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 commands/jsk9999/ai-nexus/testgit clone --depth 1 https://github.com/JSK9999/ai-nexusWrote 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/commands/jsk9999/ai-nexus/test)<a href="https://agentmods.dev/commands/jsk9999/ai-nexus/test"><img src="https://agentmods.dev/badge/commands/jsk9999/ai-nexus/test.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 | $0.00004 | $0.00288 |
| Opus 5 | $0.00002 | $0.00144 |
| Sonnet 5 | $0.00001 | $0.00058 |
| Haiku 4.5 | $0.00000 | $0.00029 |
Grade A, and why
test 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 4d 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.
What it actually says
/test
When asked to write tests:
- Read the source — Understand what the code does before writing tests
- Identify cases — Happy path, edge cases, error cases
- Write tests — Use AAA pattern (Arrange, Act, Assert)
- Run tests — Verify they pass
- Check coverage — Ensure critical paths are covered
Test Structure
describe('functionName', () => {
it('should return X when given Y', () => {
// Arrange
const input = createInput();
// Act
const result = functionName(input);
// Assert
expect(result).toBe(expected);
});
it('should throw when input is invalid', () => {
expect(() => functionName(null)).toThrow();
});
});
What to Cover
- Normal inputs → expected outputs
- Empty/null/undefined inputs
- Boundary values (0, -1, MAX)
- Error conditions and exceptions
- Async operations (resolve and reject)
Rules
- One assertion per test (when practical)
- No test interdependence — each test runs independently
- Use descriptive names: "should [expected] when [condition]"
- Don't test private/internal methods directly
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.
- 4d ago First seen · 48 lines · 4 tokens per session scan A e36e62281bb4
test is a command published in the GitHub repository JSK9999/ai-nexus (19 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 4 tokens to every session and 288 once invoked, about $0.0000 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 commands, from other repositories
specify
Create or update the feature specification from a natural language feature description.
speckit.assess.intake
Capture and normalize a raw idea (text, URL, ticket, or codebase pointer) into an intake note.
eval
Evaluate and improve one healthcare agent's system prompt. Run up to 5 iterations of: prepare fixed questions -> answer -> judge -> improve -> re-score -> commit if better.
csm-workledger
Show the surviving work-ledger entries across sessions.
scaffold-python-package
Scaffold a new Python package following PEP 621, PDM, Black/isort/Flake8, type hints, and Loguru logging.
import
Import Spec Kit artifacts into local Dotdog knowledge graphs.