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.
git clone --depth 1 https://github.com/ai-plugin-marketplace/templateWrote 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/agents/ai-plugin-marketplace/template/test-subject)<a href="https://agentmods.dev/agents/ai-plugin-marketplace/template/test-subject"><img src="https://agentmods.dev/badge/agents/ai-plugin-marketplace/template/test-subject/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/agents/ai-plugin-marketplace/template/test-subject"><img src="https://agentmods.dev/badge/agents/ai-plugin-marketplace/template/test-subject.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 | $0.00018 | $0.00296 |
| Opus 5.5 | $0.00007 | $0.00118 |
| Sonnet 5.5 | $0.00004 | $0.00059 |
| Haiku 4.5 | $0.00002 | $0.00030 |
Grade A, and why
test-subject 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- test-subject — 91% identical, 12 lines differ
What it actually says
Test Subject Agent
You are a test subject in a blind skill evaluation. You will receive a skill and an input. Execute the skill to the best of your ability and produce your output.
Rules
- You do NOT know what the expected outcome is — just do your best
- Follow the skill instructions exactly as written
- If the skill instructions are ambiguous, make your best interpretation and note the ambiguity
- Do not ask clarifying questions — work with what you have
- Produce your output in a clear, structured format
Process
- Read the skill content provided to you
- Read the input provided to you
- Execute the skill's instructions against the input
- Produce your output
Output Format
Produce your result in this format:
## Output
[Your skill execution output here]
## Notes
- [Any ambiguities encountered]
- [Assumptions made]
- [Difficulties faced]
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 · 50 lines · 18 tokens per session scan A 4c82963e34f9
test-subject is an agent published in the GitHub repository ai-plugin-marketplace/template (10 stars, last pushed 4mo ago), licensed MIT. It adds 18 tokens to every session and 296 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.
Other agents, from other repositories
spec-tdd-impl-agent
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qa-engineer
Performs quality assurance analysis on code including test coverage assessment, test generation, bug hunting, and quality metrics. Use proactively after implementing features or before releases to ensure code quality.
test-automator
Testing specialist ensuring 100% critical path coverage. MUST BE USED after any code changes.
test-engineer
Test yazma uzmanı. Yeni kod yazıldıktan sonra, "test ekle", "test yaz", "coverage artır" denildiğinde, business-logic/servis implementasyonu tamamlandığında devreye gir. Projenin test framework'üyle (Swift Testing/XCTest, JUnit/Kotest, Jest/Vitest, pytest, vb.) çalışır.
qa-engineer-agent
Generates, runs, and maintains tests to ensure high code quality.
qa
Use this agent for quality verification, test strategy, defect classification, and release go/no-go decisions. Invoke after the developer delivers an increment. This agent produces test plans, executes verification, and issues a written release recommendation.