--- name: embodied-ai-news source: https://app.decimal.ai/s/embodied-ai-news@1/SKILL.md source_sha256: ed51b4d362f5 --- # Embodied AI News Briefing > Aggregates the latest Embodied AI & Robotics news from curated sources and delivers concise summaries with direct links. Covers the full stack: algorithms, hardware, simulation, deployment, funding, policy, and the China ecosystem. ## When to Use This Skill Activate this skill when the user: - Asks for embodied AI news, robot news, or humanoid robot updates - Requests a daily/weekly/monthly robotics briefing - Mentions wanting to know what's happening in embodied AI / robotics - Asks about specific companies: Tesla Optimus, Figure, Unitree, AGIBOT, Boston Dynamics, etc. - Asks about specific technologies: VLA models, diffusion policy, sim-to-real, dexterous manipulation - Wants a summary of recent robotics research papers - Asks about robotics funding, deployments, or supply chain - Asks about simulation platforms, benchmarks, or datasets - Asks about robotics policy, safety standards, or export controls - Requests a monthly trend report or competitive analysis - Says: "给我今天的具身智能资讯" (Give me today's embodied AI news) - Says: "机器人行业有什么新动态" (What's new in the robot industry) - Says: "最近有什么人形机器人的消息" (Any recent humanoid robot news) - Says: "这个月的具身智能趋势报告" (This month's embodied AI trend report) - Says: "embodied AI updates", "robot learning news", "humanoid robot news" ### Trigger Keywords **English**: `embodied AI`, `humanoid robot`, `robot news`, `robotics update`, `robot learning`, `VLA model`, `diffusion policy`, `dexterous manipulation`, `sim-to-real`, `robot deployment`, `robotics funding`, `Figure AI`, `Tesla Optimus`, `Unitree`, `AGIBOT`, `Boston Dynamics`, `1X`, `Physical Intelligence`, `Skild AI`, `robot hand`, `quadruped robot`, `Isaac Sim`, `world model robot`, `robot benchmark`, `robot safety`, `robot regulation`, `monthly robot report` **Chinese**: `具身智能`, `人形机器人`, `机器人资讯`, `灵巧操作`, `仿真到真实`, `机器人部署`, `宇树`, `智元`, `优必选`, `银河通用`, `傅利叶`, `机器人融资`, `灵巧手`, `四足机器人`, `机器人大模型`, `机器人月报`, `机器人安全`, `机器人政策` --- ## Reference Files This skill relies on 5 companion reference files. Always consult them during execution: ``` 📁 references/ ├── 📰 news_sources.md — WHERE to find information (tiered source list) ├── 🔍 search_queries.md — HOW to search (query templates & recipes) ├── 📝 output_templates.md — WHAT format to output (6+ template variants) ├── 📊 taxonomy.md — SHARED LANGUAGE (categories, keywords, company list) └── 🧭 workflow.md — WHEN and in what ORDER to execute (SOP for daily/weekly/monthly) ``` | File | When to Consult | | --------------------- | --------------------------------------------------------------------------------------- | | `news_sources.md` | Phase 1 — choosing which sites to fetch; selecting tier-appropriate sources | | `search_queries.md` | Phase 1 — building search queries; selecting recipe by briefing type | | `taxonomy.md` | Phase 3 — classifying stories; Phase 1 — looking up company aliases & tech terms | | `output_templates.md` | Phase 5 — rendering final output; selecting template by user request | | `workflow.md` | All Phases — orchestrating the end-to-end workflow; time budgeting; monthly maintenance | ### File Interconnection Map ``` ┌─────────────────┐ ┌────────────────────┐ ┌───────────────┐ ┌──────────────────┐ │ search_queries │────▶ │ news_sources │────▶│ Classify & │────▶│ output_templates │ │ (discover) │ │ (browse & verify) │ │ Prioritize │ │ (generate) │ └─────────────────┘ └────────────────────┘ └───────────────┘ └──────────────────┘ ▲ ▲ │ │ └────── taxonomy.md ─────┘ (shared vocabulary) ``` --- ## Execution Workflow ### Phase 0: Determine Briefing Type & Time Scope **Before any tool calls**, ask the user (if not already clear): 1. **Briefing Type**: Daily / Weekly / Monthly / Custom Topic? 2. **Time Scope**: Last 24 hours / Last 7 days / Last 30 days / Custom date range? 3. **Output Format**: Standard / Brief / Thread / Markdown Report / Presentation / Custom? 4. **Focus Area** (optional): All categories / Specific category (e.g., only hardware, only China ecosystem)? **Default if user doesn't specify**: - Type: Daily - Scope: Last 24 hours - Format: Standard - Focus: All categories **Map to workflow.md**: - Daily → `workflow.md` Section "Daily Workflow" - Weekly → `workflow.md` Section "Weekly Workflow" - Monthly → `workflow.md` Section "Monthly Workflow" --- ### Phase 1: Information Gathering Consult `workflow.md` for the appropriate recipe, then execute the corresponding steps from `search_queries.md` and `news_sources.md`. #### Step 1.1: Execute Search Queries **Tool**: `WebSearch` (or equivalent web search tool) **Source**: `search_queries.md` → Select the appropriate recipe: - Daily Briefing → Recipe A (5 queries) - Weekly Roundup → Recipe B (8 queries) - Monthly Deep Dive → Recipe C (12 queries) - Custom Topic → Recipe D + user-specified filters **Parameters**: - `return_format`: markdown - `with_images_summary`: false - `timeout`: 20 seconds per source - Fetch only from publicly accessible sources listed in `news_sources.md` **Output**: A list of 20–50 URLs with headlines and snippets. --- #### Step 1.2: Fetch Tier 1 Sources Directly **Tool**: `mcp__web_reader__webReader` **Source**: `news_sources.md` → Tier 1 section Directly fetch the homepage or RSS feed of: - The Robot Report - IEEE Spectrum — Robotics - TechCrunch — Robotics - Robotics Business Review - (Add others based on briefing type) **Parameters**: - `url`: [homepage URL from news_sources.md] - `return_format`: markdown - `with_images_summary`: false - Process only URLs from verified sources in `news_sources.md` **Output**: Recent headlines (last 24h / 7d / 30d based on scope). --- #### Step 1.3: Fetch arXiv Papers **Tool**: `mcp__arxiv__readURL` (if available) or `WebSearch` with arXiv-specific queries **Source**: `search_queries.md` → Section "6. Academic Research (arXiv)" Execute 2–3 arXiv queries: ``` cat:cs.RO AND ("embodied AI" OR "robot learning" OR "VLA") submittedDate:[today - 7d TO today] ``` **Output**: 5–10 recent papers with abstracts. --- #### Step 1.4: Fetch Company Blogs & Official Announcements **Tool**: `mcp__web_reader__webReader` **Source**: `news_sources.md` → Tier 2 (Company Blogs) + Tier 4 (China Ecosystem) Fetch from: - Figure AI Blog - Physical Intelligence Blog - Tesla AI Blog - Unitree Blog (Chinese + English) - AGIBOT WeChat Official Account (if accessible) - (Add others based on focus area) **Fetch constraints**: - Only process URLs from search results and sources listed in `news_sources.md` - Skip content requiring authentication - Timeout: 15 seconds per URL **Output**: Recent announcements (last 7d / 30d based on scope). --- ### Phase 2: Content Extraction & Deduplication For each fetched URL: 1. **Extract**: - Headline - Publication date - Source name - Summary (first 2–3 paragraphs or abstract) - Key entities: companies, models, hardware platforms (use `taxonomy.md` for reference) 2. **Deduplicate**: - If multiple sources cover the same story, keep the one with the most detail - Merge information if they provide complementary details 3. **Discard**: - Stories older than the time scope - Irrelevant content (use `search_queries.md` Section 1.4 "Noise Exclusion Filter") - Duplicate announcements **Output**: A deduplicated list of 15–30 stories with extracted metadata. --- ### Phase 3: Classification & Prioritization Consult `taxonomy.md` to classify each story. #### Step 3.1: Assign Primary Category Use `taxonomy.md` → Section "1. News Category Taxonomy" Assign each story to **exactly one** primary category: - 🔥 Major Announcements - 🧠 Foundation Models & Algorithms - 🦾 Hardware & Platforms - 🌐 Simulation & Infrastructure - 🏭 Deployments & Commercial - 💰 Funding, M&A & Business - 🌍 Policy, Safety & Ethics - 🇨🇳 China Ecosystem **Rules** (from `taxonomy.md` → "Category Assignment Rules"): - **Major Announcements**: Only for top-impact stories (new paradigm, >$500M funding, first-ever deployment milestone) - **China Ecosystem**: Use when the story's primary significance is about the Chinese market/ecosystem - **Cross-cutting stories**: Assign primary + up to 2 secondary tags --- #### Step 3.2: Assign Priority Level Use `taxonomy.md` → Section "3. Priority Scoring System" Calculate priority score (0–100) based on: - **Impact** (0–40 points): Paradigm shift / Major milestone / Incremental improvement - **Timeliness** (0–20 points): Breaking news / Recent (1–3 days) / Older - **Source Authority** (0–20 points): Tier 1 / Tier 2 / Tier 3 - **Relevance** (0–20 points): Core embodied AI / Adjacent / Tangential **Priority Levels**: - **P0 (90–100)**: Must-read, above-the-fold - **P1 (70–89)**: Important, include in main body - **P2 (50–69)**: Notable, include if space allows - **P3 (<50)**: Optional, move to "Other News" section or omit --- #### Step 3.3: Sort Stories Within each category, sort by: 1. Priority score (descending) 2. Publication date (most recent first) --- ### Phase 4: Content Synthesis For each story, generate: 1. **One-sentence summary**: Capture the core news in <20 words 2. **Key points** (2–4 bullet points): Extract the most important details 3. **Metadata fields** (based on category): - For **Foundation Models**: Model Type, Embodiment, Open Source, Impact - For **Hardware**: Hardware Type, Company, Specs, Impact - For **Deployments**: Deployment Scale, Industry Vertical, Performance Metrics, Impact - For **Funding**: Amount, Lead Investor, Valuation, Use of Funds - (See `output_templates.md` for full metadata schema per category) 4. **Impact statement**: Why this matters for the embodied AI field (1–2 sentences) **Tone & Style**: - **Objective**: Present facts without hype or editorial opinion - **Concise**: Favor clarity over completeness - **Technical**: Use domain-specific terminology from `taxonomy.md` - **Neutral**: Treat all companies, countries, and technologies equally --- ### Phase 5: Output Generation Consult `output_templates.md` to select the appropriate template. #### Step 5.1: Select Template Based on user request (from Phase 0): | User Request | Template to Use | | --------------------- | -------------------------- | | "Daily briefing" | Standard Format | | "Quick summary" | Brief Format | | "Twitter thread" | Thread Format | | "Markdown report" | Markdown Report Format | | "Presentation slides" | Presentation Format | | "Custom" | Adapt from Standard Format | --- #### Step 5.2: Render Output Fill in the selected template with: - **Header**: Date, source count, time scope - **Category sections**: Ordered by priority (🔥 Major Announcements first) - **Story blocks**: Headline, summary, key points, metadata, source link - **Footer**: Methodology note, source attribution **Quality checks**: - All links are valid and correctly formatted - All metadata fields are filled (use "N/A" if not applicable) - No duplicate stories - Stories are sorted by priority within each category - Total output length is appropriate for briefing type: - Daily: 1,500–2,500 words - Weekly: 3,000–5,000 words - Monthly: 5,000–10,000 words --- #### Step 5.3: Add Contextual Notes (Optional) If the user requested analysis or trends, append: - **Trend Spotlight**: 2–3 emerging patterns observed this period - **Company Momentum**: Which companies/labs are most active - **Technology Shifts**: Notable changes in technical approaches - **Geographic Insights**: Regional differences (e.g., US vs China ecosystem) Use `taxonomy.md` → Section "5. Trend Analysis Framework" for guidance. --- ### Phase 6: Delivery & Follow-up 1. **Deliver the briefing** in the selected format 2. **Offer follow-up options**: - "Would you like me to deep-dive into any specific story?" - "Should I track these companies/topics for your next briefing?" - "Would you like a comparison with last week/month's trends?" --- ## Special Workflows ### Custom Topic Deep-Dive If user asks about a specific topic (e.g., "What's new with dexterous hands?"): 1. **Consult** `taxonomy.md` → Section "2. Technology & Product Taxonomy" → Find relevant subcategories 2. **Build custom queries** using `search_queries.md` → Recipe D (Custom Topic) 3. **Fetch** from all tiers in `news_sources.md` that cover this topic 4. **Output** using the "Deep-Dive Format" from `output_templates.md` --- ### Company-Specific Briefing If user asks about a specific company (e.g., "What's Figure AI been up to?"): 1. **Consult** `taxonomy.md` → Section "4. Company & Organization Directory" → Find company profile 2. **Build queries** targeting: - Company blog - News mentions - arXiv papers by company researchers - Funding announcements 3. **Output** using the "Company Spotlight Format" from `output_templates.md` --- ### China Ecosystem Focus If user asks specifically about China (e.g., "中国人形机器人有什么进展?"): 1. **Prioritize** `news_sources.md` → Tier 4 (China Ecosystem) 2. **Use** `search_queries.md` → Section "8. China Ecosystem" 3. **Consult** `taxonomy.md` → Section "4.3 China Ecosystem Companies" 4. **Output** in Chinese or bilingual format (ask user preference) --- ## Operational Guidelines ### Operating Scope This skill operates in **read-only mode**: - Fetches content from public sources listed in reference files - Synthesizes and presents information to the user - Does not modify, post, or interact with external systems - Does not perform actions on behalf of the user unless explicitly requested (e.g., "add this to my calendar") ### Information Freshness - **Daily briefing**: Prioritize stories from the last 24 hours - **Weekly briefing**: Include stories from the last 7 days, but highlight the most recent - **Monthly briefing**: Cover the full 30 days, but organize by week or theme ### Source Diversity Aim for a balanced mix: - 40% from Tier 1 (core industry media) - 30% from Tier 2 (company blogs & official sources) - 20% from Tier 3 (academic & research) - 10% from Tier 4 (China ecosystem, if relevant) ### Quality over Quantity - Better to have 15 high-quality, well-summarized stories than 50 shallow headlines - If a story lacks detail or verification, mark it as "Unconfirmed" or omit it ### Handling Uncertainty - If a story's details are unclear, state: "Details are limited; awaiting official confirmation" - If sources conflict, present both versions: "Source A reports X, while Source B reports Y" - Never fabricate details to fill gaps ### Language Handling - If user asks in Chinese, output in Chinese (but keep company/model names in English) - If user asks in English, output in English - For bilingual users, offer: "Would you like this in English, Chinese, or bilingual?" --- ## Error Handling ### If a source is unreachable: - Skip it and note in the footer: "Note: [Source Name] was unavailable at the time of this briefing" ### If search returns no results: - Broaden the query or try alternative keywords from `taxonomy.md` - If still no results, inform the user: "No recent news found for [topic] in the specified time range" ### If classification is ambiguous: - Default to the most specific applicable category - Add a secondary tag if the story spans multiple domains ### If output exceeds length limits: - Prioritize P0 and P1 stories - Move P2 and P3 stories to a "Quick Hits" section with one-line summaries - Offer to generate a separate deep-dive on omitted topics --- ## Maintenance & Updates ### Monthly (consult `workflow.md` → "Monthly Workflow"): - Review `taxonomy.md` for new companies, models, or terminology - Update `news_sources.md` if new authoritative sources emerge - Refine `search_queries.md` based on what queries yielded the best results ### Quarterly: - Audit the priority scoring system — are P0 stories truly the most impactful? - Review output templates — do they match user preferences? --- ## Example Invocations ### Example 1: Daily Briefing **User**: "Give me today's embodied AI news" **Agent**: 1. Determines: Daily briefing, last 24h, Standard format, All categories 2. Executes Recipe A from `search_queries.md` (5 queries) 3. Fetches Tier 1 sources from `news_sources.md` 4. Classifies using `taxonomy.md` 5. Outputs using Standard Format from `output_templates.md` --- ### Example 2: Weekly Roundup **User**: "What happened in robotics this week?" **Agent**: 1. Determines: Weekly briefing, last 7 days, Standard format, All categories 2. Executes Recipe B from `search_queries.md` (8 queries) 3. Fetches Tier 1 + Tier 2 sources 4. Prioritizes P0 and P1 stories 5. Outputs using Standard Format with "Trend Spotlight" section --- ### Example 3: Custom Topic **User**: "What's new with VLA models?" **Agent**: 1. Determines: Custom topic, last 7 days, Deep-Dive format 2. Consults `taxonomy.md` → "Vision-Language-Action (VLA) Models" 3. Builds custom queries from `search_queries.md` Section 2.1 4. Fetches from Tier 1 + Tier 3 (arXiv) 5. Outputs using Deep-Dive Format --- ### Example 4: Company Spotlight **User**: "What's Unitree been up to?" **Agent**: 1. Determines: Company-specific, last 30 days, Company Spotlight format 2. Consults `taxonomy.md` → Company profile for Unitree 3. Fetches Unitree blog + news mentions + arXiv papers 4. Outputs using Company Spotlight Format from `output_templates.md` --- ### Example 5: China Ecosystem **User**: "中国人形机器人有什么进展?" **Agent**: 1. Determines: China focus, last 7 days, Standard format, Chinese output 2. Prioritizes `news_sources.md` Tier 4 sources 3. Uses `search_queries.md` Section 8 (China Ecosystem) 4. Outputs in Chinese using Standard Format --- ## Summary This skill orchestrates a multi-phase workflow: 1. **Determine** briefing type & scope 2. **Gather** information from curated sources using structured queries 3. **Classify** stories using a shared taxonomy 4. **Prioritize** based on impact, timeliness, and relevance 5. **Synthesize** concise summaries with metadata 6. **Output** in the user's preferred format **Key success factors**: - Always consult the 5 reference files at the appropriate workflow stage - Maintain objectivity and source attribution - Prioritize quality and relevance over quantity - Adapt to user preferences (language, format, focus area)