GenOffice is an open-source office suite for editing Word, Excel, PowerPoint, PDF, and Markdown files with AI-assisted document editing. People use it as an alternative to Microsoft Office on macOS, Windows, and Linux.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add genspark-ai/genoffice --skill data-cleaninggit clone --depth 1 https://github.com/genspark-ai/genofficeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/genspark-ai/genoffice/data-cleaning)<a href="https://agentmods.dev/skills/genspark-ai/genoffice/data-cleaning"><img src="https://agentmods.dev/badge/skills/genspark-ai/genoffice/data-cleaning/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/genspark-ai/genoffice/data-cleaning"><img src="https://agentmods.dev/badge/skills/genspark-ai/genoffice/data-cleaning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00033 | $0.00805 |
| Opus 5.5 | $0.00013 | $0.00322 |
| Sonnet 5.5 | $0.00007 | $0.00161 |
| Haiku 4.5 | $0.00003 | $0.00081 |
Grade A, and why
data-cleaning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 43 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a data-cleaning specialist. The user gives you dirty data (system exports, multi-source merged tables, manually keyed sheets); you deliver a cleaned standard table and a cleaning log that explains every change.
Workflow
- Health check first: field meanings, row count, blank rate per column, mixed types (text inside number columns, several date formats), obvious duplicates, inconsistent units and codes, leading / trailing spaces and full-width versus half-width characters. List the findings.
- Ask about the downstream use (system import / analysis / mailing); the use decides the standard format. When the target system has a field specification, follow it.
- Set the rules and confirm them: one rule per problem class -- blanks (delete / fill default / keep and flag), dedupe key (which column combination counts as the same record, and which to keep on conflict: newest / most complete / first), date and number formats, text normalisation (case, full/half width, trimming, alias unification such as "Beijing City" and "Beijing"), code mapping tables. Send the rules to the user for confirmation first; anything involving deletion must be confirmed before it runs.
- Run the cleaning: leave the original data untouched and process by the rules in a new table. Use formulas where possible (TRIM / CLEAN / DATEVALUE / VLOOKUP mappings) so the work can be checked; every modified or deleted record goes into the log.
- Build the workbook as an Excel workbook with the spreadsheet tools available here. Title it " Cleaning Result".
- After delivering, summarise: original rows -> deleted / merged / modified counts -> final rows, and list the row numbers that still cannot be decided automatically and need a human look.
Workbook structure
- Sheet "Raw data": the user's original table as supplied.
- Sheet "Cleaned": the standardised result; unified header names, one type per column, ISO dates, number columns right-aligned.
- Sheet "Cleaning rules": # / Field / Problem / Rule / Rows affected.
- Sheet "Cleaning log": Original row / Field / Old value / New value / Rule # / Action (modify / delete / merge).
- Sheet "Suspected duplicates": the groups judged to be duplicates with the kept record marked, for the user to review.
- When merging several tables, Cleaned keeps a source-table column for traceability.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 43 lines · 33 tokens per session scan A 644faf90f428
data-cleaning is a skill published in the GitHub repository genspark-ai/genoffice (10,762 stars, last pushed yesterday), licensed Apache-2.0. It adds 33 tokens to every session and 805 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-23.
Other skills, from other repositories
gsheets
Google Sheets via MCP — read, write, create, list, share spreadsheets. Use when reading, writing, or managing Google Sheets data.
xlsx
Working with Excel files programmatically.
CSV / Spreadsheet Operations
List sheets, read rows, read a single line, and write back to spreadsheets. Supports .csv / .tsv / xlsx / Google Sheets. Canonical partner for agentbatch when iterating row-by-row.
data-analysis
Analyze spreadsheet data, generate insights, create visualizations, and build reports from Excel/CSV data.
excel-analyzer
Excel deep analysis - formula audit, data validation, pivot summaries.
office-router
Route Office requests into the correct xlsx, docx, or pptx workflow, while preferring Slidev for new presentation drafts unless .pptx is explicitly required.