Cosmic Insight
Cosmic Insight
Showcase
Description
Automatically searches multiple platforms for news in finance, tech, or business topics, filters preliminary and core clues, and uncovers related events within 10 years to output a structured topic analysis report. Step 1: Multi-Source Information Collection - Search news from the past 30 days covering WeChat official accounts, Wall Street CN, 36Kr (high weight), Huxiu, Xueqiu, TMTpost (standard weight) - Record title, time, platform, summary, original links - Initially filter duplicates, ads, and low-credibility content Step 2: Information Deduplication and Aggregation - Aggregate the same event by core object, core fact, and time window (within 7 days) - Count how many independent sources mention it - Remove low-value events, output a candidate event list Step 3: Event Analysis and Clue Classification Preliminary clues (any one): abnormal events, high discussion (more than 3 sources), unusual changes, major industry milestones, negative news about large companies Core clues (2 or more): dramatic conflict, room for meaning exploration, more than 3 entities, timeliness (within 14 days) - At least 5 core clues, total 10-25 clues - Each clue includes: event title, summary, deep analysis of writing value, criteria met Step 4: Deep Association Mining - Expand search scope to 10 years - Extract key entities, search: historical nodes of the subject, industry policy evolution, competitor dynamics over the same period, upstream and downstream industry chain changes - Include any association, note association type and year Step 5: Structured Output Output includes: 1. Core clues block; 2. Preliminary clues block; 3. Research suggestions Each clue includes: event overview, criteria met, deep writing value analysis, related events (10-year history), reference links Tools: Web search, long document generation Reference resources: None fixed; user can attach at input.
Related Skills
View all
Research Report Generator
📝 Value in one sentence: Enter a topic or upload materials, and AI handles the entire process from organizing sources to drafting. 🔍 Core features: Smart source integration: Upload multiple documents and AI automatically extracts key viewpoints, critical data, and representative cases, while removing duplicates and flagging conflicts. Automatic outline generation: Based on the topic or source content, it creates a logically clear and well-structured article outline. One-click draft writing: Generates content section by section according to the outline, with every data point and case study source-tagged and traceable. Five writing styles: Supports formal reports, WeChat articles, Xiaohongshu notes, academic papers, and business briefings. Transparent citations: Every figure and viewpoint is marked with its source—no fabrication or invention. Automated content workflow: Source parsing → viewpoint extraction → outline generation → writing in five styles. 🎯 Use cases: 📊 Industry research: Enter "Humanoid Robot Industry 2026" to generate a draft analysis report with data and case studies. 📝 Content creation: Upload 3 related articles to combine into an in-depth WeChat article. 📚 Academic writing: Upload multiple paper abstracts to create a literature review draft. 📋 Competitor analysis: Upload several competitor documents to instantly generate a comparative analysis report. 🧠 Personal learning: Enter a new-domain topic to quickly generate a knowledge overview. 💼 Business decisions: Input business data and background information to produce a decision brief for the CEO. 👩💼 Who it's for: Researchers, analysts, content creators, product managers, students, and consultants—anyone who needs to quickly produce high-quality first drafts.
Grant Proposal Review PRO V2.0
🎯 Core Functionality Overview This is an intelligent review and optimization system specially designed for national social science, education ministry, and provincial grant applications. It simulates the thinking mode of a senior review expert with 15 years of experience, ensuring academic rigor and competitiveness through three core mechanisms. 🔧 Three Core Mechanisms 1️⃣ 12-Step Structured Methodology Covers the full lifecycle of grant proposal review: Phase 1-3: Basic Diagnosis - In-depth analysis of announcement (funding priorities, review criteria, application requirements) - Cross-disciplinary type judgment (precise identification of 8 types) - Research GAP five-dimension identification (theory/methodology/empirical/policy/technology) Phase 4-7: Core Element Review - Research question TMAQ model analysis (theory/methodology/approach/question four dimensions) - Research objective SMART principle test - Research content framework completeness assessment - Research approach type matching (6 types) Phase 8-10: Deep Quality Enhancement - Precise extraction of key difficulties (distinguish criteria + breakthrough paths) - Innovation point seven-dimension mining - Feasibility seven-dimension argumentation Phase 11-12: Overall Optimization - Nine-dimension quality check (academic rigor, innovativeness, feasibility, etc.) - Comprehensive optimization suggestions and final report 2️⃣ Dual-Core Adversarial Mechanism (Builder vs Supervisor) Working Principle: - Builder (academic writer): Generates optimization plans based on user materials - Supervisor (top journal reviewer): Challenges Builder's plans with the strictest standards - Adversarial iteration: 3 rounds of confrontation to ensure plans are robust Application Scenarios: - Innovation point mining: Builder proposes innovation points → Supervisor questions novelty → iterative optimization - Feasibility argumentation: Builder designs plan → Supervisor challenges feasibility → supplementary argumentation - Literature citation: Builder cites literature → Supervisor verifies authenticity → ensure academic standards 3️⃣ Literature Authenticity Verification Mechanism Two working modes: Mode A: Placeholder Mode (Default) - Use markers like [Literature Placeholder-001] in place of specific references - Output a Literature Requirement List specifying search requirements for each placeholder - User searches and fills in real references Mode B: Real-Time Verification Mode - Call Google Scholar to verify literature authenticity in real time - Generate Literature Verification Report (authenticity/relevance/authority scores) - Ensure every citation is traceable Preventing AI Hallucination: - Prohibits fabricating authors, journals, DOIs - All references must be verified or marked as placeholders - Guarantees academic integrity bottom line 💡 Core Value and Applicable Scenarios ✅ Key Pain Points Addressed 1. Academic sloppiness: AI-generated content often includes fake references, logical gaps 2. Insufficient innovation: Difficulty uncovering true academic innovation points 3. Weak feasibility: Research plans lack systematic argumentation 4. Cross-disciplinary difficulty: Interdisciplinary topics often fall between two stools 🎓 Target Users - University faculty (social sciences, education, humanities) - Researchers (applying for national and provincial grants) - Academic teams (needing systematic review processes) 📋 Typical Workflow 1. Input: Upload announcement + proposal draft 2. Review: System executes 12-step structured analysis 3. Adversarial: Dual-core mechanism iteratively optimizes key sections 4. Verification: Literature authenticity check 5. Output: Complete review report + optimization suggestions + literature list 🔍 Differences from Traditional Review | Dimension | Traditional Human Review | Expert Review System | |-----------|------------------------|----------------------| | Review depth | Depends on personal experience | 12-step structured + 9D QC | | Academic rigor | Hard to fully audit | Literature verification + dual-core adversarial | | Innovation mining | Subjective judgment | 7-dimension systematic analysis | | Feasibility argumentation | Experience-driven | 7-dimension item-by-item argumentation | | Consistency | Varies by individual | Standardized process | | Efficiency | Days to weeks | 1-2 hours for initial review | The core advantage of this system is: it makes the tacit knowledge of a 15-year senior review expert explicit, structured, and replicable, enabling every user to receive top-level expert review services.
Deep Stock Research Engine
Not a news summary, but an individual stock research report with a methodological framework. Enter a company (A-share/H-share/US stock name or code), and it will perform 10-15 bilingual web searches to produce a report based on a seven-dimension framework: ① Business Model – where money comes from and goes, with a "translated into plain language" version ② Revenue Structure – breakdown by business, growth engines and drags ③ Moat – mandatory answer on widening or narrowing, using market share, gross margin, and pricing power ④ Financial Quality – cash flow vs profit alignment, changes in receivables, inventory, and goodwill ⑤ Competitive Landscape – competitor comparison table and concentration direction ⑥ Valuation Framework – only data and calculation framework, no judgment of over/undervaluation ⑦ Risk List – sorted by impact, each with verifiable trigger signals Each report includes: a one-page bull/bear summary (with substantial evidence on both sides), a next-quarter tracking list, and data sources for each point. Three iron rules: · Each key number is traceable to official filings or authoritative media; if not found, labeled 'insufficient information', never fabricated · Facts, calculations, and inferences are labeled separately · No stock price predictions, no target prices, no buy/sell advice – even for 'should I buy', only bull/bear evidence is given Supports three perspectives: Understand the Business (default), Financial Checkup, and Company Comparison. Suitable for investment researchers, financial content creators, and serious investors who want to systematically understand a company.
Information
- Version
- v1
- Last updated
- Runtime credits
- Usage-based
- Models
- Gemini 3.1 Pro
