feat(pipelines): add 10 industry-specific pipeline templates
Education (3): research-to-quiz, student-analysis, lesson-plan Healthcare (3): policy-compliance, meeting-minutes, data-report Design Shantou (4): trend-to-design, competitor-research, client-communication, supply-chain-collect
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pipelines/education/student-analysis.yaml
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pipelines/education/student-analysis.yaml
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# ZCLAW Pipeline - 学情分析报告
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# 输入班级学生成绩/表现数据，自动生成学情分析报告和分层教学建议
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apiVersion: zclaw/v1
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kind: Pipeline
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metadata:
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name: student-analysis
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displayName: 学情分析报告
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category: education
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industry: education
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description: 输入学生成绩或表现数据，自动分析学情并生成分层教学建议
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tags:
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- 教育
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- 学情分析
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- 分层教学
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- 数据分析
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icon: 📊
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author: ZCLAW
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version: 1.0.0
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spec:
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inputs:
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- name: student_data
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type: text
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required: true
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label: 学生数据
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placeholder: |
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粘贴成绩表或表现描述，支持格式：
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姓名,分数 或 姓名,等级,A/B/C 或自由文本描述
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- name: subject
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type: string
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required: false
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label: 科目
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default: 综合
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placeholder: 例如：数学
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- name: analysis_focus
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type: multi-select
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required: false
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label: 分析维度
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default:
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- score_distribution
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- weak_points
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- group_recommendation
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options:
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- score_distribution
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- weak_points
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- group_recommendation
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- improvement_plan
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- parent_communication
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- name: class_name
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type: string
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required: false
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label: 班级名称
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placeholder: 例如：初一(3)班
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steps:
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# Step 1: 数据解析与统计
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- id: parse_data
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description: 解析输入数据并生成基础统计
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action:
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type: llm_generate
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template: |
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请解析以下学生数据并生成基础统计：
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科目: {{subject}}
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班级: {{class_name}}
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学生数据:
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```
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{{student_data}}
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```
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请生成 JSON 格式统计：
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{
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"total_students": 0,
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"score_distribution": {
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"excellent": {"count": 0, "range": "90-100", "percentage": 0},
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"good": {"count": 0, "range": "80-89", "percentage": 0},
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"average": {"count": 0, "range": "60-79", "percentage": 0},
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"below_average": {"count": 0, "range": "<60", "percentage": 0}
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},
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"class_average": 0,
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"median_score": 0,
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"std_deviation": "高/中/低"
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}
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input:
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student_data: ${inputs.student_data}
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subject: ${inputs.subject}
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class_name: ${inputs.class_name}
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json_mode: true
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temperature: 0.3
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max_tokens: 2000
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# Step 2: 薄弱环节识别
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- id: identify_weakness
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description: 识别学生群体的薄弱知识点
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action:
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type: llm_generate
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template: |
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基于以下学情统计，识别薄弱环节：
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科目: ${inputs.subject}
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统计数据: ${steps.parse_data.output}
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学生数据:
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```
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${inputs.student_data}
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```
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请生成 JSON 格式分析：
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{
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"weak_areas": [
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{
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"area": "薄弱知识点",
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"affected_group": "受影响群体",
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"severity": "高/中/低",
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"possible_cause": "可能原因"
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}
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],
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"strong_areas": ["优势领域1", "优势领域2"],
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"polarization": "是否存在两极分化，程度如何"
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}
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input:
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subject: ${inputs.subject}
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stats: ${steps.parse_data.output}
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student_data: ${inputs.student_data}
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json_mode: true
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temperature: 0.4
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max_tokens: 2000
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# Step 3: 分层教学建议
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- id: group_recommendation
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description: 生成分层教学建议
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action:
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type: llm_generate
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template: |
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基于以下学情分析，生成分层教学建议：
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科目: ${inputs.subject}
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统计数据: ${steps.parse_data.output}
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薄弱环节: ${steps.identify_weakness.output}
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请生成 JSON 格式建议：
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{
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"groups": [
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{
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"name": "培优组",
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"criteria": "选拔标准",
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"size": "建议人数",
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"teaching_focus": "教学重点",
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"activities": ["建议活动1", "建议活动2"],
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"resources": ["推荐资源1"]
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},
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{
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"name": "提高组",
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"criteria": "选拔标准",
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"size": "建议人数",
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"teaching_focus": "教学重点",
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"activities": ["建议活动1"],
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"resources": ["推荐资源1"]
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},
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{
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"name": "基础组",
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"criteria": "选拔标准",
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"size": "建议人数",
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"teaching_focus": "教学重点",
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"activities": ["建议活动1"],
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"resources": ["推荐资源1"]
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}
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],
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"shared_activities": ["全班共同活动1"],
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"timeline": "建议实施周期"
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}
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input:
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subject: ${inputs.subject}
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stats: ${steps.parse_data.output}
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weakness: ${steps.identify_weakness.output}
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json_mode: true
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temperature: 0.6
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max_tokens: 2500
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# Step 4: 生成报告
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- id: generate_report
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description: 生成完整学情分析报告
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action:
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type: llm_generate
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template: |
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基于以上分析，生成一份完整的学情分析报告摘要。
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班级: ${inputs.class_name}
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科目: ${inputs.subject}
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请生成 JSON 格式报告：
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{
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"title": "学情分析报告标题",
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"executive_summary": "200字摘要",
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"data_overview": "数据概览描述",
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"key_findings": ["发现1", "发现2", "发现3"],
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"recommendations": ["建议1", "建议2", "建议3"],
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"preview_text": "300字报告预览"
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}
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input:
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class_name: ${inputs.class_name}
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subject: ${inputs.subject}
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stats: ${steps.parse_data.output}
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weakness: ${steps.identify_weakness.output}
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groups: ${steps.group_recommendation.output}
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json_mode: true
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temperature: 0.5
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max_tokens: 2000
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outputs:
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statistics: ${steps.parse_data.output}
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weakness_analysis: ${steps.identify_weakness.output}
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group_recommendation: ${steps.group_recommendation.output}
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report: ${steps.generate_report.output}
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on_error: stop
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timeout_secs: 240
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