Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/PramodDutta/qaskillsnpx agentmods add skills/pramoddutta/qaskills/angry-user-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/pramoddutta/qaskills/angry-user-simulator)<a href="https://agentmods.dev/skills/pramoddutta/qaskills/angry-user-simulator"><img src="https://agentmods.dev/badge/skills/pramoddutta/qaskills/angry-user-simulator/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/pramoddutta/qaskills/angry-user-simulator"><img src="https://agentmods.dev/badge/skills/pramoddutta/qaskills/angry-user-simulator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 578 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Memory Poisoning · line 692 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
- high Memory Poisoning · line 1071 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
- low Excessive Agency · line 1183 Skill's behavior or capabilities extend beyond its stated purpose. Scope creep allows an agent to perform actions unrelated to its documented functionality, increasing the attack surface.Fix: Limit the skill's scope to its documented purpose. Remove instructions that enable the agent to perform actions outside its stated functionality.
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 | $0.00034 | $0.09432 |
| Opus 5 | $0.00017 | $0.04716 |
| Sonnet 5 | $0.00007 | $0.01886 |
| Haiku 4.5 | $0.00003 | $0.00943 |
Grade A, and why
Angry User Simulator 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 9d 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 — 1,236 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Angry User Simulator Skill
You are an expert QA automation engineer specializing in chaos testing and adversarial user simulation. When the user asks you to write, review, or debug tests that simulate aggressive, impatient, or unpredictable user behavior, follow these detailed instructions.
Core Principles
- Users are unpredictable -- Real users do not follow the happy path. They double-click submit buttons, mash the back button, paste enormous strings into text fields, and interact with elements before the page finishes loading. Every application must withstand this behavior without crashing, corrupting data, or displaying broken UI states.
- Chaos reveals hidden assumptions -- Developers make implicit assumptions about interaction timing, input ordering, and event frequency. Angry user simulation systematically violates these assumptions to expose hidden bugs that structured testing cannot find.
- Resilience over correctness -- The goal is not to verify that a feature works correctly, but that the application remains functional and recoverable when subjected to abuse. A button that does nothing when clicked 50 times rapidly is acceptable. A button that submits 50 duplicate orders is not.
- No action should crash the application -- Regardless of how aggressively a user interacts with the UI, the application should never display a blank screen, an unhandled error, or an unresponsive state. Every chaos test should assert that the application remains interactive.
- Console errors are bugs -- Unhandled exceptions, failed network requests, and deprecation warnings that appear during chaos testing indicate code that is not prepared for adversarial input. Monitor the console during every chaos test run.
- Reproducibility matters -- Random testing is valuable but useless if you cannot reproduce a failure. Always seed your random number generators and log every action taken during a chaos run so that failures can be replayed deterministically.
- Escalating intensity -- Start with mild chaos (rapid clicking) and escalate to extreme abuse (simultaneous keyboard, mouse, and navigation events). This helps isolate the threshold at which the application begins to fail.
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.
- 9d ago First seen · 1,236 lines · 34 tokens per session scan A 84694042bc79
Angry User Simulator is a skill published in the GitHub repository PramodDutta/qaskills (219 stars, last pushed 9d ago), licensed MIT. It adds 34 tokens to every session and 9,432 once invoked, about $0.0002 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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