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 ololand-ai/ololand-plugins --skill cmd-cim-generategit 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/skills/ololand-ai/ololand-plugins/cmd-cim-generate)<a href="https://agentmods.dev/skills/ololand-ai/ololand-plugins/cmd-cim-generate"><img src="https://agentmods.dev/badge/skills/ololand-ai/ololand-plugins/cmd-cim-generate.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 | $0.00062 | $0.01400 |
| Opus 5 | $0.00031 | $0.00700 |
| Sonnet 5 | $0.00012 | $0.00280 |
| Haiku 4.5 | $0.00006 | $0.00140 |
Grade A, and why
cim-generator-cim-generate 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 8d 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codex wrapper for /cim-generate
Follow the OloLand command instructions below when the user asks for /cim-generate or the equivalent workflow in Codex.
CIM Generate
Generate a Confidential Information Memorandum (CIM) — the sell-side marketing document used in M&A to present a target company to potential buyers. OloLand's generator pulls from the deal's reconciled financial snapshots, knowledge-graph risk/opportunity insights, and market research already on the platform, and only falls back to fresh web research for gaps that platform data doesn't cover — so exhibits are traceable to a source, not synthesized whole-cloth.
Usage
/cim-generate <deal_id> [section, section, ...]
/cim-generate read <deal_id> [section, section, ...]
Arguments
deal_id(required) — The deal to generate or read a CIM for. Must already exist in OloLand with at least a financial snapshot; run due diligence first if it doesn't.sections(optional) — A subset of the 14 sections to generate or read, if the user only wants part of the CIM (see the list below). Default: all 14.
Execution — generate (default)
- If the user asked for specific sections, map each user-phrased name to its exact snake_case identifier from the 14-section list below before calling the tool (e.g. "Executive Summary" →
executive_summary, "risk section" →risk_factors, "financials" →financial_performanceand/orfinancial_projections— confirm with the user when ambiguous). Only these identifiers are validsectionsvalues; the backend silently drops anything else. - Call
generate_cim(deal_id, sections=<optional list>)from the MCP server. This is a long-running operation — it returns atask_id, not the CIM itself. - Tell the user generation has started and poll
check_task_status(task_id)every ~5 seconds, up to a maximum of ~24 attempts (~2 minutes), until the status issuccessorfailure. A full 14-section CIM typically takes 30-90 seconds (platform data aggregation, then targeted market research for gaps, then section synthesis). - If the task is still pending after the polling budget, stop polling — do not loop indefinitely. Tell the user generation continues server-side, give them the
task_id, and invite them to ask again in a minute (you'll re-check withcheck_task_status(task_id)); the finished CIM also appears in the deal workspace regardless. - On success, the task result includes the
cim_id,sections_count, andword_count. - On failure, surface the error from the task result — do not retry silently more than once.
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.
- 8d ago First seen · 68 lines · 62 tokens per session scan A a751fac917b0
cim-generator-cim-generate is a skill published in the GitHub repository ololand-ai/ololand-plugins (0 stars, last pushed 3d ago), licensed Apache-2.0. It adds 62 tokens to every session and 1,400 once invoked, about $0.0003 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 2026-08-31.
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