sop-agent

This sop executes automated clustering and tagging of customer support chat data through a Python pipeline, producing clustered data, cluster tags, and a comprehensive analysis report for Stage 2 (Pattern Extraction). The agent orchestrates the Python clustering script, monitors execution, validates outputs, and generates an analysis report to guide subsequent extraction work. **Language:** Auto-detects Korean (한국어) or Japanese (日本語) from user input.

Generate an ALF implementation package from all pipeline outputs. Produces rules draft, RAG knowledge items, dialog type cross-analysis heatmap, automation feasibility analysis, ROI calculation, task flowcharts (05_sales_report/tasks/), API requirements doc, and final ALF implementation guide. **Language:** Auto-detects Korean (한국어) or Japanese (日本語) from user input.

Split rules_draft.md into individual rule files and expand rag_items.md into standalone RAG knowledge documents for direct ALF registration.

Generate a client-facing deployment scenario and QA set from pipeline outputs (Stages 1-6). Maps consultation categories to resolution methods (RAG/Task) and deployment steps, with test queries per category. Outputs HTML + Markdown, optionally publishes to Notion.

Complete end-to-end pipeline for transforming Excel customer support data into production-ready Agent SOP documents, flowcharts, ALF implementation package, individual ALF registration files, and client-facing deployment scenario through 7 stages.

Channel.io 봇의 RAG 응답 품질을 Playwright로 자동 테스트합니다.

Task JSON 품질 평가. 검증 및 품질 점수를 제공합니다.

Generate production-ready SOP documents from Stage 2 extraction results, with a verification loop that tests each SOP against real conversations. **Language:** Auto-detects Korean (한국어) or Japanese (日本語) from user input.

Extract patterns, FAQs, and SOP topic map from clustered customer support data by reading full conversation transcripts. Stage 2 of the Userchat-to-SOP pipeline. **Language:** Auto-detects Korean (한국어) or Japanese (日本語) from user input.

Analyze existing bot performance in customer support data. Preprocesses Excel chat data via Python script to classify bot conversations as resolved/unresolved, then generates a comprehensive bot performance report with topic-level insights. **Language:** Auto-detects Korean (한국어) or Japanese (日本語) from user input.

Requests Upstage API key via Channel.io when the key is not configured. Sends a message to Pete, waits for his reply, then writes the key to .env automatically.

Task JSON 파일을 채널톡에 업로드(생성/수정). channelId, x-account, 파일 경로를 받아 ALF Task API를 직접 호출한다.

This sop executes automated clustering and tagging of customer support chat data through a Python pipeline, producing clustered data, cluster tags, and a comprehensive analysis report for Stage 2 (Pattern Extraction). The agent orchestrates the Python clustering script, monitors execution, validates outputs, and generates an analysis report to guide subsequent extraction work. **Language:** Auto-detects Korean (한국어) or Japanese (日本語) from user input.

This SOP guides the generation of a production-ready Agent SOP document from extracted patterns and FAQs. This is **Stage 3** (final stage) of the Userchat-to-SOP pipeline, performed entirely by the AI agent using natural language composition. **Language:** Auto-detects Korean (한국어) or Japanese (日本語) from user input. **Stage Flow:** - **Input**: Stage 2 extraction results (JSON files with patterns, FAQs, strategies) - **Process**: LLM composition of Agent SOP following standard format - **Output**: Agent SOP document (.sop.md) ready for deployment **Key Capabilities:** - Generate Agent SOP in standardized format (RFC 2119 compliant) - Transform extracted patterns into parameterized workflows - Create constraint-based steps with MUST/SHOULD/MAY keywords - Include examples and troubleshooting sections - Ensure reusability across different customer support scenarios

Generate an ALF implementation package from all pipeline outputs. Produces rules draft, RAG knowledge items, dialog type cross-analysis heatmap, automation feasibility analysis, ROI calculation, task flowcharts (04_tasks/), API requirements doc, and final ALF implementation guide. **Language:** Auto-detects Korean (한국어) or Japanese (日本語) from user input.

Complete end-to-end pipeline for transforming Excel customer support data into production-ready Agent SOP documents and flowcharts through Clustering, Pattern Extraction, SOP Generation, and Flowchart Generation stages.

This SOP guides the real sample-based LLM extraction of patterns, FAQs, and response strategies from clustered customer support data. This is Stage 2 of the Userchat-to-SOP pipeline, combining Python sample extraction with AI agent natural language analysis. **Language:** Auto-detects Korean (한국어) or Japanese (日本語) from user input.

Generate Mermaid flowcharts from Stage 3 SOP documents and convert to SVG images. Visualizes customer support processes with color-coded decision trees. **Language:** Auto-detects Korean (한국어) or Japanese (日本語) from user input.