
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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/kangarooking/x-skills --skill x-collect- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads x-collect automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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:
# {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:
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
- Receive topic from user
- Execute Round 1 searches (official sources)
- Execute Round 2 searches (technical analysis)
- Execute Round 3 searches (comparisons)
- Analyze gaps from rounds 1-3
- Execute Round 4 searches (fill gaps)
- Synthesize results into structured format
- Output MATERIALS_JSON (for hooks/state ingestion)
- (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}'
- Report summary to user
Example
User: /x-collect Claude MCP协议
Expected behavior:
- Search "Claude MCP协议 官方文档"
- Search "MCP Model Context Protocol GitHub"
- Search "MCP协议 详细介绍"
- Search "MCP协议 教程"
- Search "MCP vs function calling"
- Search "MCP协议 评测"
- Identify gaps: need more about security, adoption rate
- Search "MCP协议 安全性"
- Search "MCP协议 最新 2025"
- 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
FAQ
What does the x-collect 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.
How do I install x-collect?
Run npx skills add https://github.com/kangarooking/x-skills --skill x-collect in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/x-collect/ (personal) or .claude/skills/x-collect/ (project).
Which AI agents can use x-collect?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is x-collect safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains x-collect?
It is published by kangarooking in kangarooking/x-skills. It has 89 recorded installs.
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