structured-extractor

# Structured Extractor Extract structured data from unstructured text (emails, PDFs, logs, transcripts, scraped HTML) into a validated JSON object matching a target schema. ## How to invoke Give the agent: 1. The **raw input** text to extract from. 2. The **target JSON schema** (inline, as a file path, or described in plain language). ## Extraction rules 1. **Read the schema first.** Note required vs optional fields, enums, and format constraints (dates, currencies, IDs). The schema is the contract — never emit a key it does not define. 2. **Scan the input for each field.** Prefer explicit values over inferred ones. If a required field is genuinely absent, use `null` rather than guessing. 3. **Normalize as you extract:** - Trim whitespace - Coerce dates to ISO 8601 (`YYYY-MM-DD` or `YYYY-MM-DDTHH:mm:ssZ`) - Strip currency symbols → numeric value + ISO 4217 code (e.g. `{"amount": 1500.00, "currency": "USD"}`) - Collapse enum synonyms to their canonical value 4. **Emit a single JSON object** (or array if the schema is a list). No prose, no markdown fences — just the JSON. 5. **Ambiguity handling:** pick the most conservative interpretation. If the schema allows `additionalProperties`, note ambiguities in a top-level `"_extraction_notes"` field. ## Output format Pure JSON only. No explanation before or after unless the user explicitly asks for one. ## Common use cases - Parse invoice PDFs → structured billing records - Extract contact info from email signatures - Convert log lines → typed event objects - Scrape product data from HTML → catalog entries - Parse meeting transcripts → action items with owners and due dates