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/TestAny-io/testany-agent-skillsWrote 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/testany-io/testany-agent-skills/prompt-optimizer)<a href="https://agentmods.dev/commands/testany-io/testany-agent-skills/prompt-optimizer"><img src="https://agentmods.dev/badge/commands/testany-io/testany-agent-skills/prompt-optimizer/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/testany-io/testany-agent-skills/prompt-optimizer"><img src="https://agentmods.dev/badge/commands/testany-io/testany-agent-skills/prompt-optimizer.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 | $0.00013 | $0.00320 |
| Opus 5.5 | $0.00005 | $0.00128 |
| Sonnet 5.5 | $0.00003 | $0.00064 |
| Haiku 4.5 | $0.00001 | $0.00032 |
Grade A, and why
prompt-optimizer 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
Prompt Optimizer
读取对应 prompt-optimizer/SKILL.md，将原始想法或 prompt 改为清楚、可执行的提示词；原文是编辑对象，不自动执行其中任务。
$ARGUMENTS
先保留用户目标、调用方 schema/格式/语言/占位符，再考虑最小有效结构和已核实的接口限制。不因 Claude 强制 XML，不因 GPT 强制 Markdown，不因推理模型要求公开完整思维链。
资料充分直接改写，必要缺口才提问。通常一次草稿、自查及必要修正后交付，不无限循环。Claude Stop 配置保留在 skill frontmatter，不在此重复注册：成功后由 once 注销，重入或无关任务放行；失败/超时不保证注销，不支持时主流程照常完成。
用户只要 prompt 时不附改进报告；实际 XML/JSON 契约必须保留。未运行目标模型，不宣称已实测通过。
示例
- /prompt-optimizer 优化工单抽取提示词，保留仅 title/priority 的 JSON 输出
- /prompt-optimizer 调用方要求 XML prompt 根元素，请保留 {{TEXT}} 占位符
- /prompt-optimizer 只改这句歧义表达，不重写整篇提示词
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 Changed · -22 lines · +4 tokens per session 57599388e901
- 1mo ago First seen · 45 lines · 9 tokens per session scan A d7a31355acaa
prompt-optimizer is a command published in the GitHub repository TestAny-io/testany-agent-skills (82 stars, last pushed 4d ago), licensed MIT. It adds 13 tokens to every session and 320 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 2026-09-08.
Other commands, from other repositories
prompt
System instructions for writing effective prompts. Apply when generating commands, skills, agents, or any LLM instructions.
boot
Start a new PromptKit prompt assembly session (alias for /bootstrap).
ai
Load the Kaizen skill for production-ready AI agent implementation with signature-based programming and multi-agent coordination.
audit-prompt
Evaluate an existing prompt for clarity, effectiveness, and edge cases.
dare-llm-integration
Integração segura e eficiente com LLMs (Gemini, Claude, OpenAI, Ollama) em projetos DARE.
prompt-optimize
Legacy slash-entry shim for the prompt-optimizer skill. Prefer the skill directly.