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annu-rev-food

Author-guideline skill for Annual Review of Food Science and Technology (Annual Reviews, ISSN 1941-1413). Use to format or check an invited review for this journal — review structure, author–date Annual Reviews references, and figure specs. Triggers: submit to Annual Review of Food Science and Technology, Annual Reviews guidelines, invited food review formatting. Also fires when the user wants to publish on/in this journal, format a manuscript for it, or match its reference/citation style.

DeepseekModel 官方收录技能 质量 良好 · 64 v1.0.0

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https://deepseekmodel.com/api/download.php?id=pangenomeai-academic-skills-food-nutrition-journals-annu-rev-food-skill-md&format=skill
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.skill 文件中 system_prompt 字段的实际内容。
name annu-rev-food description Author-guideline skill for Annual Review of Food Science and Technology (Annual Reviews, ISSN 1941-1413). Use to format or check an invited review for this journal — review structure, author–date Annual Reviews references, and figure specs. Triggers: submit to Annual Review of Food Science and Technology, Annual Reviews guidelines, invited food review formatting. Also fires when the user wants to publish on/in this journal, format a manuscript for it, or match its reference/citation style. metadata {"publisher":"Annual Reviews","issn":"1941-1413","verified":"2026-07","source":"https://www.annualreviews.org/journal/food"} Annual Review of Food Science and Technology — Author Guideline Skill Publisher: Annual Reviews · ISSN: 1941-1413 · Source: Journal page · Verified: 2026-07 (confirm at source). Aims & scope Authoritative, invited critical reviews across food science and technology: food chemistry, microbiology, engineering, safety, nutrition, and emerging technologies. Articles are commissioned by the Editorial Committee — unsolicited submissions are generally not accepted; propose topics to the editors first. Article type Review articles only (comprehensive, synthesis-driven, forward-looking). Typical length is substantial (often ~8,000–10,000 words); confirm the assigned length with the production editor. Manuscript structure Title → Authors & affiliations → Abstract → Keywords → Introduction → thematic sections with descriptive headings → Summary Points / Future Issues (Annual Reviews signature elements) → Disclosure Statement → Acknowledgments → Literature Cited. Provide "Summary Points" and "Future Issues" bullet lists. Abstract: ~150–250 words. Keywords: ~6–8. Disclosure statement: required (funding, affiliations, potential bias). Reference style Author–date (name–year), Annual Reviews style. In-text (Author & Author 2023) ; "Literature Cited" list alphabetical. Article titles included. Example: Author AB, Author CD. 2023. Title of the review article. Annu. Rev. Food Sci. Technol. 14:123–45 Figures & tables Figures ≥300 dpi (line art higher); Annual Reviews uses professional redraw — supply clear, editable source files and permissions for any reused figures; RGB/CMYK per production; editable tables; cite in order. Submission checklist Topic pre-agreed/invited by the Editorial Committee · [ ] Summary Points + Future Issues lists Abstract ~150–250 words · [ ] 6–8 keywords · [ ] Disclosure statement Author–date (Annual Reviews) Literature Cited · [ ] Permissions for reused figures Figures ≥300 dpi, editable source provided Also covers — Annual Review of Nutrition Same Annual Reviews format (invited only; author–date; Summary Points / Future Issues). Annual Review of Nutrition (ANNU REV NUTR) — invited comprehensive nutrition reviews. Formatting constraints journal: Annual Review of Food Science and Technology; Annual Review of Nutrition publisher: Annual Reviews submission: invited-only reference_style: author-date-annual-reviews abstract_words: 250 keywords_max: 8 required_elements: [ summary_points , future_issues , disclosure_statement ] figure_dpi: { halftone: 300 , line_art: 1000 }
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字段 说明
format格式标识(skill/v1)
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name技能名称
version版本号
description技能描述
category所属分类(数组)
trigger_words触发词列表
tags标签列表
source来源标识
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exported_at导出时间(每次下载生成)
system_prompt系统提示词正文
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examples示例
install_guide各平台导入说明(Coze / Dify / Claude / 自定义框架)
同一份技能可按不同平台格式导出。
.skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用 下载
.skillpro 增强格式,额外含脚本 / 工具 / 依赖 / 钩子占位 下载
.json 纯 JSON 导出,只含 system_prompt 与模型参数 下载
Coze 带 frontmatter 的 Markdown,Coze 平台导入用 下载
Dify Dify DSL,创建应用后直接导入 下载

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