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 breeze-r/claude-prompt-craft-skill --skill prompt-craftgit clone --depth 1 https://github.com/breeze-r/claude-prompt-craft-skillWrote 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/breeze-r/claude-prompt-craft-skill/prompt-craft)<a href="https://agentmods.dev/skills/breeze-r/claude-prompt-craft-skill/prompt-craft"><img src="https://agentmods.dev/badge/skills/breeze-r/claude-prompt-craft-skill/prompt-craft/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/breeze-r/claude-prompt-craft-skill/prompt-craft"><img src="https://agentmods.dev/badge/skills/breeze-r/claude-prompt-craft-skill/prompt-craft.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 | $0.00123 | $0.02361 |
| Opus 5 | $0.00062 | $0.01180 |
| Sonnet 5 | $0.00025 | $0.00472 |
| Haiku 4.5 | $0.00012 | $0.00236 |
Grade A, and why
prompt-craft 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 12d 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 — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Craft — 提示词设计与优化
把用户的意图变成一条清晰、可复用、拿来就能用的 prompt。核心方法论来自实践验证的四要素框架:目标(Goal)、上下文(Context)、输出(Output)、边界(Boundaries)。
一条好 prompt 不需要技术语法或固定套路。只在有帮助时才补充要素——不是每项都要填。
工作流程
当用户要求写或优化 prompt 时,按以下步骤:
1. 判断任务类型
| 类型 | 特征 | 对应模板 |
|---|---|---|
| 日常型 | 提问、找点子、草稿、比较、小计划 | 短 prompt,1-3 句即可 |
| 交付型 | 多来源/多步骤、产出文件、给他人看 | 完整四要素 + 终检 |
| 代码型 | 处理代码、修 bug、写测试、重构 | 行为 + 复现/定位 + 约束 + 验证方式 |
| Agent/系统型 | 系统提示词、自动化任务、定时执行 | 四要素 + 审批边界 + 失败处理 |
2. 快速补齐缺口
如果用户给的信息不足以确定关键要素,最多问一个最重要的问题(受众?用途?不能动什么?)。信息基本够时直接动手,把假设写在 prompt 旁边标注,不要连环追问。
3. 产出 prompt
- 把最终 prompt 放在代码块里,方便复制
- prompt 用用户的目标语言写(给英文模型/英文场景用英文,日常用用户的语言)
- 附 2-4 句说明:为什么这样组织、哪里可以按需删改
- 复杂任务可以给 2 个版本:精简版 + 完整版
4. 优化已有 prompt 时
先诊断再改写。常见病灶按优先级检查:
- 结果不清:写了一堆步骤,但没说要什么结果、给谁用 → 重写为结果导向
- 上下文噪音:塞了不相关的来源/背景 → 只留会改变结果的信息
- 边界缺失:没说什么不能动、什么要先批准 → 补 1-2 条最要紧的
- 过度控制:把每一步都写死,模型没有调整空间 → 删掉过程性指令,保留约束
- 无验证:重要任务没有终检要求 → 加一条收尾自查
改写后用一句话说明改了什么、为什么。
核心原则
从结果说起,不从步骤说起
描述要产出什么、给谁看。只有当过程本身重要时才描述过程,否则给模型留出搜索、比对、调整的空间。
弱:先读会议记录,然后提取要点,然后分类整理,然后写成报告…… 强:把这些会议记录整理成一份给项目组的简短进展通报。决策和下一步行动放最前面。
上下文只加会改变结果的
- 每个来源说明该取什么,不要只把文件丢过去
- 图片要指出关键区域
- 依赖最新信息就要求联网搜索并给出来源
- 相关任务需要共享文件时用 project 组织
边界防的是"真麻烦"
边界不求多,盯住 1-2 条最要紧的:改错就报废的细节,以及影响他人之前该先过目的动作。
常用边界句式库:
- 已批准的日期和预算数字保持不动
- 只用我提供的来源,信息缺了就标出来,不要猜
- 建议控制在说好的预算之内
- 准备成草稿,不要发送
- 发送、发布或修改别人依赖的信息之前,必须经我批准
- 不要改 API 的形状 / 公开接口保持稳定
- 用户可见的行为不能变
说清用途,模型才能选对形态
受众和用途决定长度、详细度、组织方式:
- "做成一页纸摘要,让总监开会前扫一眼就行"
- "整理成跟进邮件,写清决策、负责人和截止日期"
- "做对照表,差异超过 10% 的都标出来"
重要任务要终检
收尾前让模型自查一次:
- 确认每个行动项都有负责人和截止日期
- 把没法核实的信息标出来
- 检查多个产出物之间口径一致
第一版不必完美,靠追问收敛
告诉用户:审第一版结果 → 说出具体改动("开头更直接、证据保留、建议挪到背景前面")→ 不用从头再来。可以补来源、纠方向、要备选方案、调详细度。跑通的 prompt 就沉淀下来复用。
场景模板
日常型(短 prompt)
[要什么结果] + [关键条件] + [格式/长度]
示例:
- 给一个从没投资过的人解释复利。用一个具体例子,引入的术语都要定义。
- 起草一封友好的邮件婉拒这个邀请,理由是我届时出差。120 词以内,给以后的活动留口子。
- 帮一个每年出国两次的人比较这两个套餐。差异用表格,然后推荐一个并说清代价。
交付型(完整四要素)
[目标] 为 [受众/场合] 准备 [交付物]。
[上下文] 用 [来源A] 里的 [什么],加上 [来源B] 里的 [什么]。
[输出] [结构要求:什么放最前面、包含哪些部分、长度]。
[边界] [1-2 条不能动/要批准的]。
[终检] 收尾前,检查 [具体自查项]。
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.
- 12d ago First seen · 190 lines · 123 tokens per session scan A 5d8d60c38045
prompt-craft is a skill published in the GitHub repository breeze-r/claude-prompt-craft-skill (11 stars, last pushed 2mo ago), licensed MIT. It adds 123 tokens to every session and 2,361 once invoked, about $0.0006 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 2026-08-30.
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