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.
git 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/war-game)<a href="https://agentmods.dev/commands/ololand-ai/ololand-plugins/war-game"><img src="https://agentmods.dev/badge/commands/ololand-ai/ololand-plugins/war-game/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/commands/ololand-ai/ololand-plugins/war-game"><img src="https://agentmods.dev/badge/commands/ololand-ai/ololand-plugins/war-game.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 | $0.00029 | $0.01306 |
| Opus 5.5 | $0.00012 | $0.00522 |
| Sonnet 5.5 | $0.00006 | $0.00261 |
| Haiku 4.5 | $0.00003 | $0.00131 |
Grade A, and why
war-game 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 1mo 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
War Game Simulation
Run an RL-powered competitive strategy simulation for a deal.
Usage
/war-game <deal_id> [scenarios]
Arguments
deal_id(optional) — The deal to simulate. If omitted, resolve it from the prose (see below).scenarios(optional) — Comma-separated:base_case,expansion,macro_stress,regulated_stress, orall. Default:base_case. When the user asks about regulatory response, includeregulated_stress.
Natural-language invocation (no deal_id)
Users rarely pass a bare deal_id — they describe a company and a counterfactual, e.g. "/war-game on Robinhood, assume it built Exchange Funds in-house instead of acquiring Frec/Cache. Competitive and regulatory response?" Treat the counterfactual as a caller-supplied hypothetical. If the simulator or required deal evidence is unavailable, relay that result; do not present an empty or inferred result as a simulation.
- Resolve the target to an existing deal. Use
deal-search/list_dealsto find an existing deal for the named company. If none exists, say that a deal context is required; do not create one automatically. - Separate review from execution. For an audit, review, or read-only request, follow
/plan: create or reuse the deal conversation session, submit the user's verbatim counterfactual withsubmit_plan_for_approval, render the plan, and stop for approval. After approval, direct the user to continue the same session in the OloLand app or normal SSE/message endpoint with the returned plan payload supplied asapproved_planonSubmitMessageRequest. That field supplies execution context; it is not a persisted or hash-validated approval identity. Do not call the MCPask_deal_agentortalk_to_dealtools for this planned execution because they cannot carry the field; quoted plan prose is not a substitute. If the client cannot use the normal endpoint, report the governed review as unavailable on this rail; do not run a simulation or infer an answer. For a request that explicitly asks to run the simulation, continue to Execution step 1; that is a bounded, fixed-purpose tool call rather than a conversational executor turn. - Pick scenarios from intent. "regulatory response" →
regulated_stress; "across conditions" / unspecified depth →all; otherwisebase_case. - If the user supplied a custom counterfactual or premise that would change the simulated business, buyer, capability, market, or competitive setup, fail closed as unsupported on this MCP rail.
run_war_game_simulationaccepts onlydeal_idandscenarios; do not discard the premise, invent an argument, substitute another tool, or run the default deal-context simulation as if it answered the custom question. State that the requested premise cannot be carried by this bounded tool. A plain scenario request with no custom premise may continue to Execution. - Separate factual buyer premises from simulator predictions. Do not claim a buyer/acquirer identity, observed strategy, historical action, expected synergy, or any other real-world premise unless a tenant-authorized returned source supports it with an inline citation. Keep caller-provided buyer premises labeled hypothetical. A completed simulator's modeled strategy, likely action, competitor response, EV distribution, or robustness score may be relayed without a document citation only when it is clearly labeled simulator prediction and tied to the exact returned simulation identity and scenario; never restate that prediction as a sourced fact.
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.
- 1mo ago First seen · 61 lines · 29 tokens per session scan A 1b43e95f2adc
war-game is a command published in the GitHub repository ololand-ai/ololand-plugins (0 stars, last pushed 3d ago), licensed Apache-2.0. It adds 29 tokens to every session and 1,306 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 2026-09-03.
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cost-per-task
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audit-checklist
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scan
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