SkillKit is a package manager for AI coding-agent skills that installs skills from many sources and translates them between the different formats used by coding agents. It helps developers share skills across agents such as Claude Code, Cursor, Copilot, and Codex.
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 rohitg00/skillkit --skill anti-patternsgit clone --depth 1 https://github.com/rohitg00/skillkitWrote 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/rohitg00/skillkit/anti-patterns)<a href="https://agentmods.dev/skills/rohitg00/skillkit/anti-patterns"><img src="https://agentmods.dev/badge/skills/rohitg00/skillkit/anti-patterns/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/rohitg00/skillkit/anti-patterns"><img src="https://agentmods.dev/badge/skills/rohitg00/skillkit/anti-patterns.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Data Exfiltration · line 176 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00095 | $0.01997 |
| Opus 5.5 | $0.00038 | $0.00799 |
| Sonnet 5.5 | $0.00019 | $0.00399 |
| Haiku 4.5 | $0.00010 | $0.00200 |
Grade A, and why
testing-anti-patterns 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 1mo 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 — 282 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Testing Anti-Patterns
You are identifying and avoiding common testing anti-patterns.
Review Workflow
Follow these steps when reviewing test code:
- Run tests in isolation — Verify each test passes independently (no shared state, no ordering dependency).
- Check for patterns below — Scan for each anti-pattern in the checklist; flag every match with the specific defect.
- Apply refactoring strategy — Use the refactoring strategies section to select and apply the appropriate fix.
- Verify the test still fails when code breaks — After fixing, confirm the corrected test catches real regressions (remove or stub the implementation to confirm a failure occurs).
Critical Anti-Patterns
1. The Liar - Tests That Always Pass
Problem: Test passes even when the code is broken.
// BAD - Always passes because it tests nothing meaningful
it('should process data', () => {
const result = processData(input);
expect(result).toBeDefined(); // Too weak
});
// GOOD - Actually verifies behavior
it('should transform input to uppercase', () => {
const result = processData({ text: 'hello' });
expect(result.text).toBe('HELLO');
});
Detection: Remove or break the implementation - test should fail.
2. The Giant - Tests Too Large
Problem: Single test covers too many behaviors.
// BAD - Tests multiple things
it('should handle user registration', async () => {
const user = await register(userData);
expect(user.id).toBeDefined();
expect(user.email).toBe(userData.email);
expect(user.password).toBeUndefined();
expect(sendEmail).toHaveBeenCalled();
expect(createProfile).toHaveBeenCalled();
// ... 20 more assertions
});
// GOOD - Focused tests
it('should create user with provided email', async () => {
const user = await register(userData);
expect(user.email).toBe(userData.email);
});
it('should send welcome email on registration', async () => {
await register(userData);
expect(sendEmail).toHaveBeenCalledWith(
expect.objectContaining({ type: 'welcome' })
);
});
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.
- 1mo ago First seen · 282 lines · 95 tokens per session scan A 296b4de35bb6
testing-anti-patterns is a skill published in the GitHub repository rohitg00/skillkit (1,556 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 95 tokens to every session and 1,997 once invoked, about $0.0004 per session on Opus 5.5. 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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