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llm-parsability

Use when auditing content pages for AI discoverability. Applies to any informational page intended to appear in AI-generated answers, search snippets, or knowledge base extraction.

DeepseekModel 官方收录技能 质量 优秀 · 90 v1.0.0

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https://deepseekmodel.com/api/download.php?id=thedaviddias-front-end-checklist-skills-llm-parsability-skill-md&format=skill
下载 .skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
name llm-parsability description Use when auditing content pages for AI discoverability. Applies to any informational page intended to appear in AI-generated answers, search snippets, or knowledge base extraction. metadata {"category":"seo","priority":"medium","difficulty":"intermediate","estimatedTime":"15","source":"frontendchecklist.io","url":"https://frontendchecklist.io/en/rules/seo/llm-parsability"} Make content easy for LLMs to parse AI assistants and answer engines (including Google's AI Overviews) extract and cite content from web pages—pages with clear structure and explicit context are more likely to be accurately cited and surfaced in AI-generated responses. Quick Reference Use semantic HTML headings, paragraphs, and lists — LLMs prefer structured markup Avoid content locked behind JavaScript rendering or requiring user interaction Write clear, self-contained sections that make sense out of full-page context Structured data (JSON-LD) provides machine-readable context alongside human-readable text Check Evaluate whether the page content is parseable by an LLM. Check: (1) Is content in semantic HTML tags ( – , , , , )? (2) Is key content accessible without JavaScript? (3) Are section headings descriptive enough to stand alone? (4) Does the page have JSON-LD structured data? (5) Are there FAQ sections or explicit Q&A patterns that match common search queries? Fix Restructure content into explicit HTML sections with descriptive headings. Replace JavaScript-rendered content with server-side rendered HTML. Add JSON-LD schema (Article, FAQPage, HowTo) to annotate the content type. Write headings and lead sentences that work as standalone answers—assume the reader only sees one paragraph. Explain Large language models and answer engines process web content by extracting text from HTML. Pages that use semantic markup, clear headings, and server-rendered content are parsed more accurately than JavaScript-heavy or visually-structured pages. As AI-generated answers increasingly cite specific web sources, well-structured content is more likely to be accurately quoted and linked. Code Review Check the page's rendered HTML for: (1) proper heading hierarchy (h1→h2→h3), (2) content wrapped in semantic elements ( , , ), (3) key content visible in initial HTML response (not injected by JS), (4) presence of FAQPage, HowTo, or Article JSON-LD schema, (5) absence of content hidden behind modals, tabs, or accordions that require JS interaction. For full implementation details, code examples, and framework-specific guidance, see references/rule.md . Rule page: https://frontendchecklist.io/en/rules/seo/llm-parsability
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下载的 .skill 包内含以下字段。
字段 说明
format格式标识(skill/v1)
skill_id技能唯一 ID
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
source_url来源链接(本页地址)
exported_at导出时间(每次下载生成)
system_prompt系统提示词正文
model_config模型参数:provider / model / temperature / max_tokens / top_p
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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