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/commands/ai-plugin-marketplace/template/evaluate)<a href="https://agentmods.dev/commands/ai-plugin-marketplace/template/evaluate"><img src="https://agentmods.dev/badge/commands/ai-plugin-marketplace/template/evaluate/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/ai-plugin-marketplace/template/evaluate"><img src="https://agentmods.dev/badge/commands/ai-plugin-marketplace/template/evaluate.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 | $0.00011 | $0.00158 |
| Opus 5.5 | $0.00004 | $0.00063 |
| Sonnet 5.5 | $0.00002 | $0.00032 |
| Haiku 4.5 | $0.00001 | $0.00016 |
Grade A, and why
evaluate 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:
- evaluate — 100% identical, 0 lines differ
What it actually says
Evaluate the skill at $ARGUMENTS.skill-path using the test cases at $ARGUMENTS.test-cases-path.
Use the evaluate-skill skill to orchestrate the evaluation. This will:
- Run the skill with blind test-subject agents at opus, sonnet, and haiku tiers
- Compare outputs against expected outcomes
- Generate a refinement report with specific recommendations
Report the results when complete.
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 · 22 lines · 11 tokens per session scan A 624fd6a54529
evaluate is a command published in the GitHub repository ai-plugin-marketplace/template (10 stars, last pushed 4mo ago), licensed MIT. It adds 11 tokens to every session and 158 once invoked, about $0.0000 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 commands, from other repositories
spec-impl
Execute spec tasks using TDD methodology.
kiro-validate-impl
Command "kiro-validate-impl" from gotalab/cc-sdd, covering implementation validation, core task, execution steps, 1. detect validation target and 2. load context.
kiro-spec-impl
Command "kiro-spec-impl" from gotalab/cc-sdd, covering implementation task executor, core task, execution steps, step 1: load context and step 2: select tasks.
tdd
Guide a Test-Driven Development workflow for the current task.
tdd
TDD workflow - red-green-refactor per docs/methodology/tdd.md. Use when asked to '/tdd', 'TDD workflow', 'red-green-refactor', 'test-first development', 'write the failing test first'.
agent-eval-design
Design an evaluation for an AI agent or LLM feature: what to test, how to grade it, and how to catch regressions.