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 skills/codebygarv/ai-skills/test-generatornpx skills add codebygarv/Ai-skills --skill test-generatorgit clone --depth 1 https://github.com/codebygarv/Ai-skillsWrote 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/codebygarv/ai-skills/test-generator)<a href="https://agentmods.dev/skills/codebygarv/ai-skills/test-generator"><img src="https://agentmods.dev/badge/skills/codebygarv/ai-skills/test-generator.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 | $0.00041 | $0.00509 |
| Opus 5 | $0.00020 | $0.00254 |
| Sonnet 5 | $0.00008 | $0.00102 |
| Haiku 4.5 | $0.00004 | $0.00051 |
Grade A, and why
test-generator 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 — 37 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Generate tests that actually verify behavior — correctness under normal input, boundary conditions, and failure modes — rather than tests that exist only to move a coverage percentage.
When to Use
- Code has no tests, or existing tests don't cover the behavior that matters.
- Before refactoring, to lock in current behavior first.
- The user asks for tests for a specific function/module/component.
What to Analyze / Do
- Understand actual behavior — read the implementation (not just the function name/docstring) to know what it really does, including edge cases it may or may not handle.
- Cover the happy path — normal, expected input produces the expected output.
- Cover boundary conditions — empty input, zero, single-element, max-size, exactly-at-a-threshold.
- Cover failure modes — invalid input, missing dependencies/data, error paths — verify the function fails the way it's supposed to (throws, returns an error value, etc.), not just that it doesn't crash unpredictably.
- Cover integration points if relevant — mocked dependencies behave as expected, and the unit under test handles both success and failure responses from them.
- One behavior per test — each test should have a clear, singular reason to fail; avoid mega-tests asserting many unrelated things.
Output Format
- Tests in the project's actual testing framework/convention (ask or infer from existing test files — don't assume Jest if the project uses Vitest, etc.).
- Descriptive test names that state the behavior being verified ("returns empty array when input is empty," not "test 1").
- Grouped logically (
describeblocks or equivalent) by function/scenario. - A brief note on what's intentionally not covered and why, if anything significant was left out (e.g. requires a live network call, out of scope for unit tests).
Avoid
- Writing tests that just re-assert the implementation's own logic back at it (a test that would still pass if a bug were introduced isn't testing anything).
- Testing implementation details that aren't part of the actual contract (internal variable names, private helper call counts) instead of observable behavior.
- Generating tests purely to inflate a coverage number without verifying real behavior.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 37 lines · 41 tokens per session scan A d978768a5b66
test-generator is a skill published in the GitHub repository codebygarv/Ai-skills (24 stars, last pushed 16d ago), licensed MIT. It adds 41 tokens to every session and 509 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 2026-09-03.
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