holy-sheet

Zero-dependency .xlsx writer + reader + formula linter for agentic document creation. The Python mirror of PHP particle-academy/holy-sheet and Node @particle-academy/holy-sheet — same schema in, the same .xlsx out, whichever runtime you happen to be on.

import holy_sheet

schema = {
    "sheets": [
        {
            "name": "Sales",
            "columns": [
                {"header": "Region", "type": "string"},
                {"header": "Revenue", "type": "currency", "currency": "USD"},
            ],
            "rows": [
                ["North", 12000],
                ["South", 9800],
            ],
            "totals": {"Revenue": "sum"},
        }
    ]
}

holy_sheet.write(schema, "sales.xlsx")      # -> {"path": …, "bytes": …, "sheets": 1}
data = holy_sheet.to_bytes(schema)          # bytes, for an HTTP response

The schema is the point

That dict is the whole API. It is declarative and emittable in one shot — an agent describes the workbook it wants and hands it over, rather than driving a builder through forty calls and hoping the state machine agrees. There is no Workbook() to construct, no add_row(), no cursor.

Which is why the input is a plain dict, not a dataclass. The validator is the gate, not the type system:

holy_sheet.validate(schema)            # [] means valid; otherwise structured errors
holy_sheet.validate_and_repair(schema) # fixes the unambiguous mistakes, reports what it fixed
holy_sheet.lint(schema)                # evaluates every formula, reports the broken ones

validate_and_repair exists precisely because models send slightly-wrong JSON: a singular sheet key, row where rows belongs, an integer-keyed rows object, "1200" where a number goes. A dataclass would move the gate into a constructor and reject exactly the input the repairer is there to rescue. holy_sheet.schema.types carries TypedDicts for editor autocomplete; they are documentation, not constructors.

lint catches what an LLM actually gets wrong with formulas — referencing the header row instead of the first data row, a string in arithmetic, a cell that does not exist, a circular dependency — and says what to do about it:

>>> holy_sheet.lint({"sheets": [{"name": "Q4", "rows": [
...     ["Region", "Annual", "Monthly"],
...     ["NA", 12000, {"formula": "B1*12"}],
... ]}]})
[{'sheet': 'Q4', 'address': 'C2', 'formula': 'B1*12', 'error': '#VALUE!',
  'hint': 'Arithmetic on a non-numeric cell: B1 = "Annual" (string) '
          'Did you mean B2? (it holds 12000)'}]

The Agent API

Module-level functions — no class to instantiate, no DI container:

tool_definition() is byte-identical across all three engines and checksum-pinned in each — drop it into an Anthropic tool_use block or an OpenAI function definition and every backend describes the same tool.

Lower-level services are exported under their peer names for when you want to inject them: Validator, Repairer, Normalizer, FormulaLinter, Inference, Theme, XlsxWriter, XlsxReader, ArrayBuilder, CsvBuilder, CellAddress, SchemaException.

Moving between runtimes

The schema does not change. Only the call shape does.

Three differences worth knowing, each deliberate:

• write is synchronous. PHP's is; Node's is async only because browsers have no synchronous filesystem, which is not a constraint Python shares.
• from_csv accepts a path as well as content, following PHP. Node takes content only, because it targets browsers.
• read takes bytes and describe takes a path, which is Node's split rather than PHP's path-only reader. Bytes are the better primitive: an upload, a response body and a file all work.

What it writes

Multiple sheets · inline-string text cells · deduplicated fonts/fills/borders/numFmts in styles.xml · merged regions · column widths · frozen panes · comments (comments1.xml + vmlDrawing1.vml) · formulas with optional cached values · symbolic totals ({"Revenue": "sum"} becomes SUM(B2:B4)) · four themes.

Exactly those parts and no others — no sharedStrings.xml, no calcChain.xml, no theme1.xml. Those are the classic sources of xlsx diff noise and every engine in this family deliberately skips them. Output is deterministic: the same input produces the same bytes, always.

No dependencies, permanently

zipfile and xml.etree are standard library and are generic infrastructure. There is deliberately no openpyxl, no xlsxwriter, no lxml.

This is not minimalism for its own sake. The schema model is the product, and an all-in-one spreadsheet library would own it — along with the XML layout, which is the cross-runtime contract. The three engines agree at the level of part bytes: attribute order, self-closing style, the absence of inter-element whitespace, ' rather than '. A library owns every one of those decisions and cannot be talked out of them, so tests/test_parity_php.py — which runs the PHP engine as a subprocess and diffs each OOXML part — would fail on the first fixture.

Reading is different: xml.etree does that work, because nothing is serialised there.

Install

pip install fancy-holy-sheet

Requires Python 3.11+.

Development

python -m pytest

The suite includes cross-runtime parity against the PHP engine. It needs php on PATH (or PHP_BIN pointing at an interpreter) and a checkout of the PHP package beside this one (or HOLY_SHEET_PHP_SRC). Locally a missing toolchain skips those tests, loudly. Under CI it fails — a parity suite that silently stops comparing anything reads exactly like one that compares everything, and that is how two sibling suites reported green over zero cross-engine coverage for months.

The rest of the family

holy-sheet writes spreadsheets. Its siblings write the other two formats an agent gets asked for, with the same declarative-schema shape:

• dark-slide — .pptx
• last-word — .docx

Part of the Fancy UI suite.

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