Borrowing it
Nothing to install: this file belongs to adarshp14/relay. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/adarshp14/relay/main/.agents/skills/wrdn-effect-vitest-tests/SKILL.mdgit clone --depth 1 https://github.com/adarshp14/relayWrote 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/adarshp14/relay/wrdn-effect-vitest-tests)<a href="https://agentmods.dev/skills/adarshp14/relay/wrdn-effect-vitest-tests"><img src="https://agentmods.dev/badge/skills/adarshp14/relay/wrdn-effect-vitest-tests.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 5.1 | $0.00029 | $0.00200 |
| Opus 5 | $0.00015 | $0.00100 |
| Sonnet 5 | $0.00006 | $0.00040 |
| Haiku 4.5 | $0.00003 | $0.00020 |
Grade A, and why
wrdn-effect-vitest-tests 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 5d 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.
This is a copy
100% identical to wrdn-effect-vitest-tests — 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.
What it actually says
Use @effect/vitest for tests in this repo.
Fix Shape
- Import
describe,it,expect, and helpers from@effect/vitest. - Import utility helpers from
@effect/vitest/utilswhen needed. - Do not import from raw
vitestexcept in config or tooling files. - Do not put
expect(...)behindif, ternary, logical, or switch branches. - Split conditional behavior into separate tests, or assert the branch condition and expected value explicitly.
Bad
if (result.ok) {
expect(result.value).toBe("x");
}
Good
expect(result).toEqual({ ok: true, value: "x" });
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.
- 5d ago First seen · 30 lines · 29 tokens per session scan A d3835d20da79
wrdn-effect-vitest-tests is a skill published in the GitHub repository adarshp14/relay (0 stars, last pushed 2mo ago), licensed MIT. It adds 29 tokens to every session and 200 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to wrdn-effect-vitest-tests, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
llm-as-judge-evaluation
Evaluate LLM outputs using frontier models as judges. Use for pairwise model comparison, quality scoring with custom rubrics, and automated evaluation pipelines. Covers position bias mitigation, statistical significance, and generating preference data for DPO/RLHF.
langsmith-observability
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
impl-validator
Validate whether an implementation matches its stated goal. Use this skill when a skill or agent wants a second opinion on its own output, when the user says "check this implementation", "validate what you did", "is this correct?", "review the output", or "did you do this right?". Also spawned automatically as a…
performing-graphql-introspection-attack
Performs GraphQL introspection attacks to extract the full API schema including types, queries, mutations, subscriptions, and field definitions from GraphQL endpoints. The tester uses introspection queries to map the attack surface, identifies sensitive fields and mutations, tests for query depth and complexity…
performing-api-fuzzing-with-restler
Uses Microsoft RESTler to perform stateful REST API fuzzing by automatically generating and executing test sequences that exercise API endpoints, discover producer-consumer dependencies between requests, and find security and reliability bugs. The tester compiles an OpenAPI specification into a RESTler fuzzing…
implementing-api-security-testing-with-42crunch
Implement comprehensive API security testing using the 42Crunch platform to perform static audit and dynamic conformance scanning of OpenAPI specifications.