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 skills add patricio0312rev/skillset --skill flaky-test-detectivegit clone --depth 1 https://github.com/patricio0312rev/skillsetWrote 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/patricio0312rev/skillset/flaky-test-detective)<a href="https://agentmods.dev/skills/patricio0312rev/skillset/flaky-test-detective"><img src="https://agentmods.dev/badge/skills/patricio0312rev/skillset/flaky-test-detective/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/patricio0312rev/skillset/flaky-test-detective"><img src="https://agentmods.dev/badge/skills/patricio0312rev/skillset/flaky-test-detective.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00057 | $0.02629 |
| Opus 5 | $0.00028 | $0.01314 |
| Sonnet 5 | $0.00011 | $0.00526 |
| Haiku 4.5 | $0.00006 | $0.00263 |
Grade A, and why
flaky-test-detective scanned grade A with 1 finding 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
import { execSync } from "child_process"; This is a copy
100% identical to flaky-test-detective — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 417 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Flaky Test Detective
Diagnose and eliminate flaky tests systematically.
Common Flaky Test Patterns
1. Timing Issues
// ❌ Flaky: Race condition
test("should load user data", async () => {
render(<UserProfile userId="123" />);
// Race condition - might pass or fail
expect(screen.getByText("John Doe")).toBeInTheDocument();
});
// ✅ Fixed: Wait for element
test("should load user data", async () => {
render(<UserProfile userId="123" />);
await waitFor(() => {
expect(screen.getByText("John Doe")).toBeInTheDocument();
});
});
// ❌ Flaky: Fixed timeout
test("should complete animation", async () => {
render(<AnimatedComponent />);
await new Promise((resolve) => setTimeout(resolve, 500)); // Brittle!
expect(element).toHaveClass("animated");
});
// ✅ Fixed: Wait for condition
test("should complete animation", async () => {
render(<AnimatedComponent />);
await waitFor(
() => {
expect(element).toHaveClass("animated");
},
{ timeout: 2000 }
);
});
2. Shared State
// ❌ Flaky: Global state pollution
let userId = "123";
test("test A", () => {
userId = "456"; // Modifies global
// ...
});
test("test B", () => {
expect(userId).toBe("123"); // Fails if test A runs first!
});
// ✅ Fixed: Isolated state
test("test A", () => {
const userId = "456"; // Local variable
// ...
});
test("test B", () => {
const userId = "123";
expect(userId).toBe("123");
});
// ❌ Flaky: Database not cleaned
test("should create user", async () => {
await db.user.create({ email: "[email protected]" });
// No cleanup!
});
test("should create another user", async () => {
await db.user.create({ email: "[email protected]" }); // Fails! Duplicate
});
// ✅ Fixed: Proper cleanup
afterEach(async () => {
await db.user.deleteMany();
});
3. Randomness
// ❌ Flaky: Random data
test("should sort users", () => {
const users = generateRandomUsers(10); // Different each time!
const sorted = sortUsers(users);
expect(sorted[0].name).toBe("Alice"); // Might not be Alice
});
// ✅ Fixed: Deterministic data
test("should sort users", () => {
const users = [
{ name: "Charlie", age: 30 },
{ name: "Alice", age: 25 },
{ name: "Bob", age: 35 },
];
const sorted = sortUsers(users);
expect(sorted[0].name).toBe("Alice");
});
// ✅ Fixed: Seeded randomness
import { faker } from "@faker-js/faker";
beforeEach(() => {
faker.seed(12345); // Same data every time
});
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 · 417 lines · 57 tokens per session scan A a81d751cb579
flaky-test-detective is a skill published in the GitHub repository patricio0312rev/skillset (6 stars, last pushed 8mo ago), licensed MIT. It adds 57 tokens to every session and 2,629 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). It is 100% identical to flaky-test-detective, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
neuron-evaluation-engineer
Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
skill-benchmark
Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.