Use Cases

Steps

1. 1.Configure Dify workflow and connect Brave Search to fetch industry trends.
2. 2.Set AI Agent prompts to define brand tone and content format.
3. 3.Have the Agent generate a 7-day content calendar.

Steps

1. 1.Use fetch MCP to periodically scrape competitor website content
2. 2.Search for competitor-related news and social discussions via Perplexity
3. 3.AI analyzes change trends and extracts key insights

Steps

1. 1.Enter target keywords and competitor domain names
2. 2.Agent automatically analyzes SERP results and user intent
3. 3.Generate SEO-optimized article outlines

Steps

1. 1.Install SWE-agent and configure GitHub Token
2. 2.Set trigger rules (Issues labeled "auto-fix")
3. 3.Agent analyzes the issue and related code

Steps

1. 1.Install GitHub MCP Server in Cursor
2. 2.Set up code review prompt template
3. 3.Trigger automatic review and view suggestions

Steps

1. 1.Configure the database MCP Server to connect to the target database
2. 2.Set read-only permissions to ensure data security
3. 3.Write system prompts describing the database table structure

Steps

1. 1.Visit Deep Research or Genspark
2. 2.Enter the research topic and report framework requirements
3. 3.AI automatically searches and integrates information

Steps

1. 1.Upload PDFs to a designated folder
2. 2.Configure filesystem MCP to read files
3. 3.Let AI extract key information and structure it

Steps

1. 1.List target competitors
2. 2.Agent automatically visits official websites, App Store reviews, and social media
3. 3.Analyze product feature matrix and differentiation advantages

Steps

1. 1.Create a Gmail Trigger workflow in n8n
2. 2.Add an AI node to determine email type and priority
3. 3.Generate reply drafts and mark important emails

Steps

1. 1.Upload the recording file after the meeting
2. 2.Whisper API automatically transcribes to text
3. 3.AI extracts Action Items and assignees

Steps

1. 1.Organize and export Notion/Obsidian notes
2. 2.Create a knowledge base in Dify and upload documents
3. 3.Configure RAG parameters (chunk size, similarity threshold)

Steps

1. 1.Configure filesystem MCP to read the PDF contract
2. 2.Write professional contract review prompts
3. 3.Claude analyzes key clauses one by one

Steps

1. 1.Input the target category and target platform
2. 2.Agent searches for hot-selling products and market data
3. 3.Analyze competitive landscape and price range

Steps

1. 1.Configure database MCP to connect to the financial system
2. 2.Define report templates and analysis dimensions
3. 3.AI automatically generates SQL queries and analyzes data

Steps

1. 1.Enter the target company name and research focus areas
2. 2.Agent automatically searches the company website, Crunchbase, and LinkedIn
3. 3.Analyze funding rounds, valuation trends, and investors

Steps

1. 1.Define target patent scope and keywords
2. 2.Download USPTO/EPO patent documents via fetch MCP
3. 3.Analyze patent claims and technical solutions with Claude

Steps

1. 1.Configure the filesystem MCP to allow the AI to read the codebase
2. 2.Let the AI analyze the Controller/Route layer code
3. 3.Automatically identify interface parameters and return value structures

Steps

1. 1.Install and configure Sentry MCP and K8s MCP
2. 2.Set monitoring thresholds and trigger conditions
3. 3.Agent automatically analyzes stack traces after receiving alerts

Steps

1. 1.Save resume PDFs to a designated folder
2. 2.Let AI extract education, experience, and skill information
3. 3.Automatically score and rank according to job description

Steps

1. 1.Upload original content to Google Drive
2. 2.Configure target languages and localization style guides
3. 3.AI performs semantic translation (not word-for-word)

Steps

1. 1.Input research question in PICO format
2. 2.AI searches PubMed and arXiv for relevant papers
3. 3.Filter literature based on inclusion/exclusion criteria

Steps

1. 1.Connect to PostgreSQL database to store financial data
2. 2.Configure historical comparison and industry benchmark thresholds
3. 3.AI automatically calculates key financial ratios

Steps

1. 1.Use n8n to periodically pull new leads from CRM (HubSpot/Salesforce)
2. 2.Design scoring rules: company size × industry fit × interaction frequency
3. 3.AI generates personalized emails for each high-scoring lead (citing the client's latest company news)

Steps

1. 1.Create a 'JD Generation' workflow in Dify, input key job information
2. 2.AI generates a complete JD based on templates and industry standards
3. 3.Use browser-use to control the browser and automatically log into each recruitment platform

Steps

1. 1.Connect data sources: App Store API / CSV import / Zendesk tickets
2. 2.AI tags each feedback: feature request / bug / performance / UI experience
3. 3.Automatically cluster similar feedback and count mention frequency

Steps

1. 1.Configure information sources in n8n: RSS/Twitter API/WeChat public account scraping
2. 2.Set interest keywords and filtering rules (exclude ads/low-quality content)
3. 3.AI generates 2-3 sentence core summaries for each article

Steps

1. 1.Collect student information: target exam, current level, daily available study time
2. 2.AI breaks down the exam syllabus and generates a knowledge point map with priorities
3. 3.Push daily learning tasks and corresponding resources

Steps

1. 1.Define the content topic and target audience
2. 2.Use AI tools to generate an article outline
3. 3.Generate the first draft section by section

Steps

1. 1.Map the entire marketing chain and identify high-repetition tasks
2. 2.Select appropriate AI tools for each task
3. 3.Build a standardized prompt template library

Steps

1. 1.Clarify learning objectives or teaching tasks
2. 2.Select an AI tool matching the scenario
3. 3.Design an effective prompt (define role, task, constraints)

Steps

1. 1.Install an AI coding plugin in your IDE (Cursor or Copilot)
2. 2.Provide sufficient context to the AI (project description, tech stack)
3. 3.Describe requirements in natural language and let AI generate the code skeleton

Steps

1. 1.List the top 5 most time-consuming repetitive tasks in your business
2. 2.Assess the degree of AI replaceability for each task
3. 3.Start with the simplest, highest-ROI scenario for a pilot

Steps

1. 1.Use Perplexity/Claude to search for competitor ranking content for target keywords
2. 2.Analyze the structure, word count, and content gaps of the top 5 articles
3. 3.Design an SEO-optimized article outline (H1/H2/H3 structure)

Steps

1. 1.Connect user feedback data sources (App Store, Zendesk, NPS, etc.)
2. 2.Design AI classification prompt, define classification dimensions and tag system
3. 3.Use Claude API to batch process feedback and generate structured data

Steps

1. 1.Map out your current sales process and identify the most time-consuming repetitive tasks.
2. 2.Integrate CRM data and configure a lead enrichment tool (e.g., Clay).
3. 3.Design personalized email prompt templates that dynamically generate based on prospect profiles.

Steps

1. 1.Enable Deep Research mode in Perplexity Pro
2. 2.Input a structured competitive research prompt: "Compare and analyze [your product] vs [competitor A] and [competitor B] across dimensions including: feature comparison, pricing strategy, target users, user reviews (from Reddit/G2/Trustpilot), and major updates in the last 6 months"
3. 3.Wait for Deep Research to automatically execute 15-20 searches (approximately 10 minutes)

Steps

1. 1.Organize enterprise documents: Export PDF/Markdown files from Confluence/Feishu
2. 2.Install dependencies: pip install chromadb langchain anthropic
3. 3.Write document loading script: Read files, split into chunks of 500-1000 characters, generate vectors, and store in Chroma

Steps

1. 1.Record the meeting video (export MP4 from Zoom/Tencent Meeting, or use Google Meet recording directly)
2. 2.Open gemini.google.com, upload the meeting recording file (or a Google Drive link)
3. 3.Use a standardized prompt: 'This is a meeting recording. Please: 1) Generate structured meeting minutes (attendees/discussion topics/key decisions/open issues) 2) List all action items (owner/deadline/specific task) 3) Mark the video timestamp for each action item'

Steps

1. 1.Create .mcp.json in the project root, configuring GitHub MCP and filesystem MCP
2. 2.Create CLAUDE.md to define code review rules and output format (🔴🟡🟢 levels)
3. 3.Test the workflow in Claude Code with "review PR #1"

Steps

1. 1.Define customer health indicators: login frequency, feature adoption rate, support ticket count, contract renewal time.
2. 2.Configure HubSpot Trigger in n8n to pull account data daily.
3. 3.Use postgres MCP to connect to the product usage log database.

Steps

1. 1.Configure a PR-triggered workflow in GitHub Actions
2. 2.Extract the PR diff content and format it into a structured format
3. 3.Use the Claude API to analyze code changes and detect security/performance/style issues

Steps

1. 1.Use RSSHub to aggregate RSS feeds from target information sources
2. 2.Set up an n8n scheduled task to trigger scraping at 7 AM every morning
3. 3.AI evaluates the importance of each piece of content (1-5 scale) and filters out low-scoring items

Steps

1. 1.Extract user behavior metrics (activity frequency, feature usage, payment records) from the database using Python
2. 2.AI automatically performs RFM segmentation (Recency/Frequency/Monetary) on users
3. 3.AI generates natural language feature descriptions and operational suggestions for each user group

Steps

1. 1.Upload the contract PDF to Dify knowledge base or analyze directly with Claude
2. 2.AI extracts key contract information: parties, amount, term, breach clauses
3. 3.AI identifies unfavorable clauses and missing protective items against industry standards

Steps

1. 1.Organize the product parameter table, including fields such as model, specifications, materials, and applicable scenarios.
2. 2.Design platform-specific prompt templates (Taobao focuses on emotion, JD focuses on parameters).
3. 3.Use Python to batch read parameters and call the OpenAI API line by line to generate copy.

Steps

1. 1.Requirement analysis: Define quality inspection standards (types of appearance defects, dimensional tolerances), evaluate production line layout, select industrial cameras and edge computing devices.
2. 2.Data collection: Collect 5000+ defect sample images (qualified products + various defects), perform pixel-level annotation using Labelme.
3. 3.Model training: Fine-tune an object detection model based on YOLOv11, achieving 99.2% accuracy on the internal dataset.

Steps

1. 1.Data Integration: Connect ERP (Kingdee/SAP), bank statements, and e-commerce platform data to establish a unified data pipeline, with Python ETL scripts pulling data daily automatically.
2. 2.AI Analysis Layer: Send standardized data to GPT-4o to generate key metric analysis, anomaly detection, and narrative interpretation comparing with previous periods and budgets.
3. 3.Report Template: Build standard report templates in Power BI, with AI analysis results automatically populating predefined formats (executive summary + detailed schedules + trend charts).

Steps

1. 1.Concept exploration: Use Midjourney to quickly generate 20-30 visual concept directions, present multiple styles at client proposal meetings, replacing the traditional 2-3 days with 2 hours.
2. 2.UI component generation: Use Figma AI to rapidly generate variants of buttons, cards, forms, etc., based on design system specifications, reducing repetitive work.
3. 3.Copy generation: Let ChatGPT generate interface copy, error messages, empty state descriptions, etc., allowing designers to focus on visuals rather than text.

Steps

1. 1.Information collection: Use Typeform to create a standardized assessment questionnaire to collect fitness test data, exercise history, goals, and time constraints, with results automatically saved to Notion.
2. 2.Plan generation: Use ChatGPT to read client information and generate a 12-week training plan (weekly schedule, sets/reps/weights, progression scheme). The coach reviews and sends it out.
3. 3.Dietary advice: Based on client goals and dietary preferences, AI generates weekly dietary suggestions, automatically updated every two weeks, and sent to clients via WeChat/email through Zapier.

Steps

1. 1.Current state analysis: Analyze ticket data from the past 3 months, identify the top 20 issue types, and calculate resolution time and repeat rate for each type.
2. 2.Knowledge base organization: Organize FAQs and customer service documents into a structured format (problem description - solution - relevant screenshots - applicable versions) and migrate to Notion.
3. 3.AI configuration: Enable Fin AI in Intercom, connect the Notion knowledge base, configure response style, and set trigger conditions for human handoff.

Steps

1. 1.Assess current workflow: Map time allocation across case processing stages, identify the most time-consuming repetitive tasks (contract review, legal research, client communication).
2. 2.Deploy Harvey AI: Integrate into contract review workflow; AI generates preliminary review reports, allowing lawyers to focus on strategic decisions rather than line-by-line review.
3. 3.Build a legal research database: Compile research findings on common legal issues into a knowledge base, paired with ChatGPT for quick citation retrieval.

Steps

1. 1.AI-generated sand table scripts: Generate tailored presentation scripts for different unit types, floor levels, and customer segments (first-time buyers, upgraders, investors) for sales staff to use on demand.
2. 2.AI-rendered interior images: Use Midjourney to quickly generate renderings of different decoration styles to help customers envision their future home.
3. 3.HeyGen digital presenter: Create AI-powered introduction videos available 24/7, allowing remote customers to learn about the project anytime.

Steps

1. 1.Traffic flow data collection: Deploy cameras and geomagnetic sensors at major intersections to collect real-time data on traffic volume, speed, and queue length.
2. 2.Build traffic model: Use Python + TensorFlow to train an intersection flow prediction model, learning traffic patterns at different times of the day.
3. 3.Intelligent signal optimization algorithm: Dynamically adjust signal timing based on real-time traffic data to reduce unnecessary waiting.

Steps

1. 1.Accelerate market research with AI: Use AI to analyze competitors, generate interview guides, conduct 20 user interviews, and extract key insights with AI.
2. 2.AI-assisted product planning: Input user insights into Claude to generate a PRD (Product Requirements Document) and feature priority matrix.
3. 3.Rapid prototyping with Figma AI: Use Figma AI to generate UI framework, refine design, and complete a 20-page prototype in 1 day.

Steps

1. 1.Tool Selection Testing: Compare Midjourney, Firefly, and SD in terms of accuracy, efficiency, and cost for product image generation, and select the most suitable tool combination.
2. 2.Build Prompt Library: Create standardized generation prompts for different product categories (clothing/digital/home) to ensure consistent brand style.
3. 3.Workflow Standardization: Front-end product image → Photoshop cutout → AI background replacement → batch scene generation, forming a standard SOP.

Steps

1. 1.Build a diagnostic assessment system: Design a standardized academic diagnostic question bank (200 questions per subject). After the entrance assessment, AI automatically outputs a knowledge point mastery report and a list of weak areas.
2. 2.Generate personalized learning paths: Based on diagnostic results, AI creates targeted practice plans for each student, prioritizing weak knowledge points in high-frequency exam topics.
3. 3.AI-assisted lesson preparation: Teachers input teaching objectives into iFlytek Spark, and AI automatically generates draft courseware, practice questions, and homework. Teachers review and revise, reducing preparation time from 3 hours to 1.5 hours.

Steps

1. 1.Digital reporting: Develop a WeChat mini-program for claim reporting, where customers submit accident photos/videos and supporting documents; AI OCR automatically extracts key information (accident time, damage description, amount) and stores it in a structured format.
2. 2.AI initial review: GPT-4o Vision analyzes accident photos to automatically assess damage severity and estimate repair costs; NLP models analyze text descriptions, compare them with historical cases and policy terms, and generate initial review opinions.
3. 3.Fraud detection: Use machine learning models to compare the applicant's historical claim records, accident patterns, and submitted image metadata; high-risk cases are automatically flagged for manual review.

Steps

1. 1.Data infrastructure construction: Integrate historical delivery data (routes, timeliness, costs), real-time traffic API (Amap), and station capacity data to establish a unified data platform.
2. 2.AI route planning: Use Google OR-Tools to build a Vehicle Routing Problem (VRP) model, automatically generating the optimal delivery routes for the entire day at 6 AM daily, considering time windows, load limits, and driver working hours.
3. 3.Dynamic scheduling: Monitor delivery progress in real-time; when delays occur (accidents/traffic changes), AI re-plans delivery routes for affected areas within 30 seconds.

Steps

1. 1.AI demand forecasting: Integrate historical sales data (daily sales per dish), weather, holidays, and nearby events to build an LSTM prediction model. Push daily inventory recommendations (precise quantities per dish) to each store by 8 AM.
2. 2.Smart ordering system: On the mini-program ordering page, dynamically adjust dish recommendation order and display based on time of day, user order history, and current inventory to quickly move soon-to-expire ingredients.
3. 3.Personalized marketing: Analyze member purchase behavior to segment users into 5 types (frequent regulars, price-sensitive, novelty seekers, family-type, single-type). Target push coupons accordingly (new dishes for regulars, discounts for price-sensitive).

Steps

1. 1.Automated requirement collection: Design a structured requirement questionnaire (departure city, dates, number of people, budget, preferences, restrictions). After clients fill it out, n8n automatically organizes it into a consultant worksheet, reducing back-and-forth communication.
2. 2.AI-generated itinerary draft: Consultants feed the requirement sheet to ChatGPT, using optimized prompts to generate a complete itinerary draft covering transportation, accommodation, attractions, restaurants, and notes. Consultants then review and refine.
3. 3.Visualized proposal: Use Notion templates to create illustrated proposals, and Canva to automatically generate polished PDF versions, significantly enhancing professionalism.

Steps

1. 1.Build trend monitoring system: Use Python to scrape popular content tags from Xiaohongshu, Douyin, and Instagram, integrate Google Trends API, and automatically generate daily 'Trending Keyword Rankings' and 'Emerging Style Direction' reports.
2. 2.AI-powered product selection analysis: Input trend reports, competitor new product data, and historical similar product sales data into ChatGPT to generate comprehensive product selection recommendations including 'recommendation rationale', 'predicted sales range', and 'risk warnings'.
3. 3.Small-batch test orders: Place initial orders of 50 units for AI-recommended products, and decide whether to restock based on 7-day sales velocity (daily average sales + repurchase rate + add-to-cart rate).

Steps

1. 1.ABC analysis + heatmap location optimization: Analyze 6-month outbound frequency, relocate high-frequency SKUs (Class A, 20% of total) to golden locations near the shipping area, reducing walking distance for picking.
2. 2.AI picking path planning: Implement shortest path algorithm in the warehouse based on OR-Tools, switching from random picking to 'batch wave + shortest path' mode, reducing average walking distance per order by 35%.
3. 3.Anomaly detection AI: Train computer vision models to identify misplaced items, blurred labels, and damaged packaging, automatically intercepting before packing, reducing error rate from 0.8% to 0.15%.

Steps

1. 1.Omnichannel Integration: Use Zendesk API to integrate Taobao Wangwang, WeChat Work, and Amazon Seller Central messages, funneling them into a single customer service workstation, eliminating the need for agents to switch platforms.
2. 2.Unified Knowledge Base: Organize product descriptions, return/exchange policies, and FAQs into a structured knowledge base (JSON format), connect to ChatGPT API, and support synchronized updates across platforms.
3. 3.AI Tiered Processing: AI automatically resolves standard issues (shipping tracking, refund status, product inquiries); complex issues (disputes, special returns, complaints) are automatically escalated to human agents with AI-generated issue summaries.

Steps

1. 1.Data system construction: Integrate Amap POI data (surrounding commercial density), foot traffic heat maps (weekday/weekend distribution), competitor store distribution, and rent database to establish a site selection data foundation.
2. 2.AI scoring model: Train a site selection scoring model based on historical store data. Input candidate locations and output a comprehensive score (target customer density, competition level, rent affordability, transportation convenience).
3. 3.Profit forecasting: Based on the site selection score and revenue data from historical stores with similar scores, generate a 12-month revenue forecast range and break-even time estimate for the new store.

Steps

1. 1.Voice entry deployment: Install noise-canceling microphones in consultation rooms and integrate iFlytek Medical AI voice recognition to support real-time conversion of doctors' dictation into structured medical record fields (chief complaint, history of present illness, signs, diagnosis). Doctors confirm and enter into the HIS system.
2. 2.Assisted diagnosis integration: An AI diagnostic assistance system trained on 2 million historical hospital cases pushes real-time suggestions of 'common diagnoses for similar cases + recommended tests' for doctors' reference after they enter symptoms.
3. 3.Data anonymization and privacy protection: All medical record data is processed on the hospital's local servers. AI model training uses federated learning and differential privacy, complying with the Personal Information Protection Law and the Data Security Law.

Steps

1. 1.Systematic topic selection: Every Monday, use ChatGPT to analyze trending topics across platforms, generate 20-30 topic ideas based on account positioning, filter out the 10 with the highest viral potential, and create a Notion content calendar.
2. 2.One draft, multiple uses: First write a 2,000-word in-depth article, then use AI to automatically rewrite it into: a Douyin/Kuaishou script (500 words), a Xiaohongshu post with images (300 words + 5 images), a WeChat public account article (1,500 words), and Twitter/LinkedIn posts (280 words).
3. 3.Batch image generation: Use Midjourney to generate cover images that fit each platform's dimensions, then use Canva AI to quickly adjust layouts and brand elements. Production time for each set of visual assets drops from 1 hour to 15 minutes.

Steps

1. 1.AI Customer Profile Analysis: Integrate customer source channels, browsing history, query keywords, and property viewing behavior data to train an intent prediction model, classifying customers into 'high/medium/low intent' categories and automatically assigning follow-up priorities.
2. 2.Pre-Show AI Preparation: The agent inputs property information and customer profiles, and the AI generates personalized showing scripts (tailored to different concerns: school district, investment, self-use) along with 30 common questions and reference answers that may arise.
3. 3.Real-Time Assistance During Showings: Develop a WeChat mini-program that allows agents to access AI suggestions on the fly (competitor comparison data, historical transaction prices in the same community, mortgage calculator) to answer customer questions more promptly and professionally.

Steps

1. 1.AI Resume Pre-screening: Integrate an AI scoring module into the ATS system to automatically score resumes based on key JD requirements (skill match, experience relevance, education). Only resumes scoring above 60 enter manual review, reducing pre-screening time by 70%.
2. 2.Job Description Optimization: Use AI to analyze successful hires from the past 3 years, identify candidate traits that truly predict success, rewrite JDs more precisely (reducing irrelevant applications by 40%), and check for gender/age bias in language.
3. 3.Intelligent Interview Question Bank: AI generates structured interview questions (behavioral + situational) tailored to different roles, with scoring criteria, reducing interviewer bias, and building a reference for evaluating candidate response quality.

Steps

1. 1.AI ad optimization (traffic): Integrated with Ocean Engine AI auto-bidding, tested 50+ ad creative combinations (headlines/images/videos), automatically eliminated low-performing creatives, scaled top performers, boosting overall ROI from 2.1 to 3.6.
2. 2.AI product page upgrade (conversion): Used Midjourney to generate AI model images replacing real shoots, reducing costs by 60%. AI analyzed competitor page structures to optimize selling point order, lifting conversion rate from 2.8% to 4.2%.
3. 3.AI customer service 24/7 (conversion): Deployed AI chatbot handling 65% of standard inquiries, reducing response time from 5 minutes to 20 seconds, increasing pre-purchase inquiry conversion rate by 18%.

Steps

1. 1.Defect database construction: Systematically collect 3 years of historical quality inspection data, gather 500+ sample images for each of 15 defect types (scratches, bubbles, eccentricity, dirt, etc.), perform professional annotation, and establish a standardized defect image dataset.
2. 2.Detection model training: Train a multi-task detection model based on YOLOv8 and ResNet architectures (simultaneously detecting positional and appearance defects), achieving 99.3% accuracy on the test set with a false detection rate below 0.5%.
3. 3.Production line integration and deployment: Install industrial cameras (5MP resolution + telecentric lenses) at key workstations, use edge computing servers for real-time image processing (single inspection < 150ms), and push inspection results to the MES system in real time.

Steps

1. 1.Establish a five-layer memory system: Create an Enterprise-level CLAUDE.md for security and compliance policies; Project-level files limited to 200-300 lines for team norms; Rules-level for path-conditional loading of detailed specifications; Local-level for personal notes, added to .gitignore.
2. 2.Configure context triage mechanism: Classify candidate information into four levels P0-P3, injecting only core logs and historical ticket handles into context, reducing token consumption from 18K to 2K, improving signal-to-noise ratio.
3. 3.Implement structured input and Stop Hook gate: Avoid vague prompts; provide specific functions and line numbers; configure Stop Hook to automatically run lint and unit tests; block submission if tests fail and let AI self-heal.

Steps

1. 1.In Claude Code, use the /loop command or the ultracode trigger to create a dynamic workflow; the system will automatically generate a JavaScript orchestration framework.
2. 2.Define the task objective and select an orchestration pattern, such as classify-and-act or fan-out-and-synthesize; Claude will generate a sub-agent coordination plan on the fly.
3. 3.Use Automations or /loop to set up scheduled triggers (e.g., every 5 minutes), allowing the loop to automatically discover tasks, assign work, and validate results.

Steps

1. 1.After switching models, manually adjust the thinking level to the level actually required by the task to avoid wasting tokens by keeping a high reasoning level.
2. 2.Periodically use /graphify or /compact to compress sessions during large projects, preventing excessively long histories from increasing the cost of each new message.
3. 3.Before complex agentic tasks, first use a cheaper model to decompose the task and clarify the scope, then hand it over to Fable for execution, reducing the number of exploratory rounds.

Steps

1. 1.Deploy a document parsing platform (e.g., RAGFlow DeepDoc or PaddleOCR-VL) to perform layout analysis, table restoration, and structure extraction on documents such as PDFs, scanned files, and drawings.
2. 2.Slice the parsed structured content (Markdown/JSON), build vector indexes (e.g., Infinity) and full-text indexes (e.g., Elasticsearch), and optionally integrate a knowledge graph.
3. 3.Design an offline ingestion pipeline: document parsing → slicing → vectorization → index construction; and an online Q&A pipeline: query rewriting → hybrid retrieval → re-ranking → LLM generation.

Steps

1. 1.Assess the Agent's memory needs: Determine whether long-term context, preference adherence, and timeliness updates are required, and choose architectures such as RAG, knowledge graphs, or Dreaming.
2. 2.Design the memory storage solution: Use vector databases (e.g., Pinecone) or graph databases (e.g., Neo4j) to store structured and unstructured memories.
3. 3.Implement the memory writing mechanism: Convert conversation history into persistent memories through explicit user instructions or automatic background extraction (e.g., Dreaming V0/V3).

Steps

1. 1.Check whether the teacher model satisfies two core conditions: thought pattern compatibility (high initial overlap rate) and possessing new capabilities that the student lacks (e.g., additional RL training).
2. 2.If the teacher's conditions are insufficient, prioritize models from the same family that have undergone additional RL training, or use multi-teacher OPD to integrate multiple expert capabilities.
3. 3.In the existing RL framework, replace the advantage function with the reverse KL divergence of the log ratio between teacher and student, enabling single-line code integration of OPD.

Steps

1. 1.Assess whether the current MAS workflow is fixed. If so, use MASPOB to jointly optimize each agent's prompt based on the Bandit algorithm, improving performance within 50 evaluations.
2. 2.If decentralized coordination is needed, deploy the EoM framework, enabling agents to automatically divide labor and collaborate through auctions, transactions, and wealth mechanisms without a central controller.
3. 3.For chain or graph MAS, adopt StreamMA streaming communication, where upstream agents forward results to downstream agents immediately after each inference step, achieving pipeline parallelism, reducing latency, and improving accuracy.

Steps

1. 1.Deploy the TingIS system and configure the data collection layer to capture user complaint voice streams in real time.
2. 2.In the semantic distillation module, use LLM to compress raw complaints into standardized short summaries and anonymize PII.
3. 3.Build a cascaded routing mechanism: use keyword matching to ensure core business accuracy, and multi-vector retrieval to cover long-tail scenarios.