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 skills/livekit/agent-skills/livekit-simulationsnpx skills add livekit/agent-skills --skill livekit-simulationsgit clone --depth 1 https://github.com/livekit/agent-skillsWrote 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/livekit/agent-skills/livekit-simulations)<a href="https://agentmods.dev/skills/livekit/agent-skills/livekit-simulations"><img src="https://agentmods.dev/badge/skills/livekit/agent-skills/livekit-simulations.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 | $0.00183 | $0.01644 |
| Opus 5 | $0.00092 | $0.00822 |
| Sonnet 5 | $0.00037 | $0.00329 |
| Haiku 4.5 | $0.00018 | $0.00164 |
Grade A, and why
livekit-simulations 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 4d 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
⚠️ Simulations are in private beta (not yet generally available).
- No docs/MCP coverage yet. For the
lk agent simulatecommand surface, uselk agent simulate --helpand the LiveKit Cloud dashboard rather thanlk docs/ MCP until simulations are documented.- Recent SDK required. Running simulations needs the 1.6 line of
livekit-agents. Confirm the installed version rather than assuming.- Limited availability / auth. Creating runs needs the project enabled for simulations and a current
lk cloud authsession. (Generating scenarios — the main job of this skill — needs neither; it's fully local.)
Generating Simulation Scenarios
The most valuable thing you can do with simulations is generate good test scenarios for the user's agent — grounded in the agent's actual code and in what the user wants stress-tested — then run them. You do this locally: you read the code with your normal tools (nothing is uploaded), and you (the coding agent) are the model that does the generation, so no extra API keys or services are needed.
A scenario = a simulated user's persona + goals (instructions) and the pass criteria (agent_expectations). A simulation plays each scenario against the agent over text and an LLM judge scores it. Your job is to produce a high-quality, diverse, on-target set of scenarios and write them to a YAML scenarios file the CLI can run.
What makes this better than autopilot
A naive "just generate some tests" misses the point. Three things make this skill worth using:
- It reads the agent's real code — so scenarios respect what the agent can actually do and where it blocks (especially constraints/unavailable items), instead of guessing from the name.
- It is steered by the user. The user knows what they're worried about. Always capture that intent and thread it through. This is the headline — see
references/user-guidance.md. - It guarantees coverage of every risk. Left alone, generation drifts to plausible happy-path calls and silently skips the hard cases — withholding a required field, supplying an invalid value, an empty lookup, and the guardrail/abuse surface (out-of-scope, harmful, professional-advice, sensitive-data, prompt-extraction). This skill turns the agent's constraints into an explicit risk checklist and requires at least one scenario per item — see
references/analyzing-the-agent.mdandreferences/writing-scenarios.md.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 64 lines · 183 tokens per session scan A 9b714493c73a
livekit-simulations is a skill published in the GitHub repository livekit/agent-skills (66 stars, last pushed 2mo ago), licensed MIT. It adds 183 tokens to every session and 1,644 once invoked, about $0.0009 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 2026-08-30.
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