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 skills add ryanzhao1011/workframe --skill prompt-evaluationgit clone --depth 1 https://github.com/ryanzhao1011/workframeWrote 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/skills/ryanzhao1011/workframe/prompt-evaluation)<a href="https://agentmods.dev/skills/ryanzhao1011/workframe/prompt-evaluation"><img src="https://agentmods.dev/badge/skills/ryanzhao1011/workframe/prompt-evaluation/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/skills/ryanzhao1011/workframe/prompt-evaluation"><img src="https://agentmods.dev/badge/skills/ryanzhao1011/workframe/prompt-evaluation.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 | $0.00137 | $0.01672 |
| Opus 5.5 | $0.00055 | $0.00669 |
| Sonnet 5.5 | $0.00027 | $0.00334 |
| Haiku 4.5 | $0.00014 | $0.00167 |
Grade A, and why
prompt-evaluation 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 25d 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.
How it starts
The opening of the file, as written. The whole thing — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt 评估技能
适用场景
- 评估单个 Prompt 的输出质量
- 对比多个候选 Prompt，选出最优
- 验证 Prompt 迭代后的效果（前后对比）
- 定期回归验证，防止 Prompt 效果衰减
五步流程
第 1 步：评估维度定义
根据 Prompt 场景选择评估维度（可叠加）：
| 维度 | 评估问题 | 适用场景 |
|---|---|---|
| 准确性 | 输出内容是否事实正确、任务完成度高？ | 信息类、生成类 |
| 完整性 | 是否覆盖了所有要求的要素？ | 结构化输出 |
| 一致性 | 相同输入是否产出相似输出？ | 稳定性要求高的场景 |
| 可读性 | 输出是否清晰易懂、格式合理？ | 面向终端用户 |
| 相关性 | 输出是否贴合用户意图？ | 问答、推荐 |
| 安全性 | 是否规避了违规/有害/越权内容？ | 全部 |
| 成本 | 消耗的 token 数、延迟 | 高频调用场景 |
| 鲁棒性 | 对边界/对抗输入的处理能力 | 高安全要求 |
每个维度定义 1-5 分的评分标准。
第 2 步：测试样本设计
设计代表性测试样本集：
| 样本类别 | 占比 | 用途 |
|---|---|---|
| 正常样本（Typical） | 50% | 覆盖主流场景 |
| 边界样本（Edge） | 30% | 测试边界处理 |
| 对抗样本（Adversarial） | 20% | 测试安全性和鲁棒性 |
样本集规模建议：
- 快速验证：10-20 个样本
- 标准评估：30-50 个样本
- 深度回归：100+ 个样本
每个样本包含：
sample_id: S-001
category: typical | edge | adversarial
input:
variables:
user_input: "{具体输入}"
style_tone: "neutral"
expected_characteristics:
- "应包含 X"
- "不应包含 Y"
- "格式应符合 Z"
第 3 步：对比测试执行
| 对比模式 | 说明 |
|---|---|
| 单 Prompt 评估 | 跑一个 Prompt，打分判断是否达标 |
| A/B 对比 | 两个候选 Prompt 在同一样本集上对比 |
| 版本回归 | 新版 Prompt vs 旧版 Prompt，防止退步 |
| 多候选排序 | 3+ 个候选 Prompt 排序选最优 |
执行时注意：
- 固定 temperature / top_p 等参数，保证可复现
- 每个样本至少运行 3 次（减少随机性影响）
- 记录完整输入/输出/耗时/token
第 4 步：量化评分
按维度为每个输出打分（1-5），计算综合得分：
sample_id: S-001
prompt_version: v1.2
scores:
accuracy: 4
completeness: 5
consistency: 4
readability: 5
safety: 5
overall: 4.6 # 加权平均或简单均值
cost:
input_tokens: 150
output_tokens: 320
latency_ms: 1200
汇总到评估矩阵：
| 样本 | Prompt A | Prompt B | 胜出 |
|---|---|---|---|
| S-001 | 4.6 | 4.2 | A |
| S-002 | 3.8 | 4.5 | B |
| ... | ... | ... | ... |
| 平均 | 4.2 | 4.1 | A |
第 5 步：上线建议
基于评分给出明确建议：
| 结果 | 建议 | 后续动作 |
|---|---|---|
| 新版显著优于旧版（>10%） | ✅ 上线 | 灰度发布 → 全量 |
| 新版略优（2-10%） | ⚠️ 谨慎上线 | 小流量 A/B 验证 |
| 效果持平（±2%） | 🔄 不上线 | 继续优化或保持现状 |
| 新版变差 | ❌ 回退 | 分析原因，重新设计 |
| 某维度显著回退 | ❌ 回退 | 即使综合分更高也回退 |
输出模板
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.
- 25d ago Changed · -4 lines · +98 tokens per session f4d9371241c2
- 1mo ago First seen · 170 lines · 39 tokens per session scan A f47079caeaa5
prompt-evaluation is a skill published in the GitHub repository ryanzhao1011/workframe (7 stars, last pushed 26d ago), licensed MIT. It adds 137 tokens to every session and 1,672 once invoked, about $0.0005 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.
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