city-analysis-workflow

分类: 工具与效率 | 上传者: sgarcese | 下载: 0 | 版本: v1.0（最新）

掌握波士顿市政策分析和公民创新的工作流程技能。对于涉及波士顿城市数据、城市服务、社区公平、公共政策、政府绩效、311 分析、住房、安全、交通或任何公民问题的任何请求，请始终使用此技能 - 即使用户没有明确要求“全面分析”。该技能协调了五个子技能：城市问题框架（受彭博社启发）、城市政策分析（受 J-PAL 启发）、城市沟通（受 GovLab/InnovateUS 启发）、城市基准测试（使用旧金山、西雅图和华盛顿数据的跨城市比较）和城市绩效管理（美国结果/PerformanceStat）。将此技能用于：“全面分析”、“政策简报”、“数据驱动的建议”、“城市改善项目”、“调查[问题]”、“将波士顿与其他城市进行比较”、“数据显示什么”、“帮我写一份备忘录”，或出于政府或公民目的而结合问题定义、数据分析和沟通的任何请求。

更新日志: Source: GitHub https://github.com/sgarcese/Civic-Analytics-Agent-Workflow-Claude-Skill

目录结构

当前层级: tree/main/

📄 .gitignore 258 B
📄 Analytical_Skill.md 9.2 KB
📄 Benchmarking_Skill.md 22.1 KB
📄 CHECKLISTS.md 8.9 KB
📄 Communication_Skill.md 9.7 KB
📄 EXAMPLE-311-equity.md 11.0 KB
📄 index.html 40.4 KB
📄 LICENSE 1.0 KB
📄 Performance_Management_Skill.md 14.5 KB
📄 Problem_Framing_Skill.md 8.0 KB
📄 PROMPTS.md 10.6 KB
📄 README.md 8.8 KB
📄 REFERENCE.md 13.7 KB
📄 SKILL.md 7.2 KB
📄 TEMPLATES.md 14.6 KB

SKILL.md

---
name: city-analysis-workflow
description: "Master workflow skill for City of Boston policy analysis and civic innovation. ALWAYS use this skill for any request involving Boston city data, city services, neighborhood equity, public policy, government performance, 311 analysis, housing, safety, transportation, or any civic issue — even if the user hasn't explicitly asked for a 'full analysis'. This skill orchestrates five sub-skills: city-problem-framing (Bloomberg-inspired), city-policy-analysis (J-PAL-inspired), city-communication (GovLab/InnovateUS-inspired), city-benchmarking (cross-city comparison using San Francisco, Seattle, and DC data), and city-performance-management (Results for America / PerformanceStat). Use this skill for: 'full analysis', 'policy brief', 'data-driven recommendation', 'city improvement project', 'investigate [issue]', 'compare Boston to other cities', 'what does the data show', 'help me write a memo about', or any request that combines problem definition, data analysis, and communication for government or civic purposes."
---

# City Policy Analysis — Master Orchestrator

## Four-Phase Integrated Framework

| Phase | Source Methodology | When to Use | Reference File |
|-------|-------------------|-------------|----------------|
| **1. FRAME** | Bloomberg Center for Public Innovation (JHU) | Problem is undefined or needs scoping | `Problem_Framing_Skill.md` |
| **2. ANALYZE** | J-PAL, MIT — Evidence-to-Policy | Running numbers, finding patterns, equity analysis | `Analytical_Skill.md` |
| **3. COMMUNICATE** | The GovLab (NYU) / InnovateUS | Writing memos, briefs, dashboards, community reports | `Communication_Skill.md` |
| **4. BENCHMARK** | Cross-city comparison using Boston + San Francisco + Seattle + DC data | Comparing Boston to peer cities, learning from elsewhere | `Benchmarking_Skill.md` |
| **5. PERFORM** | Results for America / PerformanceStat (CitiStat) | Budget × staffing × service outcomes: cost-per-outcome, workload-per-FTE, efficiency trends | `Performance_Management_Skill.md` |

> **Always read the relevant sub-skill file before beginning each phase.**

---

## Quick Decision Router

```
User Request
│
├─ "What data does Boston have on..." / "Help me define the problem"
│   → Read Problem_Framing_Skill.md → Run Phase 1
│
├─ "Analyze / run the numbers / what does the data show / is there an equity issue"
│   → Read Analytical_Skill.md → Run Phase 2
│
├─ "Write a memo / create a brief / make a dashboard / present these findings"
│   → Read Communication_Skill.md → Run Phase 3
│
├─ "Compare Boston to other cities / how does Boston rank / what works elsewhere"
│   → Read Benchmarking_Skill.md → Run Phase 4
│
├─ "Budget vs. performance / cost per outcome / workload per FTE / are we getting results / staffing efficiency / how much does it cost to / is the department understaffed / overtime analysis"
│   → Read Performance_Management_Skill.md → Run Phase 5
│
└─ "Full analysis / investigate / give me a recommendation / policy project"
    → Run all relevant phases in sequence
```

---

## MCP Tool Reference — All Three Cities

### Boston Open Data (Primary)
```
search_datasets(query)           → Discover datasets by topic
get_dataset_info(dataset_id)     → Find resource IDs and metadata
get_datastore_schema(resource_id)→ Get exact field names before querying
query_datastore(resource_id, filters={}, sort="", limit=100, date_range={})
```

### San Francisco Open Data (Benchmarking — Socrata)
```
San Francisco Open Data:socrata__search_datasets(query)
San Francisco Open Data:socrata__get_dataset(dataset_id)
San Francisco Open Data:socrata__get_schema(resource_id)
San Francisco Open Data:socrata__query_dataset(resource_id, ...)
San Francisco Open Data:socrata__execute_sql(soql_query)
```

### Seattle Open Data (Benchmarking — Socrata)
```
Seattle Open Data:socrata__search_datasets(query)
Seattle Open Data:socrata__get_dataset(dataset_id)
Seattle Open Data:socrata__get_schema(resource_id)
Seattle Open Data:socrata__query_dataset(resource_id, ...)
Seattle Open Data:socrata__execute_sql(soql_query)
```

### DC Open Data (Benchmarking — ArcGIS)
```
DC Open Data:arcgis__search_datasets(query)
DC Open Data:arcgis__get_dataset(dataset_id)
DC Open Data:arcgis__query_data(dataset_id, ...)
DC Open Data:arcgis__get_aggregations(dataset_id, ...)
```

**⚠️ ALWAYS confirm field names via schema before querying any dataset in any city.**

---

## Standard MCP Sequence (All Cities)
```
1. search_datasets("topic")         → find dataset IDs
2. get_dataset_info("dataset-id")   → find queryable resource IDs
3. get_datastore_schema(resource_id)→ confirm EXACT field names
4. query_datastore(resource_id, ...) → retrieve records
```

---

## Boston 311 Schema Cheat Sheet (Critical)

The 311 system changed in October 2025. Field names differ:

| Concept | Legacy (2011–Oct 2025) | New System (Oct 2025+) |
|---------|----------------------|----------------------|
| Open date | `open_dt` | `open_date` |
| Close date | `closed_dt` | `close_date` |
| Service type | `type` | `service_name` |
| Department | `department` | `assigned_department` |
| Neighborhood | `neighborhood` | `neighborhood` (same) |
| On-time | `on_time` | `on_time` (same) |

Key resource IDs: `dff4d804-...` (2024), `9d7c2214-...` (Jan–Oct 2025), `254adca6-...` (New System, Oct 2025+)

---

## Cross-Phase Quality Standards

### Rigor (J-PAL): Every claim is grounded in data or clearly labeled as interpretation. Confidence level stated. Limitations named, not buried.

### Human-Centeredness (Bloomberg): Problem framed around people's lived experience. Recommendations are implementable by real city staff.

### Inclusivity (GovLab): Equity lens applied. Plain-language versions exist. Feedback mechanisms included.

### Transparency: Data sources cited with IDs. Methodology reproducible. Findings shareable as open knowledge.

---

## Supporting Files in This Skill Set

| File | Purpose |
|------|---------|
| `Problem_Framing_Skill.md` | Bloomberg methodology: scope, stakeholders, assumptions |
| `Analytical_Skill.md` | J-PAL methodology: descriptive → diagnostic → equity |
| `Communication_Skill.md` | GovLab/InnovateUS: memos, briefs, dashboards, engagement |
| `Benchmarking_Skill.md` | Cross-city comparison using San Francisco, Seattle, and DC data; includes Performance Management Benchmarking module |
| `TEMPLATES.md` | Fill-in-the-blank templates for 6 output types |
| `CHECKLISTS.md` | Pre-flight and review checklists for all phases |
| `PROMPTS.md` | Example prompts organized by phase and complexity |
| `REFERENCE.md` | Boston dataset directory, field names, cross-referencing |
| `Performance_Management_Skill.md` | Results for America / PerformanceStat: budget × staffing × outcomes efficiency analysis |
| `EXAMPLE-311-equity.md` | Complete worked example: 311 response equity analysis |

---

## When Creating Documents
- Word docs (.docx): Also read `/mnt/skills/public/docx/SKILL.md`
- Presentations (.pptx): Also read `/mnt/skills/public/pptx/SKILL.md`
- Spreadsheets (.xlsx): Also read `/mnt/skills/public/xlsx/SKILL.md`
- Dashboards (React/HTML): Also read `/mnt/skills/public/frontend-design/SKILL.md`

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city-analysis-workflow

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