Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add corezoid/simulator-ai-plugin/plugin install simulatorWrote 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/corezoid/simulator-ai-plugin/simulator-simulate)<a href="https://agentmods.dev/skills/corezoid/simulator-ai-plugin/simulator-simulate"><img src="https://agentmods.dev/badge/skills/corezoid/simulator-ai-plugin/simulator-simulate/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/corezoid/simulator-ai-plugin/simulator-simulate"><img src="https://agentmods.dev/badge/skills/corezoid/simulator-ai-plugin/simulator-simulate.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 | $0.00271 | $0.01241 |
| Opus 5.5 | $0.00108 | $0.00496 |
| Sonnet 5.5 | $0.00054 | $0.00248 |
| Haiku 4.5 | $0.00027 | $0.00124 |
Grade A, and why
simulator-simulate 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 17d 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Simulate a Simulator graph
The simulation engine runs inside this MCP server. It reads a layer (actors, links, account values), runs behaviour rules in model time for each scenario and returns metrics. It never writes to Simulator: the source layer is only read.
Format of models and scenarios: $CLAUDE_PLUGIN_ROOT/docs/simulation/model-format.md — read
it before writing a model. Worked examples: $CLAUDE_PLUGIN_ROOT/docs/simulation/examples/.
1. Pick the graph — always ask
If a tool answers "not authenticated", call login and repeat the call.
- If the UI context has
activeLayer, offer it first; otherwise help the user find a layer (getLayerActorsPaginated,layerStats) and confirm which one. - Look at what is there before modelling:
simulationSnapshot(layerId, period?)writes<layerId>.sim.yamlwith every actor, link and account value the simulation will see. - Account values that accumulate over time (bills, turnover) need
period(e.g.30d): values become the turnover of the last period. Check for accounts that are breakdowns of the same total (by service, region, operation): summing them counts one bill many times.
2. Write the model with the user
Ask only what changes the result: which actors behave and how, what the scenarios change, the horizon, what to measure. Show the rules in plain words before running, and label assumptions (durations, probabilities, growth rates) as assumptions.
A model is YAML (spec §5): value_types, refs, params, horizon, initial_events,
behaviors.<actor type>.<event>: [actions], metrics, goals, optional helper actors
(people or resources that are not on the graph). Actor types are form titles.
Rules of thumb:
- Money and other quantities that only move between actors:
conserved: trueandtransfer. Things that only grow (hours worked, missed sales): not conserved,add. - Capacity (people, machines, slots): an integer conserved account on a resource actor plus
enqueue/release. - Processes drawn as graphs: steps are actors, transitions are links; an application walks
with
pick(where(actor(self.at).children(), …))(see examples/process_flow). - Never write a key named
on:(YAML reads it as true); useactor:.
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.
- 17d ago Changed · +6 lines 1e1ddacecc32
- 19d ago First seen · 86 lines · 271 tokens per session scan A da44d7d6838f
simulator-simulate is a skill published in the GitHub repository corezoid/simulator-ai-plugin (60 stars, last pushed yesterday), licensed MIT. It adds 271 tokens to every session and 1,241 once invoked, about $0.0011 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 2026-10-01.
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