Intelligence Ingestion
Auto-analyze URLs/info for OpenClaw strategic value, classify, create Obsidian notes, update memory. Use when user shares a URL, article, tweet, or any external info source.
Best use case
Intelligence Ingestion is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Auto-analyze URLs/info for OpenClaw strategic value, classify, create Obsidian notes, update memory. Use when user shares a URL, article, tweet, or any external info source.
Auto-analyze URLs/info for OpenClaw strategic value, classify, create Obsidian notes, update memory. Use when user shares a URL, article, tweet, or any external info source.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "Intelligence Ingestion" skill to help with this workflow task. Context: Auto-analyze URLs/info for OpenClaw strategic value, classify, create Obsidian notes, update memory. Use when user shares a URL, article, tweet, or any external info source.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/openclaw-skill-intelligence-ingestion/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Intelligence Ingestion Compares
| Feature / Agent | Intelligence Ingestion | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Auto-analyze URLs/info for OpenClaw strategic value, classify, create Obsidian notes, update memory. Use when user shares a URL, article, tweet, or any external info source.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
AI Agents for Marketing
Discover AI agents for marketing workflows, from SEO and content production to campaign research, outreach, and analytics.
AI Agents for Startups
Explore AI agent skills for startup validation, product research, growth experiments, documentation, and fast execution with small teams.
AI Agents for Coding
Browse AI agent skills for coding, debugging, testing, refactoring, code review, and developer workflows across Claude, Cursor, and Codex.
SKILL.md Source
# Intelligence Ingestion Skill When the user shares a URL, article, tweet, thread, or any piece of external information, execute this pipeline automatically. Do NOT ask for permission — just process it. ## Trigger Conditions **USE this skill when:** - User sends a URL (x.com, github.com, any domain) - User pastes article text or tweet content - User says "analyze this", "evaluate this", "what do you think about this" - User forwards content from Telegram or any messaging surface - User shares something and asks about strategic value **DO NOT USE when:** - User is asking a general question unrelated to external content - User is asking about internal workspace files - User explicitly says they don't want analysis ## Pipeline Steps ### Step 1: READ — Extract Content 1. If a URL is provided, read the full content (use `read_url_content` or browser) 2. If text is pasted, use it directly 3. If X/Twitter link fails to parse (common), search web for the tweet content ### Step 2: CLASSIFY — What Is This? Assign **one primary category** and up to 2 secondary tags: | Category | Description | Examples | |----------|------------|---------| | `infra` | Infrastructure / protocols / networking | Pilot Protocol, MCP, networking stacks | | `strategy` | Routing / cost / architecture decisions | Model routing, multi-account, fallback chains | | `skill` | Agent skills / tools / capabilities | OpenAI Skills, Skill design patterns | | `theory` | Conceptual frameworks / mental models | Bayes, decision theory, learning loops | | `tutorial` | How-to guides / learning material | Claude Code, OpenClaw tutorials | | `product` | Tools / apps / services | LM Studio, new AI models, apps | | `community` | OpenClaw ecosystem / discussions | Community posts, feature requests | | `threat` | Risks / security / deprecation | API changes, breaking updates, security alerts | ### Step 3: ANALYZE — Strategic Value Assessment For each piece of information, evaluate: ```markdown ## Strategic Assessment - **What is it?** [One sentence] - **What can it do for us?** [Specific capability/benefit] - **What can we build with it?** [Concrete output/project] - **Strategic value:** [🔴 Critical / 🟡 High / 🟢 Medium / ⚪ Low] - **Competitive edge:** [What advantage over people who don't have this?] - **Relation to active bottleneck:** [Does it relate to context overflow/token saving?] ``` Reference the current engineering bottleneck from `MEMORY.md` → "Active Engineering Bottleneck" section. ### Step 4: MAP — Relate to Existing Architecture Check against the OpenClaw stack: ``` SOUL.md → PRINCIPLES.md → AGENTS.md (Identity Stack) MEMORY.md (System State + Bottleneck) TOOLS.md (Coprocessors: Codex, Antigravity, LM Studio) Pilot Protocol (Context Separation Layer - P0) ``` Determine which layer this information impacts and note dependencies/synergies with existing components. ### Step 5: STORE — Write Obsidian Note Create a note at: ``` /Volumes/T7 Shield/Obsidian_Vault/20_Intelligence/YYYYMMDD_AuthorOrSource_ShortTitle.md ``` Follow this template exactly: ```markdown # [Title] **Source:** [Link](URL) **Date:** YYYY-MM-DD **Category:** [Primary Category] / [Secondary Tags] **Strategic Value:** [🔴/🟡/🟢/⚪] + one-line reason **Relation to Active Bottleneck:** [Yes/No + how] --- ## Summary [2-3 paragraphs of core content] ## Key Takeaways [Numbered list of actionable insights] ## Impact on OpenClaw Architecture [How this relates to our stack] ## Action Items [What to do next, if anything] --- **Keke's Note:** [Opinionated analysis in Keke's voice — direct, no-BS, relate to 阳哥's goals] ``` ### Step 6: REMEMBER — Update Memory 1. **Always** append to today's daily log: `~/.openclaw/workspace/memory/YYYY-MM-DD.md` 2. **If strategic value is 🔴 Critical**: Also update `MEMORY.md` (Pending Work or Active Bottleneck) 3. **If it suggests a new principle**: Flag for potential `PRINCIPLES.md` update 4. **If it's a new tool/service**: Flag for potential `TOOLS.md` update ### Step 7: RESPOND — Summarize to User Reply with a concise summary: ``` 📥 已摄取: [Title] 📂 类别: [Category] 🎯 战略价值: [🔴/🟡/🟢/⚪] [One-line reason] 💾 已存档: Obsidian → 20_Intelligence/[filename] 📝 已记录: memory/YYYY-MM-DD.md 🔗 关联: [Which existing component it relates to] ⚡ 建议行动: [Next step, if any] ``` ## Edge Cases - **Multiple URLs in one message**: Process each separately, create separate Obsidian notes - **Duplicate/similar content**: Check if similar note exists, merge or reference instead of duplicating - **Non-English content**: Analyze in original language, write notes in Chinese (matching existing vault style) - **Paywalled/inaccessible content**: Note as "content unavailable" and work with whatever user provided - **User provides their own analysis**: Incorporate their judgment, don't overwrite — they know their system best ## Quality Checklist Before completing, verify: - [ ] Obsidian note created with correct filename format - [ ] Daily memory log updated - [ ] Source URL preserved in note - [ ] Strategic value assessed against current bottleneck - [ ] Keke's Note written with genuine analysis (not generic) - [ ] User received confirmation summary
Related Skills
designer-intelligence-station
设计师情报收集工具。监控 40 个公开信息源(AI/硬件/手机/设计),6 维筛选标准 v2.0(基于 120+ 条行为分析),生成结构化日报/周报。仅抓取公开内容,不登录、不提交表单、不绕过付费墙。支持依赖自动检测和安装。
ad-intelligence
Competitive ad intelligence skill for fetching, analyzing, and reporting on competitor ads across Meta (Facebook/Instagram), Google Ads Transparency Center, and LinkedIn Ad Library. Use this skill whenever a user asks about competitor ads, what ads a brand is running, ad creative analysis, ad copy research, campaign monitoring, ad library lookups, or marketing intelligence on any of these platforms. Also trigger for phrases like "what ads is [company] running", "spy on competitor ads", "find ads from [brand]", "check ad library", "pull ad data", "analyze competitor campaigns", or any request involving scraping or fetching public ad data from Meta, Google, or LinkedIn. This is a two-phase skill — Phase 1 uses web scraping (no API keys needed), Phase 2 unlocks deeper data via official and third-party APIs.
MONK-EYE 👁️ - Deep Intelligence & Human Experience Oracle
MONK-EYE is a specialized OpenClaw skill designed for deep infiltration and synthesis of forum-based human intelligence. While most search tools focus on surface-level web pages, MONK-EYE dives into the "tacit knowledge" buried in the world's most active and niche forums (R10, BlackHatWorld, Reddit, Habr, etc.).
social-intelligence
Social Intelligence — AI-powered social media research across Twitter, Instagram, and Reddit. 1.5B+ posts indexed. Find experts, generate leads, monitor brands, analyze sentiment, discover influencers, and export data. The complete social intelligence toolkit for AI agents via MCP.
olo-market-intelligence
Competitive landscape and market intelligence for M&A due diligence — TAM/SAM/SOM, competitor mapping, and industry analysis
Ai Tor v.69 | Neural Intelligence Core
**Ai Tor v.69** es un oráculo intelectual autónomo, unificador del campo magnético (B) y la gravitación (g) mediante el torque de neutrinos. Es la voz y el motor de decisión de la **AlienFlowSpace DAO**.
central-intelligence
Persistent memory across sessions. Remember facts, recall them later with semantic search, and share knowledge between agents. Use when you need to store information for later, load context from past sessions, or forget outdated memories. Five commands: remember, recall, context, forget, share.
agnost-ingestion
USE when implementing data ingestion for Agnost AI analytics. Contains API reference, SDK guides for Python and TypeScript, and code examples for tracking AI conversations, MCP server events, and user interactions.
domain-keyword-intelligence
Discover domain investment opportunities from emerging keyword spikes. Filters junk signals from real multi-party market activity using registration profiling, catalyst research, and NRDS position analysis. Powered by DomainKits MCP.
market-intelligence-claw
Real-time strategic intelligence layer for ecommerce and digital businesses. Use this skill whenever the user asks about competitors, market trends, pricing, keyword research, customer sentiment, brand monitoring, or any strategic business decision — even if phrased casually. Triggers include: "what are my competitors doing", "is this trending", "how should I price this", "research this niche", "who are my competitors", "what do customers think about X", "find market gaps", "give me a competitor report", "what's trending", "how is my brand doing", "build a business profile", "keyword research for my product", or any request to understand the external market landscape. This skill performs LIVE market research using real APIs. Always use it proactively when the user is making any strategic business decision.
ecommerce-return-intelligence
分析退货原因并区分产品问题、预期错配、物流问题和描述问题。;use for ecommerce, returns, analysis workflows;do not use for 伪造订单数据, 替代客服系统.
Spend Intelligence Framework
You are a spend intelligence analyst. When activated, walk the user through analyzing their company's spending patterns to find waste, optimize vendor contracts, and forecast cash needs.