Structured Data Extraction & Audit Workflows
A progression of prompts that transform unstructured text into auditable tables, strictly separating observation from interpretation.
Structured Data Extraction & Audit Workflows is a hand-picked TokRepo arsenal pack — 3 AI assets bundled together so you can install the whole stack with one command. Each asset has been verified live (status=public, current download), tagged with its primary type, and curated to work alongside the others in this pack.
To install, run `tokrepo install pack/tokrepo-editorial-data-analysis-4f0acbda28ab` in your terminal — or hand the same command to your AI agent (Claude Code, Cursor, Codex CLI, Gemini CLI, GitHub Copilot, Cline, Roo Code, Windsurf are all supported). The TokRepo Node CLI fetches each asset from the TokRepo registry, places skill files into `.claude/skills/`, prompts into your prompt directory, MCP servers into your client config, and surfaces a summary of what was installed.
Without a pack, you would need to discover, vet, and install each of these 3 assets one by one — typically 30 to 60 minutes per asset. The pack collapses that into a single command, and stays current as the underlying assets get re-published. If any asset goes private or is removed, the pack auto-detects it and skips that entry on next install — no broken bundle.
How to use this collection
This collection guides you through a rigorous data extraction workflow using three distinct prompt templates. It is designed for researchers and analysts who need to convert messy text into structured, verifiable data without hallucinating facts. **Step 1: Scientific Metric Extraction** Use the **Scientific Data Extractor Prompt** (UUID: c7226f4e...) to process academic abstracts. This tool forces the AI to extract raw numbers, units, and p-values into a Markdown table. *Prerequisite:* A scientific abstract containing numerical data. *Limitation:* It does not interpret statistical significance; it only records what is written. If the abstract lacks numbers, it will state so explicitly. **Step 2: Financial Date Auditing** Use the **Utility Bill Date Extractor** (UUID: 0f1c93cc...) to handle financial or scheduling text. This prompt builds a due-date table but strictly flags ambiguous dates (e.g., '07/22/2025') and forbids any payment actions. *Prerequisite:* Text copied from bills or invoices. *Limitation:* It cannot calculate dates based on terms like 'due upon receipt' unless the specific calendar date is present. It serves as a review list, not a payment scheduler. **Step 3: Evidence Indexing** Use the **Question-to-Source Index Builder** (UUID: 6a73a4e6...) to map research questions to exact source excerpts. This ensures your final report relies only on evidence actually present in your materials. *Prerequisite:* A list of numbered research questions and a reading list with pasted excerpts. *Limitation:* It does not browse the live web. It works solely on the text you paste, marking any gaps where sources do not answer your questions. **Workflow Summary:** Start with Step 1 to get raw metrics. Use Step 2 if you are dealing with administrative dates or financial constraints. Finish with Step 3 to ensure your conclusions are traceable to specific source texts. All outputs are draft tables requiring human verification before use in production reports.
3 assets in this pack
Why this asset is included：Extracts raw numerical metrics from scientific abstracts into structured tables, ensuring units and values are preserved without interpretation.
Why this asset is included：Audits utility bill text for due dates, flagging ambiguity and preventing unauthorized payment actions or date calculations.
Why this asset is included：Maps research questions to exact source excerpts, creating a traceable index that prevents inventing findings from missing data.
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