Borrowing it
Nothing to install: this file belongs to Insight-Services-APAC/ingenious. 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/Insight-Services-APAC/ingenious/main/.claude/commands/quality/js-quality/ltest.mdgit clone --depth 1 https://github.com/Insight-Services-APAC/ingeniousWrote 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/insight-services-apac/ingenious/ltest)<a href="https://agentmods.dev/commands/insight-services-apac/ingenious/ltest"><img src="https://agentmods.dev/badge/commands/insight-services-apac/ingenious/ltest/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/commands/insight-services-apac/ingenious/ltest"><img src="https://agentmods.dev/badge/commands/insight-services-apac/ingenious/ltest.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00000 | $0.00044 |
| Opus 5.5 | $0.00000 | $0.00018 |
| Sonnet 5.5 | $0.00000 | $0.00009 |
| Haiku 4.5 | $0.00000 | $0.00004 |
Grade A, and why
ltest 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.
What it actually says
Run npm test and npm run lint and debug iteratively until there are no more test, formatting, or linting errors. Do not implement any slow tests or remove slow tests if you encounter them.
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 · 2 lines · 0 tokens per session scan A be68f1ea84a6
ltest is a command published in the GitHub repository Insight-Services-APAC/ingenious (25 stars, last pushed 9mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 44 tokens. 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 2026-09-04.
Other commands, from other repositories
debug
Structured bug diagnosis and fixing workflow that reproduces, diagnoses root cause, applies a minimal fix, writes regression tests, and scans for similar patterns.
create-repro
Isolate a bug report into a minimal, standalone reproduction script or test case.
debug
Use when a bug needs systematic diagnosis before a fix is attempted.
orch-fix-defect
Orchestrate fixing a bug — reproduce it as a failing regression test, fix to green, review, gated commit. Wrapper for the orch-fix-defect skill.
root-cause
Use when any test fails, bug appears, or behaviour surprises you, before proposing a fix - find the cause and prove it, by reading real evidence, tracing bad values back to their origin, comparing against a working case, and testing one hypothesis at a time.
api-aqa-flow-execution-and-report-analysis
Phase 6 Execution & Report Analysis of api-aqa-flow (USER INTERACTION REQUIRED).