Use Cases

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 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.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.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.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.