Skill Discovery SEO
Overview
Improve a skill's discoverability for both humans and AI agents. Focus on triggerable names, dense metadata, realistic example prompts, clear scope, and packaging decisions that help registries and meta-skills choose the right skill reliably.
Load references/discoverability-guide.md before rewriting metadata or publish copy.
Workflow
1. Identify the discovery surface
Check which fields the registry or agent ecosystem will likely index:
- - skill INLINECODE0
- frontmatter INLINECODE1
- INLINECODE2
- short description
- default prompt
- tags or categories
- example prompts
- listing copy or repository summary
If the platform is unknown, assume most systems use a mix of exact text match, semantic retrieval, and usage or trust signals.
2. Tighten the trigger language
Rewrite the discovery fields so they answer four questions fast:
- - what domain is this for
- what job does it do
- what inputs does it accept
- what output or decision does it produce
Prefer literal wording over branding, slogans, or clever titles.
3. Expand query coverage without bloating
Include the main phrases a user or agent would search for:
- - primary nouns such as platform, artifact, or file type
- major verbs such as review, check, create, revise, deploy, or analyze
- important synonyms such as policy and compliance, or caption and script
- edge-case phrases only if the skill truly handles them
Do not keyword-stuff. Add only terms that improve routing accuracy.
4. Check trust and scope
Make sure the skill:
- - has a narrow, believable scope
- states boundaries clearly
- promises outputs it can actually deliver
- avoids agent-specific assumptions unless truly required
If the skill claims too much, narrow it until another agent could select it confidently.
5. Produce a publish-ready pack
Return:
- - recommended skill name
- frontmatter description
- INLINECODE3
- short description
- default prompt
- optional tags
- 3 to 8 realistic example prompts
- one short note on what not to claim in the listing
Output Rules
- - Keep names literal, ASCII-safe, and easy to type.
- Put the highest-signal trigger terms early in the description.
- Match the wording users would naturally use, not internal jargon.
- Keep agent-specific wording out of general-purpose skills unless the skill is actually tied to one ecosystem.
- Prefer one strong promise over many weak ones.
技能发现与SEO优化
概述
提升技能在人类和AI代理中的可发现性。重点关注可触发的名称、密集的元数据、真实的示例提示、清晰的范围界定,以及有助于注册中心和元技能可靠选择正确技能的打包决策。
在重写元数据或发布文案前,请先加载参考资料/可发现性指南.md。
工作流程
1. 识别发现面
检查注册中心或代理生态系统可能索引的字段:
- - 技能名称
- 前置元数据描述
- 显示名称
- 简短描述
- 默认提示
- 标签或分类
- 示例提示
- 列表文案或仓库摘要
若平台未知,假设大多数系统采用精确文本匹配、语义检索以及使用或信任信号的混合方式。
2. 收紧触发语言
重写发现字段,使其快速回答四个问题:
- - 适用于哪个领域
- 执行什么任务
- 接受什么输入
- 产生什么输出或决策
优先使用字面表述,避免品牌化、口号式或花哨标题。
3. 扩展查询覆盖范围而不冗余
包含用户或代理可能搜索的主要短语:
- - 主要名词,如平台、工件或文件类型
- 主要动词,如审查、检查、创建、修订、部署或分析
- 重要同义词,如策略与合规、字幕与脚本
- 仅当技能确实处理时才包含边缘案例短语
不要堆砌关键词。仅添加能提高路由准确性的术语。
4. 检查可信度与范围
确保技能:
- - 具有狭窄且可信的范围
- 清晰说明边界
- 承诺能够实际交付的输出
- 除非确实需要,否则避免代理特定假设
如果技能声称过多,请缩小范围,直到其他代理能够自信地选择它。
5. 生成可发布包
返回:
- - 推荐的技能名称
- 前置元数据描述
- 显示名称
- 简短描述
- 默认提示
- 可选标签
- 3至8个真实的示例提示
- 一条关于列表文案中不应声称内容的简短说明
输出规则
- - 保持名称字面化、ASCII安全且易于输入。
- 将最高信号触发词放在描述开头。
- 匹配用户自然使用的措辞,而非内部术语。
- 除非技能确实与某个生态系统绑定,否则通用技能中避免使用代理特定措辞。
- 优先使用一个强有力的承诺,而非多个弱承诺。