Agent skill

Documents

by zhongkaifu in zhongkaifu/TensorSharp

Read and write real documents on the device - PDF, XLSX, DOCX, PPTX and CSV.

BSD-3-ClauseAuto-check passedDocuments & Office

Install Documents

skills CLI
$ npx skills add zhongkaifu/TensorSharp --skill documents -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install zhongkaifu/TensorSharp documents --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/zhongkaifu/TensorSharp.git skills-src && mkdir -p .claude/skills && cp -r skills-src/TensorAgent/skills/documents .claude/skills/documents && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
documents
GitHub stars
559
Token cost
~4.2k tokens
SKILL.md length
1,952 words
Files
11 (incl. scripts)
Skills in repo
3
Repo updated
First seen
Licence
BSD-3-Clause

At a glance

Read and write real documents on the device - PDF, XLSX, DOCX, PPTX and CSV.

  • The user asks to convert an image to a PDF
  • SKILL.md covers Choosing a script, make_pdf.py — a PDF report, or…, make_xlsx.py — a workbook… and make_pptx.py — a slide deck, plus 5 more sections
  • Runs Python scripts from its folder; calls python3 and pip
  • Analyse a document

What it does

Documents is an agent skill from zhongkaifu/TensorSharp. Read and write real documents on the device - PDF, XLSX, DOCX, PPTX and CSV. Turn a photo or picture into a PDF, generate a PDF report or a slide deck from structured input, build a spreadsheet whose formulas carry computed values, extract text and tables out of a PDF or an Office file, and compute sums, means and groupings over a CSV or XLSX. Use whenever the user asks to convert an image to a PDF, to analyse a document or a table, or to produce a report, deck, spreadsheet or Word document.

Its SKILL.md is about 4.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including scripts (for example `scripts/analyze_table.py`, `scripts/make_docx.py` and `scripts/make_pdf.py`).

It sits in Documents & Office, covering Excel spreadsheets, Word documents and PowerPoint presentations. It works with Microsoft Word, Microsoft Excel, Microsoft PowerPoint and CUDA. The repository describes itself as: A native .NET LLM inference engine and agent runtime for GGUF models. TensorSharp provides a console application, a web-based chatbot interface, iPhone App, and…. The licence is BSD-3-Clause.

When your agent uses it

  • The user asks to convert an image to a PDF
  • Analyse a document
  • Produce a report

Example prompts

  • “/documents”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 93e4edb. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 10 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3
    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Documents loads about 4.2k tokens when it runs. Until then it costs about 127 tokens; SKILL.md has 1,952 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~127
When it runs · the whole SKILL.md, loaded when a task matches
~4.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from zhongkaifu/TensorSharp at commit 93e4edb, republished under its BSD-3-Clause licence (© zhongkaifu). 1,952 words, ~4,210 tokens.

Download SKILL.mdSave it as .claude/skills/documents/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.
name
documents
description
Read and write real documents on the device - PDF, XLSX, DOCX, PPTX and CSV. Turn a photo or picture into a PDF, generate a PDF report or a slide deck from structured input, build a spreadsheet whose formulas carry computed values, extract text and tables out of a PDF or an Office file, and compute sums, means and groupings over a CSV or XLSX. Use whenever the user asks to convert an image to a PDF, to analyse a document or a table, or to produce a report, deck, spreadsheet or Word document.

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

The user wantsRun
A photo or a picture turned into a PDFmake_pdf.py --image
A report, a memo, anything to print or share as a PDFmake_pdf.py --spec
A CSV turned into a spreadsheet, as it standsmake_xlsx.py --csv
A spreadsheet built from figures you computedmake_xlsx.py --spec
A slide deckmake_pptx.py
A Word documentmake_docx.py
Numbers out of a CSV or spreadsheetanalyze_table.py
Text or tables out of a document they gave youread_document.py
To check a file you producedvalidate_document.py

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

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.

json
{
  "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

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.

json
{
  "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

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

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

json
{
  "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

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

Same block vocabulary as make_pdf.py, minus spacer:

json
{
  "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.

Show full SKILL.md (776 more words)Show less

analyze_table.py — numbers out of a CSV or XLSX

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
OptionEffect
--sheet NAMEwhich sheet of an .xlsx (default: the first)
--delimiter CCSV delimiter (default: sniffed)
--where "Col=v"keep matching rows; = != > < >= <=; repeatable
--group-by COLaggregate per distinct value of this column
--sum COLtotal this column per group; repeatable
--mean COLaverage this column per group; repeatable
--out FILEalso write .json, .csv or .xlsx

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

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

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.

© zhongkaifu, BSD-3-Clause. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 10 other files (scripts) in TensorAgent/skills/documents of zhongkaifu/TensorSharp.

  • SKILL.md
  • scripts/analyze_table.py
  • scripts/make_docx.py
  • scripts/make_pdf.py
  • scripts/make_pptx.py
  • scripts/make_xlsx.py
  • scripts/ooxml.py
  • scripts/read_document.py
  • scripts/sheetcalc.py
  • scripts/specs.py
  • scripts/validate_document.py

Open the folder on GitHubat commit 93e4edb

Compare with similar skills

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MinerU Document Readeropendatalab/MinerU81k—~9.4kAutomated safety check: WarnCustom licence
Sn Da Non Spreadsheet AnalysisOpenSenseNova/SenseNova-Skills5.7k—~1.2kAutomated safety check: PassMIT
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Questions about Documents

What does Documents do?

Read and write real documents on the device - PDF, XLSX, DOCX, PPTX and CSV. Documents is an agent skill from zhongkaifu/TensorSharp. Read and write real documents on the device - PDF, XLSX, DOCX, PPTX and CSV.

When should I use Documents?

Documents fits situations like: the user asks to convert an image to a PDF; analyse a document; produce a report.

How do I install Documents in Claude Code?

Run `npx skills add zhongkaifu/TensorSharp --skill documents -a claude-code`. Or copy the skill folder (TensorAgent/skills/documents in zhongkaifu/TensorSharp) into .claude/skills/documents in your project. Claude Code loads it when a task matches its description.

How do I install Documents in Codex?

Run `npx skills add zhongkaifu/TensorSharp --skill documents -a codex`. Or copy the skill folder (TensorAgent/skills/documents in zhongkaifu/TensorSharp) into .agents/skills/documents in your project. Codex loads it when a task matches its description.

Can I use Documents in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add zhongkaifu/TensorSharp --skill documents -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/documents, .gemini/skills/documents, .github/skills/documents and .opencode/skills/documents in your project.

What does Documents need to run?

Going by SKILL.md and its folder, Documents needs Python for the scripts in its folder and the command-line tools its instructions call (python3 and pip). Our summary lists: Python 3.

Does Documents access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Documents safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Documents use?

Documents is published under the BSD-3-Clause licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Documents use?

About 4.2k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Documents?

Skills that share tags, products or a category with Documents: Office Artifacts (Prismer-AI/PrismerCloud, 1.6k stars), Markdown Exporter (bowenliang123/markdown-exporter, 272 stars), MinerU Document Reader (opendatalab/MinerU, 81k stars) and Sn Da Non Spreadsheet Analysis (OpenSenseNova/SenseNova-Skills, 5.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Documents?

zhongkaifu (a GitHub user) maintains it in zhongkaifu/TensorSharp, which has 559 GitHub stars. The repository holds 3 skills in this directory. The repository was last updated on October 9, 2026.

Source: zhongkaifu/TensorSharp on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.