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earnings-preview

Generate pre-earnings previews from forecast data, KPI momentum, and call questions.

488|76|Updated Jun 2, 2026
Search Tags:#dashboard#deterministic#scenario#earnings#data-validation#pre-earnings#call-questions
One-click install
npx skills add https://github.com/openai/role-specific-plugins --skill earnings-preview-openai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: earnings-preview
Source: https://github.com/openai/role-specific-plugins/tree/main/plugins/financial-markets/skills/earnings-preview
Command: npx skills add https://github.com/openai/role-specific-plugins --skill earnings-preview-openai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, openpyxl, PyYAML, python-dateutil, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the creation of comprehensive pre-earnings briefs by aggregating forecast data, KPI momentum, and narrative into a single, auditable document.

Core Features & Use Cases

  • Deterministic pre-earnings previews generated from local inputs, KPI packs, and model templates.
  • Produces executive summaries, KPI dashboards, scenario framing, and call-ready questions to structure investor interactions.
  • Exports include a ready-to-publish preview note and accompanying artifacts with time-stamped provenance.

Quick Start

Run the deterministic plan using the included sample plan to produce a ready-to-review earnings preview pack.

Frequently Asked Questions about earnings-preview

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

FAQPage Schema
How do I automate pre-earnings preview generation from forecast data and KPI momentum?▼

Automate pre-earnings previews by aggregating deterministic forecast data, KPI momentum, and call questions into a single source-backed brief with scenario framing and reproducible local-script execution.

What is included in a deterministic pre-earnings brief for investor relations?▼

A deterministic pre-earnings brief includes an executive summary, KPI dashboard, scenario framing, and call-ready questions, all generated from local inputs and model templates with time-stamped provenance.

Can I generate earnings call questions automatically from KPI packs and forecast data?▼

Yes, you can generate call-ready questions automatically by running the deterministic plan with local KPI packs and forecast inputs to structure investor interactions.

How do I validate pre-earnings data for quarterly earnings cycles using Python?▼

Validate pre-earnings data using included scaffolds for validation and governance, ensuring deterministic inputs and reproducible briefs across quarterly earnings cycles.

Does this pre-earnings preview tool work with pandas and numpy for financial data aggregation?▼

Yes, the tool leverages pandas, numpy, and openpyxl to aggregate financial forecast data and render dashboard artifacts with deterministic, front-matter driven outputs.

What's the best way to structure investor interactions before a quarterly earnings call?▼

Structure investor interactions by producing executive summaries and call-ready questions from aggregated forecast data, creating a clear stock-reaction framework for consistent quarterly briefs.

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