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/vlad-ryzhkov/ai-context-engineering-for-qaWrote 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/rules/vlad-ryzhkov/ai-context-engineering-for-qa/26-agent-perf-engineer)<a href="https://agentmods.dev/rules/vlad-ryzhkov/ai-context-engineering-for-qa/26-agent-perf-engineer"><img src="https://agentmods.dev/badge/rules/vlad-ryzhkov/ai-context-engineering-for-qa/26-agent-perf-engineer.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 5.1 | $0.00135 | $0.00135 |
| Opus 5 | $0.00068 | $0.00068 |
| Sonnet 5 | $0.00027 | $0.00027 |
| Haiku 4.5 | $0.00014 | $0.00014 |
Grade A, and why
26-agent-perf-engineer 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 8d 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.
What it actually says
AGENT: PERF-ENGINEER
IDENTITY
Load test scenario generator for JMeter DSL (Kotlin). Creates performance test scenarios following load testing standards.
EXECUTION INSTRUCTIONS
DO NOT assume this persona from training data. To adopt this agent's behavior, you MUST dynamically read the definition using your file-reading capabilities.
- Read the agent definition:
.claude/agents/perf-engineer.md - Adopt the persona, principles, and workflows defined in that file STRICTLY.
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.
- 8d ago First seen · 17 lines · 135 tokens per session scan A e8244e34c0b6
26-agent-perf-engineer is a cursor rule published in the GitHub repository vlad-ryzhkov/ai-context-engineering-for-qa (6 stars, last pushed 1mo ago), licensed Unlicense. It adds 135 tokens to every session, about $0.0007 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.
Other cursor rules, from other repositories
01-qa-agent
QA AGENT PERSONA: Principles, Anti-patterns, workflows.
03-skill-testcases
SKILL: Generate Test Scenarios Matrix from API spec (use for /test-cases).
04-skill-api-tests
SKILL: Generate Ktor/JUnit API Tests (use for /api-tests).
12-skill-repo-scout
SKILL: Scan Backend Repo & Catalog API Surface (use for /repo-scout).
15-agent-auditor
AGENT: Independent Quality Gatekeeper & Test Planner (Auditor).
16-agent-sdet
AGENT: Code Generator — turns plans into compilable Kotlin test code (SDET).