القائمة
Task JSON 품질 평가. 검증 및 품질 점수를 제공합니다.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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.
| name | evaluate-task |
| description | Task JSON 품질 평가. 검증 및 품질 점수를 제공합니다. |
Read 툴로 아래 파일들을 읽는다:
methodology.md의 "1.2 검증 수행 방법"을 따른다.
검증 원칙:
검증 통과 시에만 실행한다.
Read 툴로 아래 파일들을 읽는다:
