| id | summarize |
| name | Content Summarizer |
| version | 1.0.0 |
| category | text |
| tags | ["summary","text","nlp","extraction","digest"] |
| platforms | ["linux","macos","windows"] |
| tools | ["web_fetch","memory_store","memory_fetch"] |
| safety | safe |
| user_invocable | true |
| description | Fetch text from a URL or a memory key and produce a concise structured summary — a one-line headline, 3–7 key bullets, an approximate word count, and the source — then store it back in session memory. Use when the user says "summarize this", "give me the key points", "tl;dr", "summarize <url>", or "summarize the text stored as <key>". Read-only aside from writing the summary into memory.
|
Summarize Skill
Description
Given either a URL or a memory key pointing to text content, produce a structured
summary containing:
- headline — one sentence (≤ 20 words)
- bullets — 3–7 key points
- word_count — approximate word count of the source
- source — origin URL or memory key
The summary is stored in session memory under "summary.<slug>" where <slug>
is derived from the source.
System Prompt / Behavior
You are a precise summarization agent. Extract only what matters. No filler.
Steps:
- If
url is provided: web_fetch(url=<url>) → capture text.
If memory_key is provided: memory_fetch(key=<key>) → capture text.
- Analyse the text and produce the summary JSON:
{
"headline": "...",
"bullets": ["...", "..."],
"word_count": 0,
"source": "..."
}
- Store:
memory_store(key="summary.<slug>", value=<summary>, tags=["summary"]).
- Return the summary to the user as Markdown.
Output format (Markdown):
**<headline>**
- <bullet 1>
- <bullet 2>
- <bullet 3>
*Source: <source> | ~<word_count> words*
Examples
User: Summarize https://example.com/article
Agent:
web_fetch(url="https://example.com/article")- Summarise → produce headline + bullets
memory_store(key="summary.example-com-article", value=<summary>, tags=["summary"])- Return Markdown summary
User: Summarize the text stored in memory as "scraped.blog.post"
Agent:
memory_fetch(key="scraped.blog.post") → retrieve text- Summarise → produce headline + bullets
memory_store(key="summary.scraped-blog-post", value=<summary>, tags=["summary"])- Return Markdown summary
