Documents

Documents

Six scripts. Every one runs under the interpreter bundled in this app, with the packages that are already there: reportlab, pypdf, openpyxl, Pillow and defusedxml, plus the standard library. Nothing here needs the network, a child process, or a package the user has to install. python-pptx, python-docx and lxml are bundled too (see Limits) for the things these writers do not do.

Read Limits before promising the user anything. Two of them change what you should say in your answer.

Choosing a script

A typical request — "analyse this spreadsheet and make me a deck" — is analyze_table.py to get the numbers, then make_pptx.py with those numbers in the spec. Do not put a number in a document that you did not compute.

Starting from a CSV. Three different requests, three different answers, and picking the wrong one wastes the turn:

• "Turn this CSV into a spreadsheet" — one command: make_xlsx.py --csv sales.csv --out sales.xlsx. Do not build a spec by hand for this, and do not reach for analyze_table.py: it profiles columns and refuses --out on a workbook without --group-by, because a profile is not a table.
• "Total the revenue in this CSV" — analyze_table.py, which computes it. Add --total revenue to the command above if you want the workbook to carry the sum as a live formula as well.
• "Make a PDF/deck/Word report of this CSV" — there is no one-shot for this, because a report is a document you design rather than a table you convert. Read the file (read_document.py, or the shell), then write a spec whose table block holds those rows, then run make_pdf.py / make_pptx.py / make_docx.py. Put the real rows in the spec; do not summarise them away.

make_pdf.py — a PDF report, or a picture

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python3 scripts/make_pdf.py --image photo.jpg --out photo.pdf
python3 scripts/make_pdf.py --image a.png --image b.png --out album.pdf --title Album
python3 scripts/make_pdf.py --spec spec.json --out report.pdf
python3 scripts/make_pdf.py --spec - --out report.pdf     # spec on stdin

"Turn this photo into a PDF" is --image, and it is one command. Do not write a spec for it. The user's attached files are already in your working directory under the names you were told — pass that name straight to --image. Each picture gets a page of its own, scaled to fit with its proportions kept; --title adds a title page. Pass either --image or --spec, never both.

An iPhone photo is HEIC, and the interpreter here has no HEIC decoder. The app stages a .png beside it for exactly this reason — if you were told about both IMG_0004.heic and IMG_0004.png, the .png is the one to pass.

Writes the PDF, reopens it with pypdf, and prints JSON: file, bytes, pages, blocks, extractable_characters. Non-zero exit means it failed.

jsonCopy code
{
  "title": "Q3 Revenue Review",
  "subtitle": "Prepared on device",
  "author": "TensorAgent",
  "page_size": "letter",
  "landscape": false,
  "margin_in": 0.9,
  "blocks": [
    { "type": "heading", "level": 1, "text": "Summary" },
    { "type": "paragraph", "text": "Revenue rose 18%.", "mono": false },
    { "type": "bullets", "items": ["South overtook East", "Costs flat"] },
    { "type": "numbers", "items": ["Load the CSV", "Group by region"] },
    { "type": "table",
      "columns": ["Region", "Units", "Revenue"],
      "rows": [["West", 120, 420.0], ["East", 200, 700.0]],
      "column_widths": [2, 1, 1],
      "zebra": true },
    { "type": "image", "path": "chart.png", "width_in": 5.0, "caption": "Figure 1." },
    { "type": "pagebreak" },
    { "type": "spacer", "height": 12 }
  ]
}

page_size is letter or a4. level is 1-3. Numeric table cells are right-aligned and thousands-separated automatically; the header row repeats when a table crosses a page. column_widths are relative, not absolute. An unknown block type is an error, not a skipped block.

There is no chart type. Draw one with Pillow into a .png and add it as an image block — that is what the image block is for.

make_xlsx.py — a workbook whose formulas show their numbers

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python3 scripts/make_xlsx.py --csv sales.csv --out sales.xlsx
python3 scripts/make_xlsx.py --csv sales.csv --out sales.xlsx --total revenue
python3 scripts/make_xlsx.py --spec spec.json --out book.xlsx

--csv writes the file as it stands: the header becomes the columns, and a cell that reads as a number is stored as one, so the column can be summed. --sheet-name names the sheet (default: the file's own name), and --total COL adds a SUM row for that column — a real formula with its value cached, like every other formula here. It is repeatable. Everything below is about --spec, which is what you use when the figures are ones you computed rather than ones already in a file.

Read this part. A formula in an .xlsx is only text; the number a reader sees is the cached value left behind by the last application that saved the file. There is no Excel and no LibreOffice on this device, so nothing will recalculate the sheet after you write it. This script therefore evaluates every formula in Python and writes the value into the cell alongside the formula. The file also carries fullCalcOnLoad, so a real Excel recomputes everything anyway the moment the user opens it.

If a formula cannot be evaluated here, the cell gets the formula and no cached value, the script lists it under uncomputable_formulas, and it exits non-zero. Tell the user which cells those are; do not present the file as finished.

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{
  "sheets": [
    {
      "name": "Sales",
      "columns": ["Region", "Units", "Price", "Revenue"],
      "rows": [["West", 120, 3.5, null], ["East", 200, 3.5, null]],
      "formulas": [
        { "range": "D2:D3", "formula": "=B{r}*C{r}" },
        { "cell": "D4", "formula": "=SUM(D2:D3)" },
        { "cell": "D5", "formula": "=ROUND(AVERAGE(D2:D3),2)" }
      ],
      "number_formats": { "C": "#,##0.00", "D": "#,##0.00" },
      "column_widths": { "A": 18 },
      "freeze_header": true,
      "auto_filter": true
    }
  ]
}

range fills a block of cells from one entry: {r} becomes the row number and {c} the column letter. Header cells are styled, columns are sized to content, and a formula may refer to another formula cell in any order.

Functions the evaluator implements: SUM AVERAGE AVG MEDIAN MIN MAX COUNT COUNTA STDEV PRODUCT ABS SQRT INT ROUND POWER LEN UPPER LOWER TRIM CONCAT CONCATENATE IF AND OR NOT, the operators + - * / ^ % &, comparisons, and A1 ranges. It does not implement lookups (VLOOKUP, INDEX/MATCH), whole-column ranges (A:B), cross-sheet references or dates. Compute those in Python and write the result as a value.

make_pptx.py — a slide deck

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python3 scripts/make_pptx.py --spec spec.json --out deck.pptx

16:9. Five slide layouts; every slide may have a title.

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{
  "title": "Q3 Revenue Review",
  "author": "TensorAgent",
  "slides": [
    { "layout": "title", "title": "Q3 Revenue Review", "subtitle": "Prepared on device" },
    { "layout": "bullets", "title": "What happened",
      "bullets": ["South overtook East", { "text": "since 2023", "level": 1 }] },
    { "layout": "table", "title": "By region",
      "columns": ["Region", "Revenue"], "rows": [["West", 420.0]] },
    { "layout": "image", "title": "Revenue", "image": "chart.png", "caption": "Figure 1." },
    { "layout": "text", "title": "Next", "text": "Recheck the North price." }
  ]
}

Bullets nest to three levels via level. The common tree form { "text": "Heading", "bullets": ["Detail"] } is also accepted and flattened to those levels, so do not rewrite an otherwise-correct spec just to expand it. A citation bullet may add date and url beside text; the writer joins all three visibly on the slide. Images are scaled to fit and centred. A table slide holds about 12 rows before it runs off the bottom — split a longer one across slides. notes is refused rather than silently dropped: speaker notes need a notesMaster part this writer does not build.

Prefer the canonical fields in the example. For compatibility with common deck specs, a slide with no layout is a bullets slide and its content array is an alias for bullets (do not provide both). An optional top-level sources array accepts URL strings or { "title": "...", "date": "...", "url": "..." } objects and becomes a final Sources slide. For compatibility, the same sources array on an individual slide is collected into that one final Sources slide; a slide whose only body is sources acts as that final-slide placeholder instead of creating an additional empty slide. A text slide that also contains a bullets array is unambiguously normalized to a bullets slide, with its introductory text retained first. Unknown fields, ambiguous aliases, and layouts without visible required content fail before a .pptx is written; the diagnostic names the slide and the fields accepted by its layout.

make_docx.py — a Word document

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python3 scripts/make_docx.py --spec spec.json --out report.docx

Same block vocabulary as make_pdf.py, minus spacer:

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{
  "title": "Q3 Revenue Review",
  "subtitle": "Prepared on device",
  "author": "TensorAgent",
  "blocks": [
    { "type": "heading", "level": 2, "text": "By region" },
    { "type": "paragraph", "text": "Revenue rose 18%.", "bold": false, "italic": false, "align": "left" },
    { "type": "bullets", "items": ["Top level", { "text": "Nested", "level": 1 }] },
    { "type": "numbers", "items": ["First", "Second"] },
    { "type": "table", "columns": ["Region", "Revenue"], "rows": [["West", 420.0]] },
    { "type": "image", "path": "chart.png", "width_in": 5.0, "caption": "Figure 1." },
    { "type": "pagebreak" }
  ]
}

Real Word styles (Title, Heading 1-3, Caption, Table Grid) and real list numbering, so the document's outline and its lists behave as Word's own.

analyze_table.py — numbers out of a CSV or XLSX

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python3 scripts/analyze_table.py sales.csv
python3 scripts/analyze_table.py sales.csv --group-by Region --sum Revenue --mean Price
python3 scripts/analyze_table.py book.xlsx --sheet Q3 --group-by Region --sum Revenue --out summary.xlsx
python3 scripts/analyze_table.py sales.csv --where "Units>=12" --where "Region=West" --sum Revenue --group-by Rep

With no --group-by it profiles every column: filled/empty counts, distinct values, and for numeric columns count, sum, mean, median, min, max and stdev; for text columns the five commonest values. JSON always goes to stdout.

$1,240.00, 1 240, 45% and (320) all read as numbers. A blank cell is not zero: it is left out of sums and means, and rows_dropped_not_a_number counts the rows an ordering filter could not judge. Say that number out loud if it is not zero — it usually is the finding.

formula_cells_without_cached_values is the other one to watch. Reading an .xlsx gives you the value the file has cached, so a workbook whose totals are formulas that nothing ever calculated reads as blank and counts as nothing. When that number is not zero, say so rather than reporting a total built out of holes.

read_document.py — text and tables out of a document

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python3 scripts/read_document.py report.pdf --tables --json extracted.json
python3 scripts/read_document.py notes.docx
python3 scripts/read_document.py deck.pptx --json deck.json
python3 scripts/read_document.py book.xlsx --sheet Q3

Readable text on stdout; --json also writes the structured form, which is what a follow-up script should consume.

• .pdf — pypdf in layout mode, which keeps each glyph near its column instead of reflowing the page. --tables then finds runs of lines whose fields start at the same character positions. It is a heuristic: it finds the tables a report lays out in aligned columns, misses ones drawn with borders and no alignment, and sometimes calls a numbered list a table. Look at what it returned before quoting it.
• .docx — paragraphs (with their style names, so headings are identifiable) and tables as rows of cells.
• .pptx — text per slide, in slide order.
• .xlsx — values via openpyxl. Formula cells hold whatever value was cached in the file; a workbook written by a tool that caches none reads as empty there. make_xlsx.py caches them, which is the point of it.

validate_document.py — check what you produced

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python3 scripts/validate_document.py report.docx deck.pptx book.xlsx report.pdf
python3 scripts/validate_document.py deck.pptx --json checks.json

Prints PASS/FAIL per file and exits non-zero if any failed. make_pdf.py, make_xlsx.py, make_docx.py and make_pptx.py already run this on their own output; use it on a file you assembled by hand or edited afterwards.

Limits

No Office application has opened these files. There is no Word, PowerPoint or Excel on this device, and no headless converter — LibreOffice, which the published document skills shell out to for exactly this check, cannot be bundled and could not be launched if it were, because iOS starts no subprocesses. What validate_document.py checks is that the package is internally consistent: every part [Content_Types].xml names exists, every part has a content type, every relationship resolves, every XML part parses, and the primary part has the root element and children its kind requires. That is a real check and it catches the mistakes that make a file unopenable — but a PASS means well formed, not opened. If the user reports that a file will not open, believe them.

python-docx and python-pptx ARE here, and so is lxml. Both import lxml.etree at module scope, and lxml is a C extension that no index publishes for iOS — so this app compiles it itself and ships it as signed frameworks, alongside python-pptx, python-docx, XlsxWriter and typing_extensions. Import them directly; pip install of any of them by name is a no-op. lxml is compiled into the app and cannot be replaced by a download; the pure ones could be shadowed by a pinned install, but nothing here needs that. The writers above still build the OOXML themselves with zipfile and xml.etree (they predate this, and they validate their own output), which is why their feature set is the list above and not everything Word can do. For what they lack — charts, speaker notes, headers and footers, editing a document the user gave you — use python-pptx or python-docx and run validate_document.py on the result, which checks their output the same way.

pdfplumber is not here. It needs pdfminer.six, which imports cryptography at the top of pdfminer/pdfdocument.py in every release back to 2022, and cryptography is a Rust extension with no pure wheel of any kind. That is why PDF reading is pypdf's layout mode and the table finder is a heuristic rather than a ruling-line analysis. An encrypted PDF that needs a real password cannot be opened here at all.

Spreadsheet formulas are evaluated by a subset. See the function list under make_xlsx.py. Anything outside it is refused by name and reported, never guessed at.