones de instalación

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npx skills add https://github.com/yogsoth-ai/paper-reading --skill second-pass-grasp

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angle-selection

Pick which candidate angle to write the article around, cross-checked against the verified bundle so the chosen angle is actually substantiated by the paper (not just appealing at the skim stage). Use this as the first step of drafting, after quality-assurance has passed, whenever you have candidate_angles from first-pass-skim and a verified bundle.

2026-08-06

audience-first-writing

Writing strategy that turns a verified paper bundle into a finished public-audience article — selecting an angle, applying a pre-set style constraint during drafting (never a post-hoc rewrite), and checking the result for drift against the bundle. Use this after quality-assurance has passed on the bundle — this is the final strategy of the paper-reading pipeline, producing the WeChat article itself.

2026-08-06

deep-read

Deep-reading strategy that turns a single specified academic paper into a structured, source-anchored bundle (problem, method, key results, limitations) ready for fact-checking and article drafting. Use this whenever the user names one specific paper (by arXiv ID, URL, or title) and wants it read, summarized, explained, or turned into content — this is the entry point of the paper-reading pipeline.

2026-08-06

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name	second-pass-grasp
description	Full-text deep-read of a paper's Introduction, Method, and Results sections to draft the structured bundle (problem, method, key results with hedge levels, limitations), each field carrying a precise source anchor. Use this after first-pass-skim has classified the paper and drafted candidate angles — this is the main content-extraction pass of the reading pipeline.
execution	subagent
prompt	./prompt.md
input	paper_ref (string), paper_type (string), skim_notes (string)
output	draft_bundle (dict), uncertain_fields (list of strings)
dependencies	{"sops":["spawn-agent","literature-research"]}