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X Collect

Community
kangarooking
x-collect

Collect and research materials for X (Twitter) content creation using multi-round web search strategy. Use when user wants to gather trending topics, research subjects for X posts, or mentions "collect materials", "research topic", "find content for X", "x-collect". Performs 4-round deep research mimicking human research workflow.

Overview

Publisherkangarooking
Repositoryx-skills
Skill namex-collect
Stars
338
Forks
55
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by kangarooking on GitHub. Read the source before you install it.

Installation

Install the X Collect AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/kangarooking/x-skills/tree/main/x-collect
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/kangarooking/x-skills.git /tmp/x-skills
mkdir -p .claude/skills
cp -r /tmp/x-skills/x-collect .claude/skills/x-collect
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable X Collect in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads X Collect on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so X Collect is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

X Collect

Collect trending topics and research materials for X content creation using a systematic 4-round web search strategy.

Prerequisites

  • WebSearch tool available
  • Internet connection

Optional State (ContextStore)

If available, leverage user preference state to improve search quality:

  • State dir: ~/.claude/skills/x-create/state/
  • Positive samples: liked_topics.json (preferred topics/entities/sources)
  • Negative samples: rejected_topics.json (avoid low-value/repetitive topics)

If state files don't exist, proceed normally.

Workflow

Input

User provides:

  • Topic (required): The subject to research (e.g., "AI Agent最新进展", "Claude 4 发布")
  • Language (optional): Output language, defaults to Chinese (zh-CN)

4-Round Search Strategy

Simulate human research thinking process with progressive depth.

Query expansion rules (lightweight):

  • Start with user topic
  • Add 2-5 query variants based on:
    • User profile (domains/persona if known)
    • liked_topics.json high-frequency keywords/entities (if available)
  • Keep ~20% queries as exploration (cross-domain / adjacent keywords) to avoid information bubble

Dedup & clustering (at the end of each round):

  • Merge near-duplicate URLs/titles
  • Group materials into topic clusters (rough tags are fine)

Round 1: Official Sources (权威信息)

Search: "{topic} 官方文档"
Search: "{topic} GitHub"
Search: "{topic} official announcement"

Goal: Get authoritative first-hand information

Round 2: Technical Analysis (技术解析)

Search: "{topic} 详细介绍"
Search: "{topic} 教程 tutorial"
Search: "{topic} how it works"

Goal: Understand technical details and mechanisms

Round 3: Comparison & Reviews (对比评测)

Search: "{topic} vs {competitor}"
Search: "{topic} 评测 review"
Search: "{topic} pros cons"

Goal: Get different perspectives and comparisons

Round 4: Supplementary Verification (补充验证)

# Analyze gaps from previous rounds
missing_info = analyze_gaps(previous_results)
Search: "{missing_info}"
Search: "{topic} 最新 latest 2024 2025"

Goal: Fill information gaps and get latest updates

Output Format

Generate structured material document:

markdown
# {Topic} 素材收集报告

## 收集时间
{timestamp}

## 核心信息
- **官方定义**: ...
- **关键特性**: ...
- **最新动态**: ...

## 素材列表

### 素材 1
- **标题**: ...
- **来源**: {url}
- **摘要**: 2-3句话概括
- **关键点**:
  - 要点1
  - 要点2
- **潜在选题角度**: ...
- **推荐推文类型**: [高价值干货/犀利观点/热点评论/故事洞察/技术解析]
- **QualityScore (0-10)**: ...
- **TopicCluster**: ...
- **HookCandidates**:
  - ...

### 素材 2
...

## 热度分析
- **当前热度**: 高/中/低
- **趋势**: 上升/稳定/下降
- **讨论焦点**: ...

## 争议点
- 争议1: ...
- 争议2: ...

## 下一步建议
使用 `/x-filter` 对素材进行打分筛选

Additionally, append a machine-readable block for hooks/state ingestion:

json
MATERIALS_JSON
{
  "schema_version": "x_skills.materials.v1",
  "topic": "{Topic}",
  "timestamp": "{timestamp}",
  "items": [
    {
      "title": "...",
      "url": "...",
      "summary": "...",
      "key_points": ["..."],
      "quality_score_0_10": 0,
      "topic_cluster": "...",
      "hook_candidates": ["..."]
    }
  ]
}

Execution Steps

  1. Receive topic from user
  2. Execute Round 1 searches (official sources)
  3. Execute Round 2 searches (technical analysis)
  4. Execute Round 3 searches (comparisons)
  5. Analyze gaps from rounds 1-3
  6. Execute Round 4 searches (fill gaps)
  7. Synthesize results into structured format
  8. Output MATERIALS_JSON (for hooks/state ingestion)
  9. (Optional) Append event for feedback loop:
    • python ~/.claude/skills/x-create/scripts/x_state.py event --event collect.completed --payload-json '{"topic":"...","items_count":12,"explore_ratio":0.2}'
  10. Report summary to user

Example

User: /x-collect Claude MCP协议

Expected behavior:

  1. Search "Claude MCP协议 官方文档"
  2. Search "MCP Model Context Protocol GitHub"
  3. Search "MCP协议 详细介绍"
  4. Search "MCP协议 教程"
  5. Search "MCP vs function calling"
  6. Search "MCP协议 评测"
  7. Identify gaps: need more about security, adoption rate
  8. Search "MCP协议 安全性"
  9. Search "MCP协议 最新 2025"
  10. Generate structured material report

Integration

After collection, suggest:

素材收集完成!共找到 X 条相关素材。

下一步:运行 /x-filter 对素材进行打分筛选,≥7分的选题将进入创作池。

Tips

  • For trending topics, prioritize recency (2024-2025)
  • For technical topics, prioritize official docs and GitHub
  • For controversial topics, collect multiple perspectives
  • Always note the source URL for credibility

Frequently asked questions

What does the X Collect AI skill do?

Collect and research materials for X (Twitter) content creation using multi-round web search strategy. Use when user wants to gather trending topics, research subjects for X posts, or mentions "collect materials", "research topic", "find content for X", "x-collect". Performs 4-round deep research mimicking human research workflow.

Why use X Collect on TypingMind?

Because you install it once and use it with any model. X Collect is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install X Collect in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kangarooking/x-skills/tree/main/x-collect. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use X Collect?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with X Collect?

As many as you like. As long as a model supports skills, you can use X Collect with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the X Collect AI skill free?

Yes. It is published on GitHub by kangarooking under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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