Daily Ai News Skill Litiao

Aggregates and summarizes the latest AI news from multiple sources including AI news websites and web search. Uses Tavily API (preferred) or Brave Search (fa...

Security Scan

The skill claims to aggregate AI news via Tavily (which matches the single required env var), but its runtime instructions expect local Node scripts and a workspace path that are not included or declared — an incoherent and potentially risky mismatch.

Purpose & Capability

The declared purpose (daily AI news using Tavily or Brave Search) matches the single required env var (TAVILY_API_KEY). However, the SKILL.md instructs the agent to run local Node scripts under ~/.openclaw/workspace/skills/tavily-search-litiao and to use a web reader tool; the skill package contains no code or install spec for those Node scripts and does not declare Node as a required binary. Expecting local scripts while shipping an instruction-only skill is an incoherence.

Instruction Scope

The instructions tell the agent to: cd into a specific user workspace path and run node scripts, use mcp__web_reader__webReader to fetch full articles, and call Tavily (if key present) or fallback to web_search. Asking the agent to run arbitrary local scripts from ~/.openclaw and to execute node commands expands scope beyond simple web search/summary behavior and could cause the agent to run unknown code on the host. The skill references filesystem paths and execution steps not present in the bundle.

Install Mechanism

There is no install specification and no shipped code, yet the SKILL.md expects a local Node-based toolchain and scripts. The skill does not declare Node (or any runtime) as a required binary. This mismatch means the skill implicitly assumes preexisting artifacts in the user's workspace, which is fragile and may cause the agent to run arbitrary local code if those artifacts exist.

Credentials

Only one environment variable is required: TAVILY_API_KEY. That matches the stated preference for Tavily search and is proportionate to the skill's declared functionality. No unrelated secrets or broad credential requests are present.

Persistence & Privilege

The skill is not always-on and does not request elevated/persistent privileges. It does not declare modifications to other skills or system-wide settings. Autonomous invocation remains allowed by default (normal for skills) but is not combined here with other high-risk privileges.

What to consider before installing

This skill is suspicious because its runtime instructions expect local Node scripts in ~/.openclaw/workspace/skills/tavily-search-litiao even though no code or install steps are included in the package and Node is not declared as a required binary. Before installing or enabling this skill: - Ask the author for the missing code and an install spec (how to install the Node scripts and any dependencies). Verify those scripts' source and contents. - Do not provide your TAVILY_API_KEY until you can inspect the code that will use it. Confirm how and where the key is sent/stored by the scripts. - If the skill will run local scripts, review those scripts for network calls, exfiltration, or commands that access other parts of your filesystem. - Prefer skills that either are instruction-only and only call platform-provided web/tools (no local exec), or that include a clear, auditable install step and declared required binaries. - If you test this skill, run it in a sandboxed environment (no sensitive credentials) and monitor network activity. Given the clear mismatch between instructions and packaged contents, treat this skill as untrusted until the missing artifacts and their behavior are verified.

Daily AI News Briefing

Aggregates the latest AI news from multiple sources and delivers concise summaries with direct links

When to Use This Skill

Activate this skill when the user:

• Asks for today's AI news or latest AI developments
• Requests a daily AI briefing or updates
• Mentions wanting to know what's happening in AI
• Asks for AI industry news, trends, or breakthroughs
• Wants a summary of recent AI announcements
• Says: "给我今天的AI资讯" (Give me today's AI news)
• Says: "AI有什么新动态" (What's new in AI)

Workflow Overview

This skill uses a 4-phase workflow to gather, filter, categorize, and present AI news:

Phase 1: Information Gathering
  ├─ Direct website fetching (3-5 major AI news sites)
  └─ Web search with date filters
      ↓
Phase 2: Content Filtering
  ├─ Keep: Last 24-48 hours, major announcements
  └─ Remove: Duplicates, minor updates, old content
      ↓
Phase 3: Categorization
  └─ Organize into 5 categories
      ↓
Phase 4: Output Formatting
  └─ Present with links and structure

Phase 1: Information Gathering

Step 1.1: Fetch from Primary AI News Sources

Use mcp__web_reader__webReader to fetch content from 3-5 major AI news websites:

Recommended Primary Sources (choose 3-5 per session):

• VentureBeat AI: https://venturebeat.com/category/ai/
• TechCrunch AI: https://techcrunch.com/category/artificial-intelligence/
• The Verge AI: https://www.theverge.com/ai-artificial-intelligence
• MIT Technology Review AI: https://www.technologyreview.com/topic/artificial-intelligence/
• AI News: https://artificialintelligence-news.com/
• AI Hub Today: https://ai.hubtoday.app/

Parameters:

• return_format: markdown
• with_images_summary: false (focus on text content)
• timeout: 20 seconds per source

Step 1.2: Execute Web Search Queries

Preferred: Use Tavily Search (if TAVILY_API_KEY is available):

cd ~/.openclaw/workspace/skills/tavily-search-litiao

# General AI news
node scripts/search.mjs "AI news today" --topic news --days 1 -n 15

# Research papers
node scripts/search.mjs "AI research paper machine learning breakthrough" --topic news --days 3 -n 10

# Industry news
node scripts/search.mjs "AI startup funding company news" --topic news --days 3 -n 10

# Product launches
node scripts/search.mjs "AI application launch new tool" --topic news --days 3 -n 10

Fallback: Use Brave WebSearch (via web_search tool):

Query Template (adjust dates dynamically):

General: "AI news today" OR "artificial intelligence breakthrough" after:[2025-12-23]
Research: "AI research paper" OR "machine learning breakthrough" after:[2025-12-23]
Industry: "AI startup funding" OR "AI company news" after:[2025-12-23]
Products: "AI application launch" OR "new AI tool" after:[2025-12-23]

Best Practices:

• Always use current date or yesterday's date in filters
• Execute 2-3 queries across different categories
• Limit to top 10-15 results per query
• Prioritize sources from last 24-48 hours
• Tavily advantage: Better news clustering, cleaner snippets, --topic news optimized for current events

Step 1.3: Fetch Full Articles

For the top 10-15 most relevant stories from search results:

• Extract URLs from search results
• Use mcp__web_reader__webReader to fetch full article content
• This ensures accurate summarization vs. just using snippets

Phase 2: Content Filtering

Filter Criteria

Keep:

• News from last 24-48 hours (preferably today)
• Major announcements (product launches, model releases, research breakthroughs)
• Industry developments (funding, partnerships, regulations, acquisitions)
• Technical advances (new models, techniques, benchmarks)
• Significant company updates (OpenAI, Google, Anthropic, etc.)

Remove:

• Duplicate stories (same news across multiple sources)
• Minor updates or marketing fluff
• Content older than 3 days unless highly significant
• Non-AI content or tangentially related articles

Deduplication Strategy

When the same story appears in multiple sources:

• Keep the most comprehensive version
• Note alternative sources in the summary
• Prioritize authoritative sources (company blogs > news aggregators)

Phase 3: Categorization

Organize news into 5 categories:

🔥 Major Announcements

• Product launches (new AI tools, services, features)
• Model releases (GPT updates, Claude features, Gemini capabilities)
• Major company announcements (OpenAI, Google, Anthropic, Microsoft, Meta)

🔬 Research & Papers

• Academic breakthroughs
• New research papers from top conferences
• Novel techniques or methodologies
• Benchmark achievements

💰 Industry & Business

• Funding rounds and investments
• Mergers and acquisitions
• Partnerships and collaborations
• Market trends and analysis

🛠️ Tools & Applications

• New AI tools and frameworks
• Practical AI applications
• Open source releases
• Developer resources

🌍 Policy & Ethics

• AI regulations and policies
• Safety and ethics discussions
• Social impact studies
• Government initiatives

Phase 4: Output Formatting

Use the following template for consistent output:

# 📰 Daily AI News Briefing

**Date**: [Current Date, e.g., December 24, 2025]
**Sources**: [X] articles from [Y] sources
**Coverage**: Last 24 hours

---

## 🔥 Major Announcements

### [Headline 1]

**Summary**: [One-sentence overview of the news]

**Key Points**:
- [Important detail 1]
- [Important detail 2]
- [Important detail 3]

**Impact**: [Why this matters - 1 sentence]

📅 **Source**: [Publication Name] • [Publication Date]
🔗 **Link**: [URL to original article]

---

### [Headline 2]

[Same format as above]

---

## 🔬 Research & Papers

### [Headline 3]

[Same format as above]

---

## 💰 Industry & Business

### [Headline 4]

[Same format as above]

---

## 🛠️ Tools & Applications

### [Headline 5]

[Same format as above]

---

## 🌍 Policy & Ethics

### [Headline 6]

[Same format as above]

---

## 🎯 Key Takeaways

1. [The biggest news of the day - 1 sentence]
2. [Second most important development - 1 sentence]
3. [An emerging trend worth watching - 1 sentence]

---

**Generated on**: [Timestamp]
**Next update**: Check back tomorrow for the latest AI news

Customization Options

After providing the initial briefing, offer customization:

1. Focus Areas

"Would you like me to focus on specific topics?"

• Research papers only
• Product launches and tools
• Industry news and funding
• Specific companies (OpenAI/Google/Anthropic)
• Technical tutorials and guides

2. Depth Level

"How detailed should I go?"

• Brief: Headlines only (2-3 bullet points per story)
• Standard: Summaries + key points (default)
• Deep: Include analysis and implications

3. Time Range

"What timeframe?"

• Last 24 hours (default)
• Last 3 days
• Last week
• Custom range

4. Format Preference

"How would you like this organized?"

• By category (default)
• Chronological
• By company
• By significance

Follow-up Interactions

User: "Tell me more about [story X]"

Action: Use mcp__web_reader__webReader to fetch the full article, provide detailed summary + analysis

User: "What are experts saying about [topic Y]?"

Action: Search for expert opinions, Twitter reactions, analysis pieces

User: "Find similar stories to [story Z]"

Action: Search related topics, provide comparative summary

User: "Only show research papers"

Action: Filter and reorganize output, exclude industry news

Quality Standards

Validation Checklist

• All links are valid and accessible
• No duplicate stories across categories
• All items have timestamps (preferably today)
• Summaries are accurate (not hallucinated)
• Links lead to original sources, not aggregators
• Mix of sources (not all from one publication)
• Balance between hype and substance

Error Handling

• If webReader fails for a URL → Skip and try next source
• If search returns no results → Expand date range or try different query
• If too many results → Increase threshold for significance
• If content is paywalled → Use available excerpt and note limitation

Examples

Example 1: Basic Request

User: "给我今天的AI资讯"

AI Response: [Executes 4-phase workflow and presents formatted briefing with 5-10 stories across categories]

---

Example 2: Time-specific Request

User: "What's new in AI this week?"

AI Response: [Adjusts date filters to last 7 days, presents weekly summary]

---

Example 3: Category-specific Request

User: "Any updates on AI research?"

AI Response: [Focuses on Research & Papers category, includes recent papers and breakthroughs]

---

Example 4: Follow-up Deep Dive

User: "Tell me more about the GPT-5 announcement"

AI Response: [Fetches full article, provides detailed summary, offers to find expert reactions]

Additional Resources

For comprehensive lists of news sources, search queries, and output templates, refer to:

• references/news_sources.md - Complete database of AI news sources
• references/search_queries.md - Search query templates by category
• references/output_templates.md - Alternative output format templates