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 GGbond-bo/MemOmics-Agent --skill lbo-modelgit clone --depth 1 https://github.com/GGbond-bo/MemOmics-AgentWrote 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/ggbond-bo/memomics-agent/lbo-model)<a href="https://agentmods.dev/skills/ggbond-bo/memomics-agent/lbo-model"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/lbo-model/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/ggbond-bo/memomics-agent/lbo-model"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/lbo-model.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 | $0.00055 | $0.03875 |
| Opus 5 | $0.00028 | $0.01937 |
| Sonnet 5 | $0.00011 | $0.00775 |
| Haiku 4.5 | $0.00006 | $0.00387 |
Grade A, and why
lbo-model 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 10d 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.
This is a copy
95% identical to lbo-model — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 292 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Environment
This skill assumes headless openpyxl — you are producing an .xlsx file on disk.
Follow the excel-author skill's conventions for cell coloring, formulas, named ranges, and sensitivity tables.
Recalculate before delivery: python /path/to/excel-author/scripts/recalc.py ./out/model.xlsx.
TEMPLATE REQUIREMENT
This skill uses templates for LBO models. Always check for an attached template file first.
Before starting any LBO model:
- If a template file is attached/provided: Use that template's structure exactly - copy it and populate with the user's data
- If no template is attached: Ask the user: "Do you have a specific LBO template you'd like me to use? If not, I can use the standard template which includes Sources & Uses, Operating Model, Debt Schedule, and Returns Analysis."
- If using the standard template: Copy
examples/LBO_Model.xlsxas your starting point and populate it with the user's assumptions
IMPORTANT: When a file like LBO_Model.xlsx is attached, you MUST use it as your template - do not build from scratch. Even if the template seems complex or has more features than needed, copy it and adapt it to the user's requirements. Never decide to "build from scratch" when a template is provided.
CRITICAL INSTRUCTIONS — READ FIRST
Use Python/openpyxl. Write formula strings (ws["D20"] = "=B5*B6"), then run the excel-author skill's recalc.py helper before delivery.
Core Principles
- Every calculation must be an Excel formula - NEVER compute values in Python and hardcode results into cells. When using openpyxl, write
cell.value = "=B5*B6"(formula string), NOTcell.value = 1250(computed result). The model must be dynamic and update when inputs change. - Use the template structure - Follow the organization in
examples/LBO_Model.xlsxor the user's provided template. Do not invent your own layout. - Use proper cell references - All formulas should reference the appropriate cells. Never type numbers that should come from other cells.
- Maintain sign convention consistency - Follow whatever sign convention the template uses (some use negative for outflows, some use positive). Be consistent throughout.
- Work section by section, verify with user at each step - Complete one section fully, show the user what was built, run the section's verification checks, and get confirmation BEFORE moving to the next section. Do NOT build the entire model end-to-end and then present it — later sections depend on earlier ones, so catching a mistake in Sources & Uses after the returns are already built means rework everywhere.
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.
- 10d ago First seen · 292 lines · 55 tokens per session scan A 7cf6b5d937d7
lbo-model is a skill published in the GitHub repository GGbond-bo/MemOmics-Agent (19 stars, last pushed 7d ago), licensed MIT. It adds 55 tokens to every session and 3,875 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to lbo-model, differing in 2 lines, and is treated as a copy.
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