guidance-inventory

Name: guidance-inventory
Availability: InStock
Author: faisalanjum

Extract forward-looking financial guidance from SEC filings and earnings call transcripts into a graph database.

3|1|Updated Dec 2, 2024

Search Tags:#data extraction #XBRL #Neo4j #SEC filings #earnings call #financial guidance

Ready to run

npx skills add https://github.com/faisalanjum/EventTrader --skill guidance-inventory

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: guidance-inventory
Source: https://github.com/faisalanjum/EventTrader/tree/main/.claude/skills/guidance-inventory
Command: npx skills add https://github.com/faisalanjum/EventTrader --skill guidance-inventory

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill automates the extraction of forward-looking financial guidance from complex corporate documents like earnings call transcripts and SEC filings, transforming raw text into structured, actionable data.

Core Features & Use Cases

Comprehensive Guidance Extraction: Identifies and extracts specific financial metrics, ranges, and qualitative outlooks.
Multi-Source Analysis: Processes data from transcripts, 8-Ks, 10-Qs, and 10-Ks, prioritizing the richest sources.
Use Case: An analyst needs to track a company's revenue and EPS guidance for the upcoming quarter. This Skill can scan the latest earnings call transcript and SEC filings to find and present this information, along with its source and any qualifying conditions.

Quick Start

Use the guidance-inventory skill to extract all financial guidance from the attached transcript file 'AAPL_2025-01-30T17.00.txt'.

Frequently Asked Questions about guidance-inventory

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I extract financial guidance from SEC filings and earnings call transcripts?▼

Financial guidance extraction from SEC filings and transcripts is automated by identifying key metrics, periods, and values, then storing them as structured nodes in a graph database. It processes 8-K, 10-Q, 10-K filings, and transcripts to normalize raw text into actionable data.

Can I store extracted forward-looking financial metrics in a Neo4j graph database?▼

Yes, extracted forward-looking financial metrics are stored as structured nodes in a Neo4j graph database. This graph-native approach uses deterministic ID generation for idempotency, ensuring data consistency across multiple extraction runs.

Does this guidance extraction approach work with both qualitative outlooks and quantitative ranges?▼

Guidance extraction works with both qualitative outlooks and quantitative ranges. It identifies and normalizes specific financial metrics, target ranges, and conditional outlooks from corporate documents, capturing the full spectrum of forward-looking statements.

What is the best way to normalize financial guidance data from multiple corporate filings?▼

The best way to normalize financial guidance data is using a graph-native approach that prioritizes the richest data sources. It scans 8-K, 10-Q, 10-K filings, and earnings call transcripts, handling source prioritization with robust error handling to ensure accurate data normalization.

How do I handle errors and duplicate data when extracting guidance from corporate filings?▼

Error handling and duplicate data prevention are managed through robust error handling and deterministic ID generation for idempotency. This ensures that reprocessing the same SEC filings or transcripts yields consistent results without creating duplicate graph nodes.

When do I need to extract XBRL data from SEC filings for financial guidance analysis?▼

Extracting XBRL data from SEC filings is needed when analyzing forward-looking financial guidance across 8-K, 10-Q, and 10-K reports. It transforms complex structured corporate documents into normalized metrics, periods, and values for actionable analysis.