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/ai-plugin-marketplace/template/evaluategit clone --depth 1 https://github.com/ai-plugin-marketplace/templateWhat 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 | $0.00011 | $0.00158 |
| Opus 5 | $0.00005 | $0.00079 |
| Sonnet 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 2d 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.
- 2d 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 2mo ago), licensed MIT. It adds 11 tokens to every session and 158 once invoked, about $0.0001 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 2026-08-31.
Other commands, from other repositories
agent-build-feature
A strict brief that makes a coding agent implement a feature end to end, matching the codebase and verifying it works.
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.
agent-tool-definition
Write a tool/function definition for an AI agent with a description that steers use and parameters it fills correctly.
agent-complete-task
A strict, goal-focused brief that makes a coding agent (Claude Code, Cursor, Antigravity) finish a task correctly and verify it.
agent-system-prompt
Write a system prompt for an AI agent that defines its role, tools, boundaries, and output clearly enough to act reliably.
agent-workflow-design
Design a multi-agent workflow with the right pattern, explicit handoffs, and human gates on consequential actions.