# Sgarcese Civic Analytics Agent Workflow Claude Skill Civic Analytics Agent Workflow Claude Skill > City Policy Analysis — Master Orchestrator - Skill: `tomevault-io/sgarcese-civic-analytics-agent-workflow-claude-skill-civic-a` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/sgarcese-civic-analytics-agent-workflow-claude-skill-civic-a` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/sgarcese-civic-analytics-agent-workflow-claude-skill-civic-a/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Data & Analytics - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/sgarcese-civic-analytics-agent-workflow-claude-skill-civic-a --- # 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` --- > Source: [sgarcese/Civic-Analytics-Agent-Workflow-Claude-Skill](https://github.com/sgarcese/Civic-Analytics-Agent-Workflow-Claude-Skill) — distributed by [TomeVault](https://tomevault.io).