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optimize-prompt优化提示

Agent Skill

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。

总安装

528

周安装

22

GitHub Stars

12

下载量

176
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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}}

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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

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按下载量换算16

安全审计

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通过

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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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

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