# Sheet Show > Turn an Excel report (.xlsx) into one self-contained interactive HTML briefing leadership will actually read: KPI strip with caveats, faceted lenses (themes, origin, sector × type, cheque size), searchable entity cards, a detail drawer per entity with its records, and a co-investor network — offline, one file, no libraries. Built from an IB buyer-universe / transaction-screening report; fits any entity ↔ record workbook (investors ↔ deals, customers ↔ orders, peers ↔ data points). ALWAYS use when the user wants an Excel report made "readable", "presentable", "visual", "easy to look up", "leadership-friendly", wants "quick insight" or to "see the connections", or asks to convert a spreadsheet / deal list into HTML, a web page, a briefing, a one-pager or a dashboard for a boss, MD, partner, IC or client — even without saying "HTML" — and whenever a Buyer Universe, transaction screening or precedent-transactions workbook must reach someone who won't open Excel. - Skill: `felixx1502/sheet-show` (Agent Skill, multi-file: 5 files) - Install (CLI): `npx skillmds@latest add felixx1502/sheet-show` - Raw SKILL.md: https://api.skillmd.com/api/skills/felixx1502/sheet-show/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Web & Frontend - Author: Felixx1502 (https://skillmd.com/u/felixx1502) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/felixx1502/sheet-show --- # sheet-show Excel is where the analysis happens; it is not where it gets read. Leadership wants the three numbers, the slice they care about, and one click to the detail — not thirteen tabs. This skill produces that page from the workbook, and keeps every number on it traceable back to the sheet the boss already has. ## What you produce One `.html` file (≈0.5 MB for ~300 records), no external assets, opens from disk. Layout: sticky header + search → KPI strip with a caveat → lens rail (themes / origins / sector×type / cheque bands) → active-filter chips → card grid of entities → footer with source and join rate and the author's name. Clicking a card opens a drawer: stats, every record with expandable detail, vehicles used, co-investor ring graph. Filters AND across lenses and the counts in each lens are computed with that lens held out, so the page answers "what if I also pick…" before the reader clicks. See `references/design-system.md` for why each of those exists, and keep the reasons when you adapt it. ## Workflow ### 1. Map the workbook before touching code ```bash pip install openpyxl python scripts/inspect_xlsx.py "Report.xlsx" # whole workbook python scripts/inspect_xlsx.py "Report.xlsx" -s "Sheet" # one sheet, more rows ``` Read the output with these questions: - **Which sheet is the final, reconciled summary?** It usually has the report's own name (here `Buyer Universe`) and a totals row. Build from final sheets, never from raw pulls — a number leadership can't find in the workbook is a number they can't trust. - **What is the lookup unit?** The thing the reader will want to click. It becomes the *entity* (cards). The thing that happens to it becomes the *record* (drawer rows). Transaction reports: buyer ↔ deal. Pipelines: target ↔ touchpoint. Comps: peer ↔ data point. - **Which columns are facets?** The inspector marks low-cardinality text as `FACET`. Pick the 3–4 that carry the analyst's argument (class, geography, sector, size band). More than four lenses is a dashboard. - **Where is the narrative?** A commentary table (themes, theses, screen outcomes, "what the investor is buying") is the most valuable lens on the page because it is the analyst's reading, not a column. Find it. - **How do entities and records join?** Look for a bridge/alias sheet, a "rolled-up name" column, or a semicolon-separated buyers column. Note anything non-unique (ids like "Google search", duplicate target-years). Write the mapping down in one paragraph before coding. If the lookup unit is genuinely ambiguous (two equally plausible entities), ask — everything else is a judgment call you make. ### 2. Build `data.json` The template consumes a fixed shape — `references/data-model.md` — entities (`buyers`), records (`deals`), `themes`, `origins`, `meta`, `rec`. Keep the keys; change the labels. Start from `scripts/build_buyer_universe.py`, the builder for the Growth Equity Transaction Screening report, and adapt it: ```bash python scripts/build_buyer_universe.py "Report v3.xlsx" -o data.json --author "Vỹ Mạc" ``` For a sibling version of the same report (v4, next quarter) that usually means editing `SHEETS` / the `col(...)` candidates / `THEME_RULES` at the top. For a different report, keep the helpers (`find_table` locates a table by its header text so positions may move; `col` tolerates header rewording; `s`/`num`/`split_names` normalise the `-`/`n/a`/`;` conventions) and rewrite `build()`. `references/worked-example.md` explains each join and why it is ordered the way it is. Rules that make the page trustworthy: - **Entities come from the entity sheet only.** Never invent one from a record's buyer string — that is how counts drift from the workbook. - **Join by the most specific key that is actually unique**, fall back to `(name, year)`, and count every miss. Misses go in `rec` and in the footer. - **Narrative tagging is code.** If themes are attached by rule, the rules live in `THEME_RULES`, one per theme, and a residual theme catches the rest so no record is invisible from the themes lens. - **`value` is a number or `null`.** Undisclosed is `null`, never 0 or "n/a". - **Say what an aggregate means.** If you sum round sizes across co-investors (the template does), the `kpiNote` caveat must say it is round size, not capital deployed. Write the caveat for whatever your aggregate is. ### 3. Render and reconcile ```bash python scripts/render.py data.json -o "Buyer Universe.html" ``` `render.py` validates the invariants (ids = positions, mirrored entity↔record links, numeric values) and prints the reconciliation block. Every line should tie to the sheet. If one doesn't, fix the builder — do not ship a page whose footer says 325 when the sheet says 326 and hope nobody compares. ### 4. Open it and click three things Open the HTML in a browser (headless Chrome works for a screenshot: `chrome --headless=new --screenshot=out.png --window-size=1400,1100 file:///…`). Check: KPI numbers match the reconciliation; each lens renders and its counts change when another lens is filtered; one card opens the drawer; one record expands; search finds a target name; the footer sentence reads correctly; the page works at 560px width. Then look at it as the boss would — is the first screen the answer to "so what"? ### 5. Adapting the UI (only when the data model isn't enough) Edit `assets/template.html`. It is ~860 lines: CSS tokens → components → responsive → `