Xlsx logo

Xlsx

Organization
cubeplexai
xlsx

Open, create, read, analyze, edit, or validate Excel/spreadsheet files (.xlsx, .xlsm, .csv, .tsv). Use when the user asks to create, build, modify, analyze, read, validate, or format any Excel spreadsheet, financial model, pivot table, or tabular data file. Covers: creating new xlsx from scratch, reading and analyzing existing files, editing existing xlsx with zero format loss, formula recalculation and validation, and applying professional financial formatting standards. Triggers on 'spreadsheet', 'Excel', '.xlsx', '.csv', 'pivot table', 'financial model', 'formula', or any request to produce tabular data in Excel format.

Overview

Publishercubeplexai
Repositorycubeplex
Skill namexlsx
Stars
533
Forks
46
Bundled files
23
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 23 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by cubeplexai on GitHub. Read the source before you install it.

Installation

Install the Xlsx AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/cubeplexai/cubeplex/tree/main/backend/skills/preinstalled/xlsx
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/cubeplexai/cubeplex.git /tmp/cubeplex
mkdir -p .claude/skills
cp -r /tmp/cubeplex/backend/skills/preinstalled/xlsx .claude/skills/xlsx
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Xlsx in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Xlsx on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Xlsx is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

xlsx

Skill directory

load_skill returned a path field — the sandbox directory holding this skill's scripts, templates, and references. Use it verbatim; never construct the path yourself:

bash
export SKILL_DIR="<the `path` value from load_skill>"

Handle the request in this conversation — don't delegate to subagents: edit integrity depends on one agent owning the file. Write output files to the working directory, never into $SKILL_DIR.

Choosing the write strategy: openpyxl vs XML surgery

Two ways to write an .xlsx, each with a job. Pick by one question: does an input file already exist whose non-cell content must survive?

SituationStrategyWhy
CREATE from scratch (no source file)openpyxl (default)Nothing to corrupt. openpyxl makes charts, conditional formatting, cover pages, and rich styling trivial — all painful in raw XML.
EDIT / FIX an existing fileXML surgery (required)openpyxl load_workbook() → save() silently drops VBA, pivot tables, sparklines, slicers, and some charts. Unpack → edit XML → pack changes only the nodes you touch.

Gray zone — a financial model built from scratch: openpyxl works and is faster. Reach for XML surgery only when you need byte-level determinism or precise styles.xml index control (see references/format.md).

The one absolute rule: never openpyxl.load_workbook(existing.xlsx) then save() — that round-trip is what corrupts. openpyxl is safe only on a workbook you created in the same script.

openpyxl writes formula strings but no cached results, so an openpyxl-created model (one that uses formulas) MUST be recalculated (libreoffice_recalc.py) and checked (formula_check.py) before delivery — same gate as the XML path. A report that writes computed values directly needs neither step.

Report vs Model: do you need formulas at all?

The Formula-First rule below applies to models — workbooks the user will keep editing, where they change inputs/assumptions and derived cells must recompute. Most "analyze this data and give me a spreadsheet" requests are reports: the numbers are already final, computed by pandas/openpyxl. Forcing live formulas + a LibreOffice recalc onto a report adds a slow, fragile external dependency for zero benefit — the data already knows the answer, and recalc can hang or silently skip cells.

Deliverable typeUse formulas?Recalculate?Why
Report (analysis output, point-in-time snapshot)No — write the computed value directlyNoValues are final; charts read the values. Zero external dependency, instant, always correct.
Model (user will change inputs/assumptions)Yes — every derived cell is a live formulaYes (mandatory)User edits inputs → other cells must recompute in Excel/WPS.

Decide with one question: Will the user open this file and change numbers, expecting other cells to update? If yes → Model (formulas + recalc). If no → Report (write values). When unsure, a report is the safer default — it always opens correctly with no recalc step.

  • Report: set each derived cell to its already-computed number (never a formula). openpyxl: ws["C2"] = round(revenue, 2). XML: <c r="C2"><v>12345.67</v></c> (no <f>). No libreoffice_recalc.py step. Run formula_check.py only when you edited an existing file (to catch XML/structure issues), not to check formulas.
  • Model: follow the Formula-First rules below; recalc is mandatory and is the final step before delivery.

Task Routing

TaskMethodGuide
READ — analyze existing dataxlsx_reader.py + pandasreferences/read-analyze.md
CREATE — new xlsx from scratchopenpyxl (default), or XML template for byte-level controlreferences/create.md + references/format.md
EDIT — modify existing xlsxXML unpack→edit→packreferences/edit.md (+ format.md if styling needed)
FIX — repair broken formulas in existing xlsxXML unpack→fix <f> nodes→packreferences/fix.md
VALIDATE — check formulasformula_check.pyreferences/validate.md

READ — Analyze data (read references/read-analyze.md first)

Start with xlsx_reader.py for structure discovery, then pandas for custom analysis. Never modify the source file.

Formatting rule: When the user specifies decimal places (e.g. "2 decimal places"), apply that format to ALL numeric values — use f'{v:.2f}' on every number. Never output 12875 when 12875.00 is required.

Aggregation rule: Always compute sums/means/counts directly from the DataFrame column — e.g. df['Revenue'].sum(). Never re-derive column values before aggregation.

CREATE — new file (read references/create.md + references/format.md)

Default: build with openpyxl — it unlocks charts, conditional formatting, cover pages, and styling with far less effort than raw XML. Alternative for byte-level determinism / precise style-index control: copy templates/minimal_xlsx/ → edit XML directly → pack with xlsx_pack.py.

Either way, the strategy depends on Report vs Model (see that section):

  • Model (user will edit inputs): every derived value MUST be a live Excel formula (=SUM(B2:B9) / <f>SUM(B2:B9)</f>), never a hardcoded number. Apply font colors per format.md. Recalculate + formula_check.py before delivery.
  • Report (final snapshot): skip formulas entirely — write the computed values directly and deliver immediately. Charts read the values; no recalc needed.

EDIT — XML direct-edit (read references/edit.md first)

CRITICAL — EDIT INTEGRITY RULES:

  1. NEVER create a new Workbook() for edit tasks. Always load the original file.
  2. The output MUST contain the same sheets as the input (same names, same data).
  3. Only modify the specific cells the task asks for — everything else must be untouched.
  4. After saving output.xlsx, verify it: open with xlsx_reader.py or pandas and confirm the original sheet names and a sample of original data are present. If verification fails, you wrote the wrong file — fix it before delivering.

Never use openpyxl round-trip on existing files (corrupts VBA, pivots, sparklines). Instead: unpack → use helper scripts → repack.

"Fill cells" / "Add formulas to existing cells" = EDIT task. If the input file already exists and you are told to fill, update, or add formulas to specific cells, you MUST use the XML edit path. Never create a new Workbook(). Example — fill B3 with a cross-sheet SUM formula:

bash
python3 $SKILL_DIR/scripts/xlsx_unpack.py input.xlsx /tmp/xlsx_work/
# Find the target sheet's XML via xl/workbook.xml → xl/_rels/workbook.xml.rels
# Then use `edit` to add <f> inside the target <c> element:
#   <c r="B3"><f>SUM('Sales Data'!D2:D13)</f><v></v></c>
python3 $SKILL_DIR/scripts/xlsx_pack.py /tmp/xlsx_work/ output.xlsx

Add a column (formulas, numfmt, styles auto-copied from adjacent column):

bash
python3 $SKILL_DIR/scripts/xlsx_unpack.py input.xlsx /tmp/xlsx_work/
python3 $SKILL_DIR/scripts/xlsx_add_column.py /tmp/xlsx_work/ --col G \
    --sheet "Sheet1" --header "% of Total" \
    --formula '=F{row}/$F$10' --formula-rows 2:9 \
    --total-row 10 --total-formula '=SUM(G2:G9)' --numfmt '0.0%' \
    --border-row 10 --border-style medium
python3 $SKILL_DIR/scripts/xlsx_pack.py /tmp/xlsx_work/ output.xlsx

The --border-row flag applies a top border to ALL cells in that row (not just the new column). Use it when the task requires accounting-style borders on total rows.

Insert a row (shifts existing rows, updates SUM formulas, fixes circular refs):

bash
python3 $SKILL_DIR/scripts/xlsx_unpack.py input.xlsx /tmp/xlsx_work/
# IMPORTANT: Find the correct --at row by searching for the label text
# in the worksheet XML, NOT by using the row number from the prompt.
# The prompt may say "row 5 (Office Rent)" but Office Rent might actually
# be at row 4. Always locate the row by its text label first.
python3 $SKILL_DIR/scripts/xlsx_insert_row.py /tmp/xlsx_work/ --at 5 \
    --sheet "Budget FY2025" --text A=Utilities \
    --values B=3000 C=3000 D=3500 E=3500 \
    --formula 'F=SUM(B{row}:E{row})' --copy-style-from 4
python3 $SKILL_DIR/scripts/xlsx_pack.py /tmp/xlsx_work/ output.xlsx

Row lookup rule: When the task says "after row N (Label)", always find the row by searching for "Label" in the worksheet XML (grep -n "Label" /tmp/xlsx_work/xl/worksheets/sheet*.xml or check sharedStrings.xml). Use the actual row number + 1 for --at. Do NOT call xlsx_shift_rows.py separately — xlsx_insert_row.py calls it internally.

Apply row-wide borders (e.g. accounting line on a TOTAL row): After running helper scripts, apply borders to ALL cells in the target row, not just newly added cells. In xl/styles.xml, append a new <border> with the desired style, then append a new <xf> in <cellXfs> that clones each cell's existing <xf> but sets the new borderId. Apply the new style index to every <c> in the row via the s attribute:

xml
<!-- In xl/styles.xml, append to <borders>: -->
<border>
  <left/><right/><top style="medium"/><bottom/><diagonal/>
</border>
<!-- Then append to <cellXfs> an xf clone with the new borderId for each existing style -->

Key rule: When a task says "add a border to row N", iterate over ALL cells A through the last column, not just newly added cells.

Manual XML edit (for anything the helper scripts don't cover):

bash
python3 $SKILL_DIR/scripts/xlsx_unpack.py input.xlsx /tmp/xlsx_work/
# ... edit XML with `edit` ...
python3 $SKILL_DIR/scripts/xlsx_pack.py /tmp/xlsx_work/ output.xlsx

FIX — Repair broken formulas (read references/fix.md first)

This is an EDIT task. Unpack → fix broken <f> nodes → pack. Preserve all original sheets and data.

VALIDATE — Check formulas (read references/validate.md first)

Run formula_check.py for static validation. Use libreoffice_recalc.py for dynamic recalculation when available.

Financial Color Standard

Cell RoleFont ColorHex Code
Hard-coded input / assumptionBlue0000FF
Formula / computed resultBlack000000
Cross-sheet reference formulaGreen00B050

Key Rules

  1. Formula-First for Models, Values for Reports: A model (user will edit inputs) MUST use live formulas for every derived cell. A report (final snapshot) MUST write the already-computed value directly — no formulas, no recalc. Decide by the "Report vs Model" rule above; do not force formulas onto a report.
  2. CREATE → openpyxl by default (charts/formatting made easy); XML template only when you need byte-level control
  3. EDIT existing → XML surgery: never openpyxl round-trip on a file you didn't create — it drops VBA/pivots/charts. Use unpack/edit/pack scripts
  4. Always produce the output file — this is the #1 priority
  5. Validate before delivery (models only): recalculate (if formulas), then formula_check.py exit code 0 = safe. Reports with no formulas skip this.
  6. Recalc is the final step — never overwrite the good copy. libreoffice_recalc.py now verifies that formula cells actually got cached <v> values; if it reports empty <v> cells, that is a HARD FAILURE — do not deliver. In a report workflow there are no formulas to recalc, so this never blocks you. If a model's recalc cannot be made to succeed, ship a report-style file (write the computed values) rather than delivering empty formula cells.

Utility Scripts

bash
python3 $SKILL_DIR/scripts/xlsx_reader.py input.xlsx                 # structure discovery
python3 $SKILL_DIR/scripts/formula_check.py file.xlsx --json         # formula validation
python3 $SKILL_DIR/scripts/formula_check.py file.xlsx --report      # standardized report
python3 $SKILL_DIR/scripts/xlsx_unpack.py in.xlsx /tmp/work/         # unpack for XML editing
python3 $SKILL_DIR/scripts/xlsx_pack.py /tmp/work/ out.xlsx          # repack after editing
python3 $SKILL_DIR/scripts/xlsx_shift_rows.py /tmp/work/ insert 5 1  # shift rows for insertion
python3 $SKILL_DIR/scripts/xlsx_add_column.py /tmp/work/ --col G ... # add column with formulas
python3 $SKILL_DIR/scripts/xlsx_insert_row.py /tmp/work/ --at 6 ...  # insert row with data

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Xlsx AI skill do?

Open, create, read, analyze, edit, or validate Excel/spreadsheet files (.xlsx, .xlsm, .csv, .tsv). Use when the user asks to create, build, modify, analyze, read, validate, or format any Excel spreadsheet, financial model, pivot table, or tabular data file. Covers: creating new xlsx from scratch, reading and analyzing existing files, editing existing xlsx with zero format loss, formula recalculation and validation, and applying professional financial formatting standards. Triggers on 'spreadsheet', 'Excel', '.xlsx', '.csv', 'pivot table', 'financial model', 'formula', or any request to prod...

Why use Xlsx on TypingMind?

Because you install it once and use it with any model. Xlsx is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Xlsx in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cubeplexai/cubeplex/tree/main/backend/skills/preinstalled/xlsx. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Xlsx?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Xlsx?

As many as you like. As long as a model supports skills, you can use Xlsx with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Xlsx AI skill free?

Yes. It is published on GitHub by cubeplexai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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