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 agentmods add commands/ololand-ai/ololand-plugins/workbookgit clone --depth 1 https://github.com/ololand-ai/ololand-pluginsWrote 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/commands/ololand-ai/ololand-plugins/workbook)<a href="https://agentmods.dev/commands/ololand-ai/ololand-plugins/workbook"><img src="https://agentmods.dev/badge/commands/ololand-ai/ololand-plugins/workbook.svg" alt="Measured on agentmods" 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 | $0.00023 | $0.00976 |
| Opus 5 | $0.00012 | $0.00488 |
| Sonnet 5 | $0.00005 | $0.00195 |
| Haiku 4.5 | $0.00002 | $0.00098 |
Grade A, and why
workbook 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 — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Workbook
Use this command when the user wants to see analysis workbooks, open workbook cells, create a workbook shell for follow-up analysis, or populate a workbook from a registered Excel template.
Usage
/workbook list <deal_id>
/workbook get <workbook_id>
/workbook create <deal_id> "QoE follow-up model"
/workbook templates [profile_id] # registered template profiles, or one profile's mappings
/workbook generate <deal_id> <template_id> [extraction_id] # DRAFT populated workbook from a template
/workbook excel-model <deal_id> [title] # persisted/downloadable live-formula LBO model
/workbook extraction <deal_id> <document_id> # extract canonical indicators from a deal document
/workbook extraction <extraction_id> # read back a finished extraction
Execution
list— callmcp__ololand__list_deal_workbooks(deal_id).get— callmcp__ololand__get_workbook(workbook_id, include_cells=true).create— confirm title and description, then callmcp__ololand__create_deal_workbook.templates— callmcp__ololand__list_excel_templates()for the company's registered template profiles; for one profile's status and learned field mappings, callmcp__ololand__get_template_profile(profile_id).generate— callmcp__ololand__generate_populated_workbook(deal_id, template_id), optionally with anextraction_id. Returns atask_id; pollmcp__ololand__check_task_statusfor the audit manifest and any fields still needing confirmation.excel-model— callmcp__ololand__generate_excel_model(deal_id, model_type="lbo", title=title)directly. This is distinct from the template-population path: it runs or reuses the persisted deterministic LBO engine, builds a live-formula.xlsx, saves it as a deal artifact, and returns the dock-artifact reference that opens with a Download button.model_type="dcf"fails closed withmodel_type_not_ready; DCF workbook generation is not ready.extraction— callmcp__ololand__extract_indicators_from_document(deal_id, document_id)to extract canonical financial indicators (with cell-level citations) from an uploaded document; returns atask_id. Pollmcp__ololand__check_task_status(task_id)until it completes and reports theextraction_id, then read results withmcp__ololand__get_canonical_extraction(extraction_id). If the user already has an extraction id, skip straight to the read.- If the user wants to write model cells, explain that workbook cell authoring remains in the app/workbook surface unless a specific MCP cell-write tool exists.
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 · 50 lines · 23 tokens per session scan A 3e27e5684317
workbook is a command published in the GitHub repository ololand-ai/ololand-plugins (0 stars, last pushed yesterday), licensed Apache-2.0. It adds 23 tokens to every session and 976 once invoked, about $0.0001 per session on Opus 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 2026-09-03.
Other commands, from other repositories
report
Generate a financial report from the book — a styled HTML page and/or an Excel workbook (balance sheet, income statement, transactions).
budget
Grant budget spreadsheet generation with forcing questions and task integration.
convert
Use when the user attaches a bank statement PDF or asks for CSV/XLSX/QBO/Xero export of a converted statement. Do not use for spending analysis, reconciliation, or benchmark scoring — those have their own skills.
clean
Turn messy or multi-format CSV exports into one clean, categorized, analysis-ready file.
3-statements
Fill out a 3-statement financial model template.
finance
You are a personal finance advisor helping the user manage their financial data using Jupyter notebooks via the jupyter-mcp server.