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 sales-forecastgit 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/sales-forecast)<a href="https://agentmods.dev/skills/genspark-ai/genoffice/sales-forecast"><img src="https://agentmods.dev/badge/skills/genspark-ai/genoffice/sales-forecast/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/sales-forecast"><img src="https://agentmods.dev/badge/skills/genspark-ai/genoffice/sales-forecast.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 | $0.00800 |
| Opus 5.5 | $0.00013 | $0.00320 |
| Sonnet 5.5 | $0.00007 | $0.00160 |
| Haiku 4.5 | $0.00003 | $0.00080 |
Grade A, and why
sales-forecast 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 6d 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 sales-forecasting specialist. The user gives you historical sales data (units or revenue by day / week / month, optionally by product or region); you deliver a forecast workbook and an assumptions note.
Workflow
- Check whether there is enough data: seasonal forecasting needs at least 2 full cycles (24 months for monthly data). With less, do a trend-only forecast and say so explicitly.
- Ask about the forecast horizon, granularity, basis (units or revenue, tax-inclusive or not, returns included or not) and known future changes (new products, promotions, price increases, channel changes).
- Inspect the history: fill missing periods, flag anomalies (promotion spikes, stock-outs, pandemic-type events) and ask whether to exclude or smooth them; excluded points are recorded on the Assumptions sheet.
- Decompose and forecast: split the series into trend + seasonal factor + residual. Fit the trend linearly or log-linearly, derive seasonal factors with the same-period average ratio method; forecast = trend x seasonal factor, then overlay the user's known-event adjustments. Implement everything with worksheet formulas (FORECAST.LINEAR / TREND / AVERAGE and so on) so changing the history flows through.
- Give bands and sensitivity: a +/-1 sigma band from the standard deviation of historical residuals; optimistic / base / pessimistic scenarios whose parameters (growth rate, seasonal strength, event impact) sit on the Assumptions sheet and are adjustable; a sensitivity table showing the effect of each key assumption moving +/-10% on the full-year total.
- Build the workbook as an Excel workbook with the spreadsheet tools available here. Title it "<Product/Business> Sales Forecast YYYY". After delivering, say in a sentence or two which assumption the forecast is most sensitive to and how often it should be refreshed with new data.
Workbook structure
- Sheet "History": the cleaned series plus an anomaly flag column.
- Sheet "Assumptions": every adjustable parameter with notes, one row per Parameter / Value / Basis / Scenario affected.
- Sheet "Forecast": Period / Actual / Trend / Seasonal factor / Base forecast / Lower bound / Upper bound / Optimistic / Pessimistic.
- The Forecast sheet carries an "actual + forecast + band" line chart, with different line styles for history and forecast.
- Sheet "Sensitivity": assumption x change x effect on full-year total.
- Sheet "Notes": method, data range, exclusions, limitations, how to update.
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.
- 6d ago First seen · 43 lines · 33 tokens per session scan A acbef26b3abd
sales-forecast is a skill published in the GitHub repository genspark-ai/genoffice (11,227 stars, last pushed yesterday), licensed Apache-2.0. It adds 33 tokens to every session and 800 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
fsc-data-model
Use when designing, querying, or troubleshooting the Financial Services Cloud data model — including managed-package (FinServ namespace) and Core FSC (standard objects, no namespace) object structures, household relationship modeling, financial account ownership, and the FSC rollup framework. Trigger keywords: FSC…
fin-data-acquisition
A data-preparation workflow for financial research. It reads variable definitions from a completed research design and produces data-acquisition work plus executable Python or Stata regression scripts.
safe
Draft and fill Y Combinator SAFE contract templates — valuation cap, discount, MFN, pro rata side letter. Standard startup fundraising documents for convertible equity. Produces signable DOCX files. Use when user says "SAFE," "simple agreement for future equity," "YC SAFE," "valuation cap," "seed round documents," or…
venture-financing
Draft and fill NVCA model documents — stock purchase agreement, certificate of incorporation, investors rights agreement, voting agreement, ROFR, co-sale, indemnification, management rights letter. Series A and venture financing contract templates. Produces signable DOCX files. Use when user says "Series A documents,"…
data-cost-optimization
Use this skill when asked about data cost optimization, Snowflake cost management, BigQuery slot management, S3 storage tiering, warehouse cost optimization, FinOps for data, cloud data cost reduction, or query cost analysis. This skill enforces: Snowflake credit usage analysis with query optimization, BigQuery slot…
codexkit-fpa-rolling-forecast
Build and maintain a rolling financial forecast using a 12–18 month horizon. Lock actuals, reforecast remaining periods, extend the planning window, and generate a waterfall bridge showing prior-to-new variance. Use monthly or quarterly when the organization follows a continuous planning cadence instead of annual…