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 WiseWong6/wise-skills --skill prompt-creatorgit clone --depth 1 https://github.com/WiseWong6/wise-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/skills/wisewong6/wise-skills/prompt-creator)<a href="https://agentmods.dev/skills/wisewong6/wise-skills/prompt-creator"><img src="https://agentmods.dev/badge/skills/wisewong6/wise-skills/prompt-creator/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/wisewong6/wise-skills/prompt-creator"><img src="https://agentmods.dev/badge/skills/wisewong6/wise-skills/prompt-creator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00061 | $0.01378 |
| Opus 5 | $0.00030 | $0.00689 |
| Sonnet 5 | $0.00012 | $0.00276 |
| Haiku 4.5 | $0.00006 | $0.00138 |
Grade A, and why
prompt-creator 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 yesterday.
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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Creator
可配置项
作者、模型、更新说明和版本号都是可选顶部信息。用户未提供、当前提示词也没有时,省略对应字段,不写空值、占位符或猜测值;全部缺省时,整个头部省略。
先给可直接使用的完整提示词,再在代码块外简短询问是否需要补充顶部信息;已提供或已明确不需要时不重复询问。顶部信息不影响正文交付,不作为开始修改或保存的前置条件。
保存仅在用户要求或已有保存授权时执行;未指定保存位置时可使用下方默认目录,不为可选配置阻塞正文交付。已有作者设置可沿用。
| 配置 | 默认值 | 用途 |
|---|---|---|
PROMPT_AUTHOR |
不填则省略 | 可选头部的 Author 字段 |
PROMPT_SAVE_ROOT |
~/Documents/prompts |
提示词文件保存根目录 |
质量内核
提示词是给模型执行的代码。目标是让接口清晰、依赖最少、职责单一、没有隐藏分支。
默认不要引入框架名、重度增强模板、技巧堆叠或特殊模块。只有当添加结构能减少复杂度、减少歧义或提高复用性时才加入。
按任务需要选择步骤和判断标准,不把“是否开启深度思考”作为前置问题。模型信息未知时先写通用可执行版本;用户明确提供模型能力或任务确有依赖时,再针对性调整。需要判断、比较或取舍时写清标准,只要求结论、理由摘要和可验证结果,不要求输出完整思考过程。
接口字段
按需使用这些字段;空字段省略,不用占位。
| 字段 | 作用 | 代码类比 |
|---|---|---|
| Task | 要完成的目标 | function name |
| Context | 必要背景和依赖 | closure vars |
| Input | 用户会提供什么 | params |
| Output | 必须返回什么 | return type |
| Constraints | 必须、禁止、边界 | type constraints |
| Success | 合格标准 | tests |
角色、语气、步骤、示例只在必要时添加。角色不能替代任务;语气不能替代输出要求;示例不能替代规则。
工作流
- 根据用户目标确定需要的输入、输出、步骤和判断标准,不为可选模型信息暂停。
- 收集最少必要字段。缺少关键信息才提问;不影响初版质量的偏好先用保守默认。
- 写提示词骨架,先保证主路径可执行,再补边界和失败处理。
- 加示例时只加能抽象方法的示例:它必须说明格式、边界或风格中的至少一项。
- 自检后先输出干净版完整提示词,再按需询问是否补充顶部信息;只有用户要求解释时,额外给说明版。
- 用户要求保存时再写文件;已有保存授权直接执行。未指定文件名时使用
${PROMPT_SAVE_ROOT}/<task-slug>/prompt.md,不要为了文件名强加版本头部。
自检清单
输出前必须检查并修正:
- MECE:指令是否互斥且覆盖主路径、边界和失败情况。
- 冲突:目标、约束、输出格式、优先级是否互相矛盾。
- 冗余:是否用不同说法重复同一条规则。
- 歧义:术语、输入范围、成功标准是否可执行。
- 范围膨胀:是否因为小需求添加了不必要实体、模块或流程。
- 示例质量:示例是否提炼方法,而不是给提示词打补丁。
- 注释污染:是否出现括号式补丁说明或营销式强调词。
- 可维护性:未来改一个规则时,是否能只改一个段落。
输出契约
默认用单一代码块输出完整 Markdown 提示词;以下正文结构按需取用,空章节省略:
# <标题>
## 任务
<可执行、可衡量的目标>
## 背景
<仅保留完成任务必需的信息>
## 输入
<输入格式、字段、来源或占位符>
## 输出
<输出格式、结构、长度、顺序>
## 约束
<必须、禁止、边界、优先级>
## 成功标准
<自检项或验收条件>
若生成执行型提示词,步骤必须确定、少分支。若生成判断型提示词,必须写清判断标准和输出哪些理由摘要,但不要要求输出完整思考过程。
需要顶部信息时,只添加已提供的字段;用户授权补写时再生成更新说明或版本号,不默认添加 Initial version。
可选 Git 管理
保存提示词文件后,可以提示用户是否需要 Git 记录;Git 不是默认流程,也不是强制项。
- 只有用户明确要求 Git 记录时,才执行 Git 命令。
- 先运行
git status确认改动范围。 - 若用户确认提交,使用
git add <具体文件>;不要默认使用git add .。 - Commit message 建议:
prompt(<task-slug>): v<version> - <update summary>。 - Push 不是默认步骤;只有用户明确要求 push 时才执行,并先说明目标 remote 和 branch。
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- yesterday Changed · -6 lines 35667dcd8c86
- 11d ago First seen · 120 lines · 61 tokens per session scan A fd60df5ca88b
prompt-creator is a skill published in the GitHub repository WiseWong6/wise-skills (6 stars, last pushed 2d ago), licensed MIT. It adds 61 tokens to every session and 1,378 once invoked, about $0.0003 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 skills, from other repositories
llm-app-patterns
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
prompt-optimization
Improve a prompt on the evaluations workbench through a measured loop. Score the baseline first, then duplicate the target column, form a hypothesis from failing rows, edit the copy's prompt draft, run, compare pass rate and cost, and repeat until the numbers hold. Use when the user asks to optimize or improve a…
enhance-prompt
Transforms vague UI ideas into polished, Stitch-optimized prompts. Enhances specificity, adds UI/UX keywords, injects design system context, and structures output for better generation results.
prompt-engineer
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot…
seedance-vocab-en
This skill should be used when an English Seedance 2.0 prompt needs clearer production wording, less generic prose, or precise vocabulary for camera, lighting, motion, VFX, audio, and constraints. Route blocked prompts through seedance-filter for context and boundary review.
ideogram4
Prompting patterns for Ideogram 4 text-to-image — best-in-class in-image text rendering and exact color/layout control via structured JSON captions. Use when generating images that need legible on-image text (title cards, thumbnails, logos, signage, CTAs), precise brand colors, or controlled spatial layout. Triggers…