ib-check-deck

Detect hidden errors in investment banking client-facing presentations.

31|4|Updated Jun 13, 2026
One-click install
npx skills add https://github.com/r9412460971-cloud/OPC-skill --skill ib-check-deck-r9412460971-cloud
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ib-check-deck
Source: https://github.com/r9412460971-cloud/OPC-skill/tree/main/skills/ib-check-deck
Command: npx skills add https://github.com/r9412460971-cloud/OPC-skill --skill ib-check-deck-r9412460971-cloud

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Investment banking pitch decks and client-ready presentations often contain hidden errors that slip through manual reviews: mismatched financial figures across slides, claims that contradict underlying data, unprofessional casual language, and formatting inconsistencies. These issues can damage client trust, lead to factual inaccuracies, and delay deal workflows.

Core Features & Use Cases

  • Cross-slide number consistency checking: Automatically detects mismatched financial figures (e.g., $500M revenue on one slide vs $485M on another) across all deck slides, normalizing units and categorizing values for accurate comparison.
  • Data-narrative alignment validation: Verifies that claims like "#1 market player" or "declining margins" are supported by the actual data presented in charts and tables.
  • IB-standard language polish: Flags casual phrasing, contractions, vague quantifiers, and inconsistent terminology, with reference patterns for professional investment banking wording.
  • Visual and formatting QC: Identifies missing chart sources, inconsistent number/date formats, typography issues, and other visual gaps that don't appear in text extraction. Use Case: An investment banker preparing a client pitch deck can use this skill to catch a $200M revenue figure that is mislabeled as $220M on a summary slide, and replace casual phrasing like "pretty good margins" with IB-standard language like "attractive margins of 22%".

Quick Start

Use the ib-check-deck skill to run a full quality check on your uploaded investment banking pitch deck and get a structured report of all critical, important, and minor issues before sending it to a client.

Frequently Asked Questions about ib-check-deck

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

FAQPage Schema
How do I check number consistency across pitch deck slides?

You can check number consistency across pitch deck slides by using a Python number extraction script to normalize and cross-reference financial figures, categorizing values for accurate comparison and detecting mismatches.

What is data-narrative alignment validation for investment banking presentations?

Data-narrative alignment validation verifies that qualitative claims like declining margins are supported by actual data presented in charts and tables, ensuring client-facing presentations remain factually accurate.

How do I polish investment banking language in a client presentation?

You can polish investment banking language by flagging casual phrasing, contractions, and vague quantifiers, then replacing them with professional IB-standard wording using reference patterns for terminology.

Can I run a quality check on a pitch deck before client delivery without manual review?

Yes, you can run a full quality check on uploaded pitch decks to automatically identify visual formatting issues, missing chart sources, and number inconsistencies, generating a severity-categorized findings report.

Does the pitch deck quality check support transaction marketing materials and client updates?

Yes, the pre-delivery deck review workflow applies to all client-facing presentations including pitch decks, client updates, and transaction marketing materials to resolve critical quality gaps.

What is the best way to identify formatting inconsistencies in pitch decks?

The best way to identify formatting inconsistencies is running visual quality control that detects missing chart sources, inconsistent number formats, and typography issues that standard text extraction misses.