# tweet-processor

> Use this tool when you need to extract valuable information from tweets and organize it into structured notes. It solves the problem of manually sifting through tweet links to gather insights, and is useful for social media monitoring, market research, and trend analysis. The tool takes tweet links as input and outputs categorized and structured notes, making it easy to quickly identify key takeaways and patterns.

Canonical page: https://skillsregistry.net/skills/caqlayan-tweet-processor  
JSON: https://api.skillsregistry.net/v1/skills/caqlayan-tweet-processor

## Description

Extract and categorize insights from tweet links into structured notes.

## Trust

- **Trust score (0–1):** 1.00
- **Verification tier:** scanned
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** instructions
- **Runtime environment:** llm
- **Category:** other
- **Updated:** 2026-09-19

## Source

- **Source listing:** [ClawHub](https://clawskills.sh/skills/caqlayan-tweet-processor)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "caqlayan-tweet-processor"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/caqlayan-tweet-processor` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/caqlayan-tweet-processor/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt