Omnigent is an open-source orchestration layer for running and coordinating different AI coding agents through one system. It is for developers who want to combine agents, apply policies and sandboxing, and continue sessions across devices. The catalogue add-ons extend its agent workflows.
Borrowing it
Nothing to install: this file belongs to omnigent-ai/omnigent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/omnigent-ai/omnigent/main/.claude/skills/run-load-test/SKILL.mdgit clone --depth 1 https://github.com/omnigent-ai/omnigentWrote 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/omnigent-ai/omnigent/run-load-test)<a href="https://agentmods.dev/skills/omnigent-ai/omnigent/run-load-test"><img src="https://agentmods.dev/badge/skills/omnigent-ai/omnigent/run-load-test/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/omnigent-ai/omnigent/run-load-test"><img src="https://agentmods.dev/badge/skills/omnigent-ai/omnigent/run-load-test.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00168 | $0.01130 |
| Opus 5.5 | $0.00067 | $0.00452 |
| Sonnet 5.5 | $0.00034 | $0.00226 |
| Haiku 4.5 | $0.00017 | $0.00113 |
Grade A, and why
run-load-test 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 2mo 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run the Omnigent load test
Drives dev/loadtest/ end to end: collect inputs → run → read summary.md →
explain the latencies. Each Locust user is a real omnigent host that
registers over the host tunnel, creates host-bound sessions, and drives real
multi-turn conversations — every turn is a genuine post→idle loop through the
host's runner, with the LLM mocked (zero latency) so the numbers are
Omnigent's own overhead. -u N scales the number of hosts.
It boots its own local stack (server + mock LLM), so there is no server to
point at, and it runs from a repo checkout only. For single-request latency
micro-benchmarks (not concurrency), that is a different tool: dev/benchmarks/.
1. Ensure deps (repo checkout)
uv sync --extra loadtest --extra agents-sdk
Run with that same interpreter (e.g. .venv/bin/python), from the repo root.
2. Gather inputs
Ask the user (AskUserQuestion when several are unknown); all have defaults.
| Input | Flag | Default | Notes |
|---|---|---|---|
| Hosts | --users |
4 | Concurrent hosts (N) — the main scale knob. |
| Spawn rate | --spawn-rate |
1 | Hosts started per second. |
| Run time | --run-time |
120s | 40s / 5m / 1h. |
| Sessions/host | --sessions-per-user |
2 | Host-bound sessions each host drives. |
| Turns/session | --turns-per-session |
4 | Turns per session — history grows across them. |
| Reply length | --reply-words |
60 | Words in the mocked (streamed) reply per turn. |
Capacity caveat — say this to the user if they ask for large N: turns run on
real host + runner subprocesses, so N hosts × M sessions = N×M runner processes
on this box. It is capacity-limited by design (real turns, not faked). Start at
--users 2 --sessions-per-user 1 --turns-per-session 2 --run-time 40s to confirm
the stack boots (~10-30s), then ramp to a few dozen hosts at most. At high N the
load box saturates before the server (Locust warns about CPU).
3. Run
python dev/loadtest/run.py \
--users <N> --spawn-rate <R> --run-time <T> \
--sessions-per-user <S> --turns-per-session <TU>
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.
- 2mo ago First seen · 81 lines · 168 tokens per session scan A 0840ffc56df4
run-load-test is a skill published in the GitHub repository omnigent-ai/omnigent (11,100 stars, last pushed today), licensed Apache-2.0. It adds 168 tokens to every session and 1,130 once invoked, about $0.0007 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 2026-08-30.
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