用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/pluginagentmarketplace/custom-plugin-software-architect --skill data-architecture命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
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| name | data-architecture |
| description | Design data architectures with modeling, pipelines, and governance |
| version | 2.0.0 |
| sasmp_version | 1.3.0 |
| bonded_agent | 06-data-architecture |
| bond_type | PRIMARY_BOND |
| last_updated | 2025-01 |
Design data architectures including data models, pipeline designs, governance frameworks, and quality management for operational and analytical systems.
| Parameter | Type | Required | Validation | Default |
|---|---|---|---|---|
data_domain | string | ✅ | min: 20 chars | - |
design_type | enum | ⚪ | model|pipeline|governance|quality | model |
data_type | enum | ⚪ | operational|analytical|streaming | operational |
volume_tier | enum | ⚪ | small|medium|large|massive | medium |
output_format | enum | ⚪ | erd|yaml|json | erd |
┌──────────────────────────────────────────────────────────┐
│ 1. VALIDATE: Check data domain and requirements │
│ 2. DISCOVER: Identify data sources and entities │
│ 3. MODEL: Create conceptual/logical/physical model │
│ 4. DESIGN: Pipeline or governance framework │
│ 5. QUALITY: Define data quality rules │
│ 6. VALIDATE: Check model consistency │
│ 7. DOCUMENT: Return data architecture │
└──────────────────────────────────────────────────────────┘
| Error | Retry | Backoff | Max Attempts |
|---|---|---|---|
VALIDATION_ERROR | No | - | 1 |
MODEL_GENERATION_ERROR | Yes | 1s | 2 |
FORMAT_ERROR | Yes | 500ms | 3 |
log_points:
- event: design_started
level: info
data: [design_type, data_type]
- event: entities_identified
level: info
data: [entity_count, relationship_count]
- event: quality_rules_defined
level: info
data: [rule_count, dimensions_covered]
metrics:
- name: models_created
type: counter
labels: [design_type]
- name: design_time_ms
type: histogram
- name: entity_count
type: gauge
| Error Code | Description | Recovery |
|---|---|---|
E401 | Missing data domain | Request domain description |
E402 | Invalid relationships | Highlight circular/missing refs |
E403 | Schema validation failed | Show validation errors |
E404 | Unsupported volume tier | Suggest architectural changes |
test_cases:
- name: "E-commerce data model"
input:
data_domain: "E-commerce order management"
design_type: "model"
output_format: "erd"
expected:
has_entities: true
entities_include: ["Customer", "Order", "Product"]
has_relationships: true
valid_erd: true
- name: "Analytics pipeline"
input:
data_domain: "Customer analytics"
design_type: "pipeline"
data_type: "analytical"
expected:
has_ingestion: true
has_transformation: true
has_serving: true
- name: "Data quality rules"
input:
data_domain: "User profiles"
design_type: "quality"
expected:
has_dimensions: true
dimensions_include: ["completeness", "accuracy"]
| Symptom | Root Cause | Resolution |
|---|---|---|
| Missing relationships | Incomplete domain | Add missing entities |
| Invalid ERD syntax | Format error | Validate Mermaid ERD |
| Missing quality rules | Dimensions not specified | Add quality dimensions |
□ Is data domain clearly defined?
□ Are all entities identified?
□ Are relationships correctly typed?
□ Is output format valid?
□ Are quality dimensions covered?
| Dimension | Example Rule |
|---|---|
| Completeness | NOT NULL checks |
| Accuracy | Regex validation |
| Consistency | Referential integrity |
| Timeliness | SLA monitoring |
| Uniqueness | Primary key constraints |
| Component | Trigger | Data Flow |
|---|---|---|
| Agent 06 | Design request | Receives domain, returns model |
| Agent 04 | Cloud data services | Cloud data platform |
| Version | Date | Changes |
|---|---|---|
| 2.0.0 | 2025-01 | Production-grade: ERD, pipelines, DQ framework |
| 1.0.0 | 2024-12 | Initial release |
Make and document architecture decisions using structured frameworks
Create and maintain architecture documentation using standard formats
Design cloud-native architectures with service selection and cost optimization
基于 SOC 职业分类