📋 Copy Blueprint Command { } Inspect JSON API Ground-Truth Source ↗

⚡ 1-Click Agent & Developer Install

Zero Auth · Ready for Grok Bot, Claude Code & Cursor

### 1. The Workflow Bottleneck & Problem Statement Analysts and investigative researchers spend 80% of their time cleaning CSV headers and date formats before they can run a single query. ### 2. Synthesized Agent Architecture & Connected Stack **Automated Public Dataset Ingestion & SQLite Investigative Analysis** (`datasette-sqlite-investigative-journal`) is a verified production blueprint curated from **@simonw**'s real-world implementation. - **Primary Capability:** Downloads messy CSV/JSON public filings, normalizes them into indexed SQLite tables with FTS5 search, and surfaces statistical outliers with reproducible SQL. - **Connected Tool Stack:** SQLite MCP, E2B Code Interpreter MCP, GitHub - **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval` - **Certified Builder Source:** [@simonw](https://github.com/modelcontextprotocol/servers) ### 3. Step-by-Step Implementation Blueprint 1. **Trigger & Data Ingestion:** Configure your agent routine to watch **SQLite MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences. 2. **Cross-System Enrichment & Verification:** Query **E2B Code Interpreter MCP** to validate live capacity, account status, or code state before generating recommendations. 3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **GitHub** with 1-click approve/edit controls so your team retains full oversight. ### 4. Copy-Paste System Prompt & Routine Configuration ```text Implement the 'Automated Public Dataset Ingestion & SQLite Investigative Analysis' workflow curated with @simonw. 1. OBJECTIVE: Downloads messy CSV/JSON public filings, normalizes them into indexed SQLite tables with FTS5 search, and surfaces statistical outliers with reproducible SQL. 2. CONNECTED TOOL STACK: SQLite MCP, E2B Code Interpreter MCP, GitHub 3. STEP-BY-STEP EXECUTION LOOP: - Step A (Ingest & Ground): Query live inputs from SQLite MCP and cross-reference state in E2B Code Interpreter MCP. - Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields. - Step C (Stage & Report): Prepare the structured output, draft replies/tickets in GitHub, and request human confirmation before executing any external send or production write. ``` ### 5. Measured ROI & Production Impact - **Verified Outcome:** Turns raw multi-gigabyte public datasets into queryable, citation-backed SQLite research briefs in minutes. - **Builder Portfolio:** [Explore all capabilities by @simonw →](/u/simonw)

Tags: #datasette-sqlite-investigative-journal#use-case#research#simonw#sqlite-mcp#e2b-code-interpreter-mcp

View All 16 Items by @simonw →

mcp · ⚡ 80

PostgreSQL Read/Write & Schema Introspection MCP Server

Connect AI agents directly to PostgreSQL databases for live table schema inspection, `EXPLAIN ANALYZE` query benchmarking, and safe read-only or transactio

mcp · ⚡ 93

SQLite & Datasette Local Analytics & Schema MCP Server

Query local SQLite databases, run full-text FTS5 searches, create analytical views, and synthesize structured data memos directly from your agent workspace

mcp · ⚡ 79

PostgreSQL Analytics & Read-Replica Inspector MCP (mcp-postgres)

Zero-mutation PostgreSQL read-replica MCP server enforcing `SET TRANSACTION READ ONLY` on every query for safe executive BI, cohort analysis, and schema ex