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 rules/dxheroes/mcp-devtools/testing-standardsgit clone --depth 1 https://github.com/DXHeroes/mcp-devtoolsWhat 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 | $0.00516 | $0.00516 |
| Opus 5 | $0.00258 | $0.00258 |
| Sonnet 5 | $0.00103 | $0.00103 |
| Haiku 4.5 | $0.00052 | $0.00052 |
Grade A, and why
testing-standards 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 yesterday.
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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Testing Standards
Follow these standards when writing tests for MCP DevTools packages:
@file packages//src/**/.test.ts @file .cursor/rules/typescript-style.mdc
Test Structure
import {
describe,
it,
expect,
beforeEach,
afterEach,
jest,
} from "@jest/globals";
import { featureUnderTest } from "../path/to/feature";
describe("Feature Name", () => {
// Setup mocks and test fixtures
beforeEach(() => {
// Setup code
});
afterEach(() => {
// Cleanup code
jest.resetAllMocks();
});
describe("Specific Functionality", () => {
it("should behave as expected in normal conditions", async () => {
// Arrange
const input = {
/* test data */
};
// Act
const result = await featureUnderTest(input);
// Assert
expect(result).toEqual(/* expected output */);
});
it("should handle error cases appropriately", async () => {
// Arrange
const invalidInput = {
/* invalid test data */
};
// Act & Assert
await expect(featureUnderTest(invalidInput)).rejects.toThrow();
});
});
});
Testing Guidelines
-
Test Organization
- Use descriptive
describeanditblocks - Group related tests together
- Follow the Arrange-Act-Assert pattern
- Keep tests focused on a single functionality
- Use descriptive
-
Mocking
- Mock external dependencies
- Use jest.mock() for external modules
- Create dedicated mock factories for complex objects
- Reset mocks between tests
-
Coverage
- Aim for >80% code coverage
- Test happy paths and error paths
- Include edge cases and boundary conditions
- Test asynchronous behavior correctly
-
Test Data
- Use realistic test data
- Create helper functions for test data generation
- Avoid test data duplication
- Keep test data close to tests that use it
-
Assertions
- Make assertions specific and meaningful
- Test return values, side effects, and exceptions
- Use appropriate matchers (toEqual, toBeCalledWith, etc.)
- Write custom matchers for complex assertions
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.
- yesterday First seen · 100 lines · 516 tokens per session scan A f8f9a1384ac4
testing-standards is a cursor rule published in the GitHub repository DXHeroes/mcp-devtools (13 stars, last pushed 1y ago), licensed MIT. It adds 516 tokens to every session, about $0.0026 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.
Other cursor rules, from other repositories
cursorrules
You are building an AI/ML project with Python. The project uses PyTorch for model training, handles data pipelines with proper validation, tracks experiments systematically, and follows production ML engineering practices. Code is type-hinted, tested, and reproducible.
rule
AI/ML Python development (PyTorch, scikit-learn).
language-agnostic-patterns
Language-agnostic programming patterns: SOLID, design patterns, clean code, and architecture. Load when refactoring, designing abstractions, or reviewing structure — not for everyday syntax.
cursor-tools-mastery
Cursor 3.7 runtime guide: choose the right tool, canvases, Design Mode, /worktree, /best-of-n, Await, and parallel execution where safe.
cursor-agent-orchestration
Cursor 3.7 orchestration guide: when to plan, when to delegate, nested subagents, multi-environment handoffs, /best-of-n, and Await for long-running branches.
fable5-reasoning
Fable 5 reasoning protocols: task interpretation, risk-first decomposition, approach selection, interleaved thinking, hypothesis ledgers, premortems, calibration, and the stuck-strategy ladder. Load for complex, ambiguous, or long-horizon tasks, for debugging strategy, or whenever progress stalls.