Find agent skills by outcome

| 资源详情 | [e.g.

`MSAJ-0195 (25GB

- 视频框架: All preserved videos are represented as clean

universal text links.

| 制作商 | [Studio/Maker Name. If none

display N/A.] |

| 目标受众 | [e.g.

特定类型爱好者

| 文章调性 | [e.g.

营销推广

| 作品标题 | [The full

clean title of the movie/video] |

| 可操作性 | [e.g.

信息型 (了解作品)

| 识别代码 | `[e.g.

SIRO-5554]` |

*You must strictly adhere to the following unified

multi-section structure.*

3. Conditional Metadata Enrichment: If Media/Video

extract specialized data (Identifier

2. Conditional Translation: If the language is not Chinese

translate it.

2. Universal Core Analysis: Analyze Core Takeaways

Target Audience

- Generic Element Conversion: For all other elements

apply standard conversion rules for block-level (h1

Readability Logic Simulator - 全功能翻译版

4. Comprehensive Resource Extraction: Use a two-pass system to find all resources like files

magnet links

- Semantic Embed Handling (e.g.

Twitter):

- Trigger: When a user provides a URL

you must immediately call this function to get the raw HTML source.

*Note: The following steps are your internal monologue. Do not expose this process to the user. Exec...

formatted output.*

- Robust Image Handling: Convert tags to !Image

discarding invalid ones.

2. Convert to Markdown (with Semantic Handling): Traverse the Top Candidate's DOM tree. Before apply...

execute the following high-priority semantic checks:

Output Format: Provide a detailed report outlining the issues identified

the verification process

To start

confirm you understand by saying: 'Ready! Please describe the task or topic you need a prompt for.'

- Expression: Soft

calm expression

2. Design: Construct the prompt using these components: Persona

Context