finance/financial-modeling/check-deck/SKILL.md
Investment banking presentation quality checker. Reviews pitch decks and client-ready presentations for: (1) Number consistency across slides, (2) Data-narrative alignment, (3) Language polish for IB standards, (4) Formatting QC. Use when asked to review, check, or QC any IB presentation, pitch deck, or client materials before delivery.
npx skillsauth add harsh040506/claude-code-unified-skill-plugin-library check-deckInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Perform comprehensive QC on investment banking presentations across four dimensions.
Extract presentation content before checking:
python -m markitdown presentation.pptx > content.md
For visual inspection, convert to images using the pptx skill workflow.
Extract numbers with slide references:
python scripts/extract_numbers.py content.md --check
Verify:
Flag pattern:
ISSUE: Revenue mismatch
- $500M on Slides 3, 8
- $485M on Slide 15 (DCF input)
ACTION: Reconcile figures
Map claims to supporting data:
Flag contradictions:
ISSUE: Narrative contradicts data
- Slide 4: "declining margins"
- Slide 7 chart: margins 18% → 22%
ACTION: Update narrative or verify data
Check plausibility (e.g., "#1 player in $100B market" with $200M revenue = 0.2% share).
Scan for:
See references/ib-terminology.md for replacement patterns.
Flag pattern:
ISSUE: Casual language (Slide 12)
- "This deal is a no-brainer"
→ "The transaction presents a compelling value proposition"
Audit each slide for:
Present findings using the template in references/report-format.md.
Categorize by severity:
testing
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tools
Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
testing
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development
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