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/oxi-717/ai-native-toolkit/loadtestnpx skills add OXI-717/ai-native-toolkit --skill loadtestgit clone --depth 1 https://github.com/OXI-717/ai-native-toolkitWrote 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/oxi-717/ai-native-toolkit/loadtest)<a href="https://agentmods.dev/skills/oxi-717/ai-native-toolkit/loadtest"><img src="https://agentmods.dev/badge/skills/oxi-717/ai-native-toolkit/loadtest.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 | $0.00155 | $0.01911 |
| Opus 5 | $0.00077 | $0.00955 |
| Sonnet 5 | $0.00031 | $0.00382 |
| Haiku 4.5 | $0.00015 | $0.00191 |
Grade A, and why
loadtest 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Webapp Loadtest
Time-bounded HTTP load test with Locust. Generates a scenario file from a template, runs for the duration the user names, produces a markdown report.
Default language: match user. Output: {cwd}/pentest-output/{target-slug}-{YYYY-MM-DD}/loadtest/.
Never run without a billing-awareness check. Serverless targets (Vercel, Cloudflare Workers, AWS Lambda) charge per request. A load test that runs for 10 minutes at 50 RPS = 30,000 requests the target owner pays for. Confirm the budget before starting.
Phase 0 — Scope
Ask or confirm:
- Target URL — base URL, e.g.
https://api.example.com - Endpoints to hit — list of paths + methods + bodies. Default: just
GET /if user didn't say. - Auth — none / bearer token / cookie / Telegram initData / custom header. If auth is needed, ask user for the artifact once and store in
secrets.env(gitignored). - Load profile — concurrent users + ramp-up + duration. Sane defaults:
--users 20 --spawn-rate 2 --run-time 2mfor a baseline- User can override with
--users 100 --spawn-rate 10 --run-time 5metc.
- RPS cap — enforced inside the generated locustfile via
wait_time = between(WAIT_MIN, WAIT_MAX). DefaultsWAIT_MIN=1, WAIT_MAX=2→ ~0.5–1 req/s/user → ~10–20 RPS at 20 users. Locust itself has no--no-rps-capflag; to remove the cap, the user passesWAIT_MIN=0 WAIT_MAX=0as env vars and the locustfile switches toconstant(0)wait. - Billing consent — confirm the target can afford this. If Vercel / serverless is involved, say it out loud.
Phase 1 — Workspace
slug=$(echo "<target-host>" | tr -cd 'a-z0-9-')
day=$(date +%Y-%m-%d)
workdir="${PWD}/pentest-output/${slug}-${day}/loadtest"
mkdir -p "${workdir}"
echo 'secrets.env
*.local.*' > "${workdir}/.gitignore"
Phase 2 — Generate Locustfile
Render references/locustfile-template.py into ${workdir}/locustfile.py. Fill in:
- Base URL
- Endpoint list (one
@task(weight=N)per endpoint) - Auth injection (headers or cookies)
- Optional: warm-up request on
on_start
What ships with it
5 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 · 136 lines · 155 tokens per session scan A ae88f024b910
loadtest is a skill published in the GitHub repository OXI-717/ai-native-toolkit (7 stars, last pushed 11d ago), licensed MIT. It adds 155 tokens to every session and 1,911 once invoked, about $0.0008 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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