fsi-fa-check-deck
Skill16 starsChanged 9 months agoApache-2.0View on GitHub
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.
In this repository99 other files it gives its agents.
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- The first 60 shown.
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 63
Check plausibility (e.g., "#1 player in $100B market" with $200M revenue = 0.2% share).
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
IB Deck Checker
Perform comprehensive QC on investment banking presentations across four dimensions.
Prerequisites
Extract presentation content before checking:
python -m markitdown presentation.pptx > content.mdFor visual inspection, convert to images using the pptx skill workflow.
Check Workflow
1. Number Consistency
Extract numbers with slide references:
python scripts/extract_numbers.py content.md --checkVerify:
- Key metrics match across all slides (revenue, EBITDA, multiples)
- Calculations are correct (totals, percentages, growth rates)
- Units consistent (same scale used: millions vs billions, % vs bps)
- Unit formatting consistent (e.g., $M vs $MM, $B vs $Bn - pick one style throughout)
- Time periods aligned (FY vs LTM vs quarterly)
Flag pattern:
ISSUE: Revenue mismatch
- $500M on Slides 3, 8
- $485M on Slide 15 (DCF input)
ACTION: Reconcile figures2. Data-Narrative Alignment
Map claims to supporting data:
- Trend statements → chart directions
- Market position claims → revenue/share data
- Factual assertions → verify accuracy
Flag contradictions:
ISSUE: Narrative contradicts data
- Slide 4: "declining margins"
- Slide 7 chart: margins 18% → 22%
ACTION: Update narrative or verify dataCheck plausibility (e.g., "#1 player in $100B market" with $200M revenue = 0.2% share).
3. Language Polish
Scan for:
- Casual phrasing ("pretty good", "a lot of")
- Vague quantifiers without specifics
- Contractions, exclamation points
- Inconsistent terminology
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"4. Formatting QC
Audit each slide for:
- Charts: Source citations, axis labels, legends
- Typography: Consistent fonts, size hierarchy
- Numbers: Consistent formatting (1,000 vs 1K)
- Dates: Consistent format throughout
- Footnotes: Proper sourcing and disclaimers
Output
Present findings using the template in references/report-format.md.
Categorize by severity:
- Critical: Number mismatches, factual errors
- Important: Language, narrative alignment
- Minor: Formatting inconsistencies
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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- It worked
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- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

