changhochien IB Deck Checker
Perform comprehensive QC on the presentation across four dimensions. Read every slide, then report findings.
Environment check
This skill works in both the PowerPoint add-in and chat. Identify which you're in before starting:
- Add-in — read from the live open deck.
- Chat — read from the uploaded
.pptxfile.
This is read-and-report only — no edits — so the workflow is identical in both.
Workflow
Read the deck
Pull text from every slide, keeping track of which slide each line came from. You'll need slide-level attribution for every finding ("$500M appears on slides 3 and 8, but slide 15 shows $485M"). A deck with 30 slides is too much to hold in working memory reliably — write the extracted text to a file so the number-checking script can process it.
The script expects markdown-ish input with slide markers. Format as:
## Slide 1
[slide 1 text content]
## Slide 2
[slide 2 text content]
1. Number consistency
Run the extraction script on what you collected:
python scripts/extract_numbers.py /tmp/deck_content.md --check
It normalizes units ($500M vs $500MM vs $500,000,000 → same number), categorizes values (revenue, EBITDA, multiples, margins), and flags when the same metric category shows conflicting values on different slides. This is the part most likely to catch something a human missed on the fifth read-through.
Beyond what the script flags, verify:
- Calculations are correct (totals sum, percentages add up, growth rates match the endpoints)
- Unit style is consistent — the deck should pick one of $M or $MM and stick with it
- Time periods are aligned — FY vs LTM vs quarterly, explicitly labeled
2. Data-narrative alignment
Map claims to the data that's supposed to support them. This is where decks go wrong quietly — someone edits the chart on slide 7 and forgets the narrative on slide 4.
- Trend statements ("declining margins") → does the chart actually go that direction?
- Market position claims ("#1 player") → revenue and share data support it?
- Plausibility — "#1 in a $100B market" with $200M revenue is 0.2% share; that's not #1
3. Language polish
IB decks have a register. Scan for anything that breaks it: casual phrasing ("pretty good", "a lot of"), contractions, exclamation points, vague quantifiers without numbers, inconsistent terminology for the same concept.
See references/ib-terminology.md for replacement patterns.
4. Visual and formatting QC
Run standard visual verification checks on each slide. You're looking for: missing chart source citations, missing axis labels, typography inconsistencies, number formatting drift (1,000 vs 1K within the same deck), date format drift, footnote and disclaimer gaps.
Visual verification catches overlaps, overflow, and contrast issues that don't show up in text extraction. Don't skip it — a chart with no source citation looks the same as a properly sourced one in the text dump.
Output
Use references/report-format.md as the structure. Categorize by severity:
- Critical — number mismatches, factual errors, data contradicting narrative. These block client delivery.
- Important — language, missing sources, terminology drift. Should fix.
- Minor — font sizes, spacing, date formats. Polish.
Lead with criticals. If there aren't any, say so explicitly — "no number inconsistencies found" is a finding, not an absence of one.
Hermes Profile Notes
This skill was packaged from pi-financial-services source path plugins/vertical-plugins/financial-analysis/skills/ib-check-deck for the Hermes financial-services profile. Use institutional data connectors first when available, cite sources, and stage outputs for qualified human review.
Common Pitfalls
- Do not present drafts as investment, legal, tax, or accounting advice.
- Do not use web search as the primary source when an institutional MCP/data connector is available.
- Do not execute transactions, contact clients, post to a ledger, or approve onboarding. Stage outputs for review.
Verification Checklist
- Inputs, assumptions, dates, and currencies are explicit.
- Numbers tie across tables, models, decks, and memos.
- Sources are cited and institutional data is preferred where available.
- Output is labeled draft / for human review where appropriate.