In the domain of financial operations, maintaining accurate and up-to-date structured data is a constant requirement. The data-research AI agent skill simplifies this by automating the extraction of structured data from a variety of sources. This tool is designed for anyone who needs to consistently pull specific pieces of information from emails, web pages, or APIs and centralize it. It populates canonical 'tracker' pages with this collected data.The skill operates through a single, versatile pipeline that accommodates numerous use cases, ranging from tracking investor updates to managing expense records and monitoring key company metrics. The specific behavior for each use case is determined by a simple YAML recipe configuration, which defines what to look for and how to extract it. This approach allows for broad applicability without requiring a custom solution for every data collection need.
How data-research Operates
The operational flow of the skill follows a precise seven-phase process, ensuring systematic and reliable data collection:1. Recipe Definition: The first step involves defining a research recipe. This YAML file outlines specific queries and extraction rules that guide the agent. It specifies the data points to identify and how to pull them from the source material.
Source Search: Once a recipe is in place, the skill searches for relevant information across various designated sources. This includes internal 'brain' files, incoming emails, specified web pages, and connected APIs. It uses internal
searchcapabilities, externalweb search, and anemail credential gatewayto access these diverse locations.Result Classification: Found results are then classified. The skill primarily uses regex patterns to identify relevant content. For more complex cases or when regex patterns are insufficient, it can fall back to an LLM for classification.
Structured Field Extraction and Archiving: With classified results, the skill proceeds to extract the structured fields defined in the recipe. Immediately after extraction, the raw sources are archived. The
put_raw_datatool facilitates this archival.Storage and Redirection: The original source materials are stored, and corresponding redirect pointers are created. This ensures data integrity and traceability, allowing easy access back to the original content via
put_page.Deduplication: Before updating any trackers, the extracted data undergoes a deduplication process. This involves both exact and fuzzy matching to prevent redundant entries in the tracker pages.
Tracker Page Update: Finally, the extracted and deduplicated data is used to update the canonical tracker pages. Backlinks to the original source entities are automatically generated, enhancing data context and navigability. This phase utilizes
put_page,add_link, and usesentity enrichment for backlinks.
Building a
Tracker and Practical ExamplesInitiating a data collection run with the tool is straightforward. You can trigger it using various commands such as 'research', 'track', 'extract from email', or 'build a tracker'.Consider a practical scenario like managing project expenses. Instead of manually reviewing receipts and updating spreadsheets, you can configure a data-research recipe. This recipe would specify patterns to look for in emails from vendors or financial institutions, or even data accessible via an accounting API. For instance, it might look for expense amounts, vendor names, transaction dates, and expense categories. When new emails arrive or the skill is triggered to 'track expenses from email', it identifies relevant messages, extracts these data points, and then automatically populates a central 'Expense Tracker' page. Each entry on this tracker would include a backlink directly to the original email or receipt, providing immediate context and verification. This automated process ensures your expense records are always current and auditable without manual intervention.Beyond expense tracking, the tool is equally effective for monitoring investor updates, aggregating key company metrics from quarterly reports, or compiling market data from specific web sources. The flexibility of the YAML recipe system means that as your data collection needs evolve, you can simply adjust the configuration without redesigning the entire pipeline.
Essential
Tools and Practical LimitationsTo perform its functions, the skill integrates with several core tools and components. These include search and query for information retrieval, get_page and put_page for managing internal pages, add_link for creating navigational connections, and add_timeline_entry for logging events. For raw data handling, put_raw_data and file_upload are available. The declarative power comes from YAML recipe files, and connectivity is provided via an email credential gateway and standard web search capabilities. Entity enrichment for backlinks helps establish robust data relationships.It's important to understand the specific scope of this tool. This is explicitly not for unstructured narrative research or for executing one-off, ad-hoc queries. The skill is purpose-built for scenarios where there are defined extraction schemas and a clear assumption of recurring data collection. It excels when you need consistent, structured data over time, not for exploratory or qualitative information gathering.
Frequently Asked Questions* **Q:
What kind of data can data-research extract?** * A: It extracts structured data from emails, web sources, and APIs, based on defined extraction rules configured within YAML recipes.* Q: Is data-research suitable for ad-hoc research? * A: No, it is designed for recurring data collection where extraction schemas are defined, not for one-off queries or unstructured narrative research.* Q: How do I initiate a data collection process? * A: You can trigger it using commands like 'research', 'track', 'extract from email', or 'build a tracker'.The skill offers a robust method for maintaining updated structured data without extensive manual effort. Configure your recipes, define your trackers, and let the agent manage the routine collection process.
