field_literature_survey

Analyzes conference and journal papers to extract field-specific writing style, vocabulary, and structure conventions.

Updated Aug 5, 2026
One-click install
npx skills add https://github.com/sayonari/claude-skills --skill field-literature-survey-sayonari
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: field_literature_survey
Source: https://github.com/sayonari/claude-skills/tree/main/field_literature_survey
Command: npx skills add https://github.com/sayonari/claude-skills --skill field-literature-survey-sayonari

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, beautifulsoup4.

What problem does it solve? Researchers writing papers for a new venue often produce manuscripts that read like they were written by someone from another field, because each community (Interspeech, ICASSP, IEEE/ACM TASLP, Speech Communication, ACL, etc.) has its own conventions for style, vocabulary, section structure, and figures. This Skill systematizes the pre-writing literature survey so those conventions are captured before drafting begins. ## Core Features & Use Cases - Structured survey workflow: Defines scope with the user (target venue, year range, keywords, 20-100 papers), builds a literature list from ISCA Archive, IEEE Xplore, ACL Anthology, ScienceDirect, DBLP, and Google Scholar, then downloads PDFs or fetches abstracts/intros into a local corpus. - 14-point observation checklist: Examines titles, abstracts, introductions, related work, methods, experiments, results, discussion, conclusions, references, figures/tables, terminology, prose style, and implicit field norms, producing structured notes (field_style_notes.md, vocabulary.md, phrasing.md, section_patterns.md, figure_table_patterns.md). - Cross-venue comparison and reuse: Records per-conference differences and maintains a reusable corpus plus a large-scale pipeline (52,000 papers, 76M words) with scripts for downloading, text extraction, n-gram statistics, trend reports, and AI-vs-human style comparison. - Use Case: Before writing a Speech Communication submission on turn-taking, run the survey over 50 recent Interspeech/ICASSP/TASLP papers, then consult the generated style notes during each writing phase of the paper_writing workflow. ## Quick Start Survey the last five years of Interspeech and ICASSP papers on end-of-turn prediction and compile field style notes, vocabulary, and phrasing patterns for my upcoming Speech Communication submission.

Frequently Asked Questions about field_literature_survey

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I learn the writing style of a conference before submitting a paper?

Survey 20-100 recent papers from the target venue and record conventions for titles, abstracts, introductions, methods, results, figures, and terminology in structured notes. This Skill defines that checklist and organizes the findings into reusable files like field_style_notes.md and vocabulary.md.

How many papers should I review for a field literature survey?

The recommended scope is 50-100 papers over the past five years, or 20-30 papers for a shortened version. When deadlines are tight, start with 10-20 papers reading only abstracts and introductions, then expand the corpus over time.

Which sources can I use to collect papers for a style survey?

The workflow uses ISCA Archive for Interspeech, IEEE Xplore for ICASSP and TASLP, ACL Anthology for NLP venues, ScienceDirect for Speech Communication, plus DBLP and Google Scholar for cross-searching and citation-based prioritization.

Can I reuse survey notes when writing multiple papers in the same field?

Yes, the notes are designed as living documents stored in the project folder and reused across papers. A prebuilt large-scale corpus of about 52,000 ICASSP and Interspeech papers with analysis scripts can also be regenerated roughly once a year.

What are the limitations of automated writing-style extraction from papers?

Automated extraction can miss what experienced reviewers perceive, so findings should be validated against human judgment from advisors or reviewers. Focusing only on highly cited papers also biases results toward established patterns, so recent papers from the last one or two years should be included.