# Send Embodied Ai News to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - Download the package from Yavira. - Extract it into a folder your agent can access. - Paste one of the prompts below and point your agent at the extracted folder. ## Suggested prompts ### New install ```text I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete. ``` ### Upgrade existing ```text I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "embodied-ai-news", "name": "Embodied Ai News", "source": "tencent", "type": "skill", "category": "开发工具", "sourceUrl": "https://clawhub.ai/HeXavi8/embodied-ai-news", "canonicalUrl": "https://clawhub.ai/HeXavi8/embodied-ai-news", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/embodied-ai-news", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=embodied-ai-news", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "SKILL.md", "references/news_sources.md", "references/output_templates.md", "references/search_queries.md", "references/taxonomy.md", "references/workflow.md" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "slug": "embodied-ai-news", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-05-02T22:25:01.230Z", "expiresAt": "2026-05-09T22:25:01.230Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=embodied-ai-news", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=embodied-ai-news", "contentDisposition": "attachment; filename=\"embodied-ai-news-1.0.4.zip\"", "redirectLocation": null, "bodySnippet": null, "slug": "embodied-ai-news" }, "scope": "item", "summary": "Item download looks usable.", "detail": "Yavira can redirect you to the upstream package for this item.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/embodied-ai-news" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/embodied-ai-news", "downloadUrl": "https://openagent3.xyz/downloads/embodied-ai-news", "agentUrl": "https://openagent3.xyz/skills/embodied-ai-news/agent", "manifestUrl": "https://openagent3.xyz/skills/embodied-ai-news/agent.json", "briefUrl": "https://openagent3.xyz/skills/embodied-ai-news/agent.md" } } ``` ## Documentation ### 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) FileWhen to Consultnews_sources.mdPhase 1 — choosing which sites to fetch; selecting tier-appropriate sourcessearch_queries.mdPhase 1 — building search queries; selecting recipe by briefing typetaxonomy.mdPhase 3 — classifying stories; Phase 1 — looking up company aliases & tech termsoutput_templates.mdPhase 5 — rendering final output; selecting template by user requestworkflow.mdAll 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) ### Phase 0: Determine Briefing Type & Time Scope Before any tool calls, ask the user (if not already clear): Briefing Type: Daily / Weekly / Monthly / Custom Topic? Time Scope: Last 24 hours / Last 7 days / Last 30 days / Custom date range? Output Format: Standard / Brief / Thread / Markdown Report / Presentation / Custom? 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: Extract: Headline Publication date Source name Summary (first 2–3 paragraphs or abstract) Key entities: companies, models, hardware platforms (use taxonomy.md for reference) Deduplicate: If multiple sources cover the same story, keep the one with the most detail Merge information if they provide complementary details 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: Priority score (descending) Publication date (most recent first) ### Phase 4: Content Synthesis For each story, generate: One-sentence summary: Capture the core news in <20 words Key points (2–4 bullet points): Extract the most important details 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) 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 RequestTemplate 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 Deliver the briefing in the selected format 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?" ### Custom Topic Deep-Dive If user asks about a specific topic (e.g., "What's new with dexterous hands?"): Consult taxonomy.md → Section "2. Technology & Product Taxonomy" → Find relevant subcategories Build custom queries using search_queries.md → Recipe D (Custom Topic) Fetch from all tiers in news_sources.md that cover this topic 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?"): Consult taxonomy.md → Section "4. Company & Organization Directory" → Find company profile Build queries targeting: Company blog News mentions arXiv papers by company researchers Funding announcements 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?"): Prioritize news_sources.md → Tier 4 (China Ecosystem) Use search_queries.md → Section "8. China Ecosystem" Consult taxonomy.md → Section "4.3 China Ecosystem Companies" Output in Chinese or bilingual format (ask user preference) ### 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?" ### 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 ### 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 1: Daily Briefing User: "Give me today's embodied AI news" Agent: Determines: Daily briefing, last 24h, Standard format, All categories Executes Recipe A from search_queries.md (5 queries) Fetches Tier 1 sources from news_sources.md Classifies using taxonomy.md Outputs using Standard Format from output_templates.md ### Example 2: Weekly Roundup User: "What happened in robotics this week?" Agent: Determines: Weekly briefing, last 7 days, Standard format, All categories Executes Recipe B from search_queries.md (8 queries) Fetches Tier 1 + Tier 2 sources Prioritizes P0 and P1 stories Outputs using Standard Format with "Trend Spotlight" section ### Example 3: Custom Topic User: "What's new with VLA models?" Agent: Determines: Custom topic, last 7 days, Deep-Dive format Consults taxonomy.md → "Vision-Language-Action (VLA) Models" Builds custom queries from search_queries.md Section 2.1 Fetches from Tier 1 + Tier 3 (arXiv) Outputs using Deep-Dive Format ### Example 4: Company Spotlight User: "What's Unitree been up to?" Agent: Determines: Company-specific, last 30 days, Company Spotlight format Consults taxonomy.md → Company profile for Unitree Fetches Unitree blog + news mentions + arXiv papers 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: Determines: China focus, last 7 days, Standard format, Chinese output Prioritizes news_sources.md Tier 4 sources Uses search_queries.md Section 8 (China Ecosystem) Outputs in Chinese using Standard Format ### Summary This skill orchestrates a multi-phase workflow: Determine briefing type & scope Gather information from curated sources using structured queries Classify stories using a shared taxonomy Prioritize based on impact, timeliness, and relevance Synthesize concise summaries with metadata 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) ## Trust - Source: tencent - Verification: Indexed source record - Publisher: HeXavi8 - Version: 1.0.3 ## Source health - Status: healthy - Item download looks usable. - Yavira can redirect you to the upstream package for this item. - Health scope: item - Reason: direct_download_ok - Checked at: 2026-05-02T22:25:01.230Z - Expires at: 2026-05-09T22:25:01.230Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/embodied-ai-news) - [Send to Agent page](https://openagent3.xyz/skills/embodied-ai-news/agent) - [JSON manifest](https://openagent3.xyz/skills/embodied-ai-news/agent.json) - [Markdown brief](https://openagent3.xyz/skills/embodied-ai-news/agent.md) - [Download page](https://openagent3.xyz/downloads/embodied-ai-news)