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/rjmurillo/ai-agents/testgit clone --depth 1 https://github.com/rjmurillo/ai-agentsWrote 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/rjmurillo/ai-agents/test)<a href="https://agentmods.dev/commands/rjmurillo/ai-agents/test"><img src="https://agentmods.dev/badge/commands/rjmurillo/ai-agents/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 | $0.00031 | $0.02146 |
| Opus 5 | $0.00015 | $0.01073 |
| Sonnet 5 | $0.00006 | $0.00429 |
| Haiku 4.5 | $0.00003 | $0.00215 |
Grade C, and why
test scanned grade C with 1 finding 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 today.
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.
Hidden instructionshighPrompt injection
Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.
Invoke Skill(skill="orphan-ref-validator"). Reject the gate on `VERDICT: CRITICAL_FAIL` or `VERDICT: ERROR`; `VERDICT: WARN` is non-blocking and surfaces in the test summary. This mirrors `/build` Mandatory Exit Gate 4 ( How it starts
The opening of the file, as written. The whole thing — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
@CLAUDE.md
Test: $ARGUMENTS
If $ARGUMENTS is empty, test the current branch diff against the base branch.
Cross-Harness Hook Routing
If the diff touches Claude Code or GitHub Copilot CLI hooks, dispatchers, generated shims, event translation, or hook output:
- Invoke
Skill(skill="agent-harness-reference")to load the pinned contract. - Run the verification path in
Skill(skill="ai-agents-portability-campaign"). - Require unit coverage for translation plus a real-harness smoke test for each affected harness.
- Treat documentation silence as an unknown to probe, not permission to guess.
Step 0: Classify PR Type
Detect the base branch from gh pr view --json baseRefName or fall back to main. Run git diff origin/<base-branch> --name-only and classify changed files:
| Type | Patterns | Gates to Run |
|---|---|---|
| CODE | *.py, *.ps1, *.ts, *.js, *.cs |
All 6 gates |
| WORKFLOW | *.yml in .github/workflows/ |
Gates 1, 3, 4 |
| CONFIG | *.json, *.yaml (non-workflow) |
Gates 3, 4 |
| DOCS | *.md, *.txt, *.rst |
Gate 5 only |
| MIXED | Combination | Apply per-file rules |
Print: PR TYPE: [type]. Running gates: [list].
Skip non-applicable gates. Do not waste agent invocations on irrelevant dimensions.
Gate 1: Functional Testing
Invoke Skill(skill="code-qualities-assessment") for quality baseline.
Task(subagent_type="qa"): You are a senior QA engineer. Your job is to catch issues that will cause production incidents. Be skeptical. Cite specific file:line evidence for every finding. Evaluate:
- Unit coverage - Each method in isolation, dependencies injected. Every new function has at least 1 test.
- Integration coverage - Contracts between components verified. Cross-module boundaries exercised.
- Acceptance coverage - Each requirement has a passing test. Map to acceptance criteria from /spec output.
- Edge cases - Null/empty/boundary values, invalid types, concurrent access where applicable.
- Error paths - Every catch/error branch tested. No silent swallowing. Resources cleaned up on failure.
- Regression risk - High-risk areas (auth, data persistence, payments) require full coverage regardless of change size.
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.
- today First seen · 159 lines · 31 tokens per session scan C 74281fbfcba9
test is a command published in the GitHub repository rjmurillo/ai-agents (45 stars, last pushed today), licensed MIT. It adds 31 tokens to every session and 2,146 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it C with 1 finding (hidden instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other commands, from other repositories
load-session
Load a synced session summary for context.
statusline
Configure which sections appear in the Claude Code statusline.
02_execute
Execute a plan (auto-moves pending to in-progress) with Ralph Loop TDD pattern.
999_release
Bump version, create git tag, and create GitHub release for plugin distribution.
add-integration
Add support for a new service (like Slack, Notion, or GitHub) with full OAuth and API integration.
update-model-rubric
Research the current LLM landscape and update Carmenta's model routing rubrics with latest models, capabilities, and pricing.