Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/ksachdeva/zephyr-rtos-ainpx agentmods add skills/ksachdeva/zephyr-rtos-ai/zephyr-testingWrote 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/ksachdeva/zephyr-rtos-ai/zephyr-testing)<a href="https://agentmods.dev/skills/ksachdeva/zephyr-rtos-ai/zephyr-testing"><img src="https://agentmods.dev/badge/skills/ksachdeva/zephyr-rtos-ai/zephyr-testing.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 | $0.00112 | $0.02333 |
| Opus 5 | $0.00056 | $0.01167 |
| Sonnet 5 | $0.00022 | $0.00467 |
| Haiku 4.5 | $0.00011 | $0.00233 |
Grade A, and why
zephyr-testing 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 7d 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 — 341 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Zephyr Testing Skill
Expert guidance for testing Zephyr RTOS applications using Ztest framework, Twister test runner, and FFF mocking.
Table of Contents
- Quick Start
- Test Project Structure
- Ztest Framework Basics
- Running Tests with Twister
- Advanced Topics
- References
Quick Start
Minimal Integration Test
tests/foo/bar/
├── CMakeLists.txt
├── prj.conf
├── testcase.yaml
└── src/
└── main.c
CMakeLists.txt:
cmake_minimum_required(VERSION 3.20.0)
find_package(Zephyr REQUIRED HINTS $ENV{ZEPHYR_BASE})
project(my_test)
target_sources(app PRIVATE src/main.c)
prj.conf:
CONFIG_ZTEST=y
testcase.yaml:
tests:
mysubsystem.feature.basic:
tags: feature
platform_allow:
- native_sim
integration_platforms:
- native_sim
src/main.c:
#include <zephyr/ztest.h>
ZTEST_SUITE(my_suite, NULL, NULL, NULL, NULL, NULL);
ZTEST(my_suite, test_example)
{
zassert_true(1, "1 was false");
zassert_equal(2 + 2, 4, "math is broken");
}
Run:
./scripts/twister -T tests/foo/bar/ -p native_sim
Minimal Unit Test
Unit tests compile only the module under test (no full Zephyr OS).
CMakeLists.txt:
cmake_minimum_required(VERSION 3.20.0)
project(app)
find_package(Zephyr COMPONENTS unittest REQUIRED HINTS $ENV{ZEPHYR_BASE})
target_sources(testbinary PRIVATE main.c)
testcase.yaml:
tests:
mymodule.unit:
tags: unit
type: unit
Run:
./scripts/twister -T tests/unit/mymodule/
Test Project Structure
Naming Conventions
| Element | Convention | Example |
|---|---|---|
| Test scenario ID | subsystem.component[.variant] |
kernel.semaphore.stress |
| Ztest suite name | snake_case, descriptive | semaphore_tests |
| Test function | test_<what_is_tested> |
test_sem_give_take |
What ships with it
4 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.
- 7d ago First seen · 341 lines · 112 tokens per session scan A d943cca8b20d
zephyr-testing is a skill published in the GitHub repository ksachdeva/zephyr-rtos-ai (23 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 112 tokens to every session and 2,333 once invoked, about $0.0006 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 skills, from other repositories
zunit
Generate and run zunit tests for java-cli-app projects. Use when asked to create tests, write tests, add tests, or generate test files for a java-cli-app project. Triggers on "zunit", "write tests", "create tests", "add tests", "test this", "generate tests", or requests to test a java-cli-app application. Also trigger…
continuous-testing
Continuous test-driven development loop — after every code change, builds the project, starts the server, and runs Unit Tests, Integration Tests, and System Tests. Applies on top of microprofile-server skill. Use during development when you want full verification after each change. Triggers on "continuous testing"…
test-writer
How to write pytest tests for modules in this workspace. Load whenever you are about to write or extend tests.
testing-llm
LLM and AI testing patterns — mock responses, evaluation with DeepEval/RAGAS, structured output validation, and agentic test patterns (generator, healer, planner). Use when testing AI features, validating LLM outputs, or building evaluation pipelines.
testing-patterns
Redirect — testing-patterns was split into 5 focused sub-skills. Use when looking for testing-patterns, writing tests, or test automation. Redirects to testing-unit, testing-e2e, testing-integration, testing-llm, or testing-perf.
deepeval
Use when discussing or working with DeepEval (the python AI evaluation framework).