Token导航 LogoToken导航

Agent Skill · 开发

optimize-prompt(优化提示)

用于辅助提示词、系统指令、Agent 行为约束和工作流模板的整理。它适合让 Agent 规范任务边界、统一输出格式、拆分操作步骤或优化提示词可复用性。使用时需要保留真实业务约束,不要把示例当硬规则;涉及自动执行、外部工具或高风险操作时,应在提示词中明确确认步骤、权限边界和失败处理方式。

optimize-prompt 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 16,172。

总安装 528周安装 22GitHub Stars 12下载 176核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:optimize-prompt(优化提示)
来源仓库:https://github.com/jbrukh/skills
仓库路径:skills/optimize-prompt
安装命令:
npx skills add https://github.com/jbrukh/skills --skill optimize-prompt
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/jbrukh/skills --skill optimize-prompt

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

optimize-prompt 用于辅助提示词、系统指令和工作流模板的整理。适合让 Agent 规范任务边界、统一输出格式或拆分操作步骤。使用时需保留真实业务约束,不把示例当作硬规则。涉及自动执行或高风险操作时,应在提示词中明确确认步骤和权限边界。优先定义失败处理方式和回退机制。

optimize-prompt 属于开发类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/optimize-prompt

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/jbrukh/skills --skill optimize-prompt;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

optimize-prompt 怎么安装?

在终端执行 npx skills add https://github.com/jbrukh/skills --skill optimize-prompt。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

optimize-prompt 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

optimize-prompt 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

optimize-prompt 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

optimize-prompt 用的人多吗?

公开数据是累计安装 528 次,近一周安装 22 次,下载 176 次,GitHub 12 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

optimize-prompt 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
optimize-prompt

优化提示 · 本页

52812只读未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Optimize Prompt

Prompt optimization pipeline. Given prompt P, improve via critique then compression, with semantic equivalence checks after each stage.

Pipeline

P → [Input Analysis] → [Critique] → P' → [Equiv Check 1] → [Compress] → P'' → [Equiv Check 2] → Output P''

Execute stages strictly in order — no skipping, reordering, or parallelizing. Each stage passes (pipeline continues) or fails (pipeline terminates with explanation).


Stage 0: Input Analysis

Examine P:

  1. Does P take input? Check for placeholders ({{X}}, {input}, [USER INPUT]), references to "the user's input"/"the given text"/"the provided data", or expectation of concatenation with external content.
  2. If yes: Construct plausible, concrete test input X — realistic, non-trivial (2-3+ sentences or meaningful data structure), exercising P's main logic paths. If P has branching conditions, X triggers the primary branch. If P produces structured output, X requires all output fields. If P has validation conditions, X passes validation. State X explicitly.
  3. If no: P is self-contained. Set X = ∅. Equivalence checks compare outputs of P directly.

Display:

**Input Analysis**
- Takes input: [Yes/No]
- Test input X: [constructed input, or "N/A — self-contained"]

Stage 1: Critique

Apply think-critically methodology to P:

  1. Derive 5-8 expectations from P itself — behavioral properties any revision must preserve. Frame as testable statements (e.g., "Produces JSON output", "Rejects off-topic queries").
  2. Evaluate P against expectations. Per expectation: confidence (0-100%) with concise rationale referencing specific text in P.
  3. Propose fixes for expectations with confidence < 95%. Each fix: exact text to add, remove, or replace.
  4. Produce P' — revised prompt with all fixes applied. Complete text, not a diff.

Display:

**Stage 1: Critique**

| Expectation | Confidence | Rationale |
|---|---|---|
| ... | ...% | ... |

**Overall Score: [average]%**

**Fixes Applied:**
1. [Fix with exact text changes]
...

---
> **P' (Revised Prompt):**
---
[Full text of P']
---

If all expectations >= 95%, set P' = P and note "No fixes needed."


Stage 2: Equivalence Check 1

Verify P and P' produce essentially the same output on X.

Procedure:

  1. Simulate running P on X. Describe expected output (2-4 sentences): structure, content, tone, key features.
  2. Simulate running P' on X. Describe expected output (2-4 sentences).
  3. Equivalent if all hold:

- (a) Output structure identical (sections, format, ordering) - (b) Factual/decisional content identical — nothing added, removed, or altered - (c) Differences limited to: wording improvements, added specificity, stronger constraint enforcement - (d) User expecting P's behavior would accept output without noticing intent change - If (a)-(c) hold but (d) uncertain, default YES.

If X = ∅, compare standalone outputs.

Display:

**Stage 2: Equivalence Check 1**

- P(X) expected output: [description]
- P'(X) expected output: [description]
- Equivalent: [YES/NO]
- Reasoning: [1-2 sentences]

If NO: Terminate:

**PIPELINE FAILED at Stage 2**
[Explanation of behavioral drift]
[Fixes that caused divergence]

If YES: Proceed to Stage 3.


Stage 3: Compress

Apply compress-prompt methodology to P' (lossless mode):

  1. Target 10-30% token reduction, 100% semantic retention.
  2. Every instruction, constraint, directive, tonal signal, example, and structural relationship in P' must be explicitly present in P''. Nothing left to inference.
  3. Allowed: remove filler, collapse redundancy, tighten syntax, merge duplicates, normalize structure.
  4. Forbidden: dropping directives, abbreviating examples beyond recognition, eliding constraints, compressing tonal/behavioral signals into vague summaries.
  5. Produce P'' and a directive map (each P' directive → P'' counterpart).

Display:

**Stage 3: Compress**

---
> **P'' (Compressed Prompt):**
---
[Full text of P'']
---

**Directive Map:**
| # | Original directive (P') | Compressed counterpart (P'') |
|---|---|---|
| 1 | [directive from P'] | [text in P''] |
| ... | ... | ... |

**Stats:**
- P' tokens (approx): [n]
- P'' tokens (approx): [n]
- Compression: [%]
- Directives: [n/n mapped]

Stage 4: Equivalence Check 2

Verify P' and P'' produce essentially the same output on X.

Procedure: Same as Stage 2, comparing P' and P''.

Display:

**Stage 4: Equivalence Check 2**

- P'(X) expected output: [description]
- P''(X) expected output: [description]
- Equivalent: [YES/NO]
- Reasoning: [1-2 sentences]

If NO: Terminate:

**PIPELINE FAILED at Stage 4**
[What compression lost]
[Elements in P' with no counterpart in P'']

If YES: Proceed to output.


Final Output

**PIPELINE SUCCEEDED**

Your optimized prompt (P''):

---
> **BEGIN OPTIMIZED PROMPT**
---
[Full text of P'']
---
> **END OPTIMIZED PROMPT**
---

**Summary:**
- Critique: [n] fixes applied, score [x]% → [y]%
- Compression: [z]% reduction
- Both equivalence checks passed

Edge Cases

  • P fewer than 20 tokens: note optimization may yield minimal improvement, proceed.
  • P already optimal (all expectations >= 95%, compression < 10%): state "Prompt is already well-optimized", return P unchanged.
  • Adversarial or self-referential P: evaluate literally, note observation.
  • Compression < 10% reduction without dropping directives: skip compression, set P'' = P', note "Compression skipped — prompt already dense."
  • P' more than 50% longer than P: flag "Significant expansion", verify in Stage 2 that expansion only adds guardrails/specificity.
  • P contains code blocks: preserve verbatim during critique and compression unless fix explicitly targets code content.

FIRST-TOKEN CONSTRAINT: Response must begin with "## Optimize Prompt". No greetings, preambles, commentary, or blank lines before it. Overrides default conversational behavior.


Input

If literal '{{P}}' appears below without content, inform user no prompt was provided.

Prompt to optimize:

{{P}}

平台分布

Codex

37.06%
按下载量换算65

Claude

30.37%
按下载量换算53

Cursor

20.34%
按下载量换算36

Gemini CLI

9.17%
按下载量换算16

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills