Use Cases

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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.