Reconcile financial metrics and validate narrative alignment in markdown pitch decks.
npx skills add https://github.com/smrik/ai-fund --skill check-deck-smrik
Please help me install this Agent Skill. Skill: check-deck Source: https://github.com/smrik/ai-fund/tree/main/skills/financial-analysis/skills/check-deck Command: npx skills add https://github.com/smrik/ai-fund --skill check-deck-smrik
This skill addresses the high-stakes risk of errors in investment banking pitch decks, ensuring that numerical data, narrative claims, and formatting meet rigorous industry standards before client delivery.
Run the check-deck skill to perform a comprehensive quality control review on the presentation content file named presentation-content.md.
High-intent search queries and answers about installing and using this skill.
Automate quality control for investment banking pitch decks by running a Python-based extraction on presentation markdown to reconcile financial metrics, verify narrative-data alignment, and enforce professional formatting standards.
Narrative-data alignment validation is the process of verifying that qualitative claims in a pitch deck are directly supported by the underlying quantitative financial data extracted from the presentation markdown.
Check for numerical discrepancies across multiple slides by systematically comparing financial metrics extracted from presentation markdown to identify and prevent calculation or data-entry errors in pitch decks.
Yes, you can flag casual language and inconsistent formatting in client-ready materials by enforcing professional investment banking terminology and consistent visual standards during the automated QC review.
Yes, Python is required to extract markdown content from the presentation file, as the automated quality control process relies on Python-based extraction to parse and audit the financial metrics.
The best way to reconcile financial data before client delivery is to perform an automated audit that cross-references numerical metrics across slides and validates narrative claims against the underlying quantitative data.
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