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Agent Skill · 研究检索

enhance-prompt(增强提示)

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。

enhance-prompt 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 58,252。

总安装 419周安装 18GitHub Stars 5下载 147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:enhance-prompt(增强提示)
来源仓库:https://github.com/yigitkonur/skills-by-yigitkonur
仓库路径:skills/enhance-prompt
安装命令:
npx skills add https://github.com/yigitkonur/skills-by-yigitkonur --skill enhance-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/yigitkonur/skills-by-yigitkonur --skill enhance-prompt

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

enhance-prompt 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。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;涉及自动执行时应明确确认步骤和权限边界。高风险操作应在提示词中定义失败处理方式和权限控制。

适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/enhance-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/yigitkonur/skills-by-yigitkonur --skill enhanc…;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

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

enhance-prompt 怎么安装?

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

enhance-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。

enhance-prompt 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

enhance-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。

enhance-prompt 用的人多吗?

公开数据是累计安装 419 次,近一周安装 18 次,下载 147 次,GitHub 5 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。

enhance-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。

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。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

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。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
enhance-prompt

增强提示 · 本页

4195需要联网未明确通过

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

Enhance Prompt

Intercept a user's raw prompt, turbocharge it with context and steering, and return it ready to execute. Fast. No bloat. No agent-spawning — you're just crafting a better user message.

Trigger boundary

Use when: enhancing a prompt before sending it to an LLM or coding agent Do NOT use when: improving general writing (emails, essays), building skill files, or reviewing code

Core philosophy

You're not rewriting for style. You're doing three things:

  1. Steering HOW the agent thinks — not just WHAT to think about
  2. Predicting WHERE the agent will trip — and pre-blocking those failures
  3. Injecting a halt condition — so the agent knows when it's done

Workflow

Step 1 — Read and diagnose (silent)

Read the prompt. Form an internal opinion on these questions — do NOT show this to the user:

QuestionWhy it matters
What's the narrative arc?Every good prompt tells a story: here's where we are → here's the problem → here's what done looks like
What will the agent do FIRST?The opening move determines everything. If unclear, the agent will stall or go sideways.
Where will it get stuck?Ambiguity, missing context, scope creep, no exit condition — predict the failure mode.
Is this code-related?If yes, the agent can see the filesystem, run commands, read files. Don't repeat what it can discover.
What's the done signal?How does the agent know to stop? Most prompts forget this and the agent spins.

Step 2 — Plan via the runtime's ask-user tool (Round 1, default)

Default: run Round 1. For non-trivial prompts, a short upfront planning round catches misalignment cheaper than rewriting after enhancement.

Dispatch one user-question call with up to 4 bundled questions covering the axes most likely to change the enhancement. The tool name depends on the runtime — here is a quick lookup, full table in references/ask-user-tools.md:

  • claude-code / kimi-cli → AskUserQuestion
  • codex / qwen-code / mistral-vibe → ask_user_question
  • gemini-cli / deepagents / github-copilot / pi → ask_user
  • cline / roo → ask_followup_question
  • cursor / continue → AskQuestion
  • droid → AskUser
  • antigravity → suggested_responses
  • opencode → question
  • Unknown runtime → prose fallback (same options, presented as markdown)

See references/ask-user-tools.md for the full 16-runtime table with confidence notes, invocation shape, naming-convention lookup, and the prose fallback template. See references/planning-questions.md for the canonical axis bank + selection rules.

Per question:

  • 2-4 options, mutually exclusive unless multiSelect: true is clearly right (e.g., "which failure modes to block" — multiple is common)
  • First option marked "(Recommended)" based on your Step 1 diagnosis — not a static default
  • Short labels (1-5 words), one-line descriptions
  • Do not manually add "Other" — Claude Code auto-provides it and most other runtimes mimic this convention; see references/ask-user-tools.md for per-runtime differences

Skip Round 1 only when:

  • The prompt is a one-line surgical edit with unambiguous intent ("fix typo in README line 12")
  • The user explicitly said "just enhance, don't ask"
  • Step 1 diagnosis shows the prompt is already excellent → proceed with the light-touch path and a one-line note

Step 2a — Round 2 (conditional, narrower)

After Round 1 answers come back, assess: did the answers surface a new ambiguity Round 1 could not have anticipated?

Fire Round 2 only if:

  • An answer steered the enhancement into an axis Round 1 did not cover (e.g., user picked "Plan of attack" on the outcome axis → now you need to ask what shape the plan should take)
  • The user chose "Other" with text that introduces a new axis

Skip Round 2 when:

  • Round 1 cleared the picture — go straight to Step 3
  • You are tempted to use Round 2 to "double-check" Round 1 answers → no, just proceed

Round 2 is one call to the runtime's ask-user tool, 1-3 focused questions. Total question budget across both rounds: ≤ 7. If the budget is blown, make a reasoned default and proceed with a note.

Step 3 — Enhance

Apply these layers to the prompt. Read references/enhancement-layers.md for the full guide.

Layer 1: Narrative structure Give the prompt a beginning-middle-end. The agent needs to know: where are we, what's the problem, what does success look like.

Layer 2: Thinking steering Don't just say what to do — say how to approach it:

  • "Start by understanding X before touching Y"
  • "Check if Z exists first — if not, the approach changes"
  • "Think about this as a [framing] problem, not a [wrong framing] problem"

Layer 3: Failure pre-emption Block the 2-3 most likely ways the agent will go wrong:

  • "Do NOT refactor surrounding code — only touch what's asked"
  • "If the test suite doesn't exist yet, say so instead of creating one"
  • "Scope: just this file, not the whole module"

Layer 4: Context injection (code-aware) If the prompt targets a coding agent, don't explain what the agent can see. Instead:

  • Name specific files/paths if the user mentioned them
  • Mention the likely tech stack if obvious from context
  • Assume the agent can tree, cat, grep, git log — don't duplicate that capability

Layer 5: Verification and halt Every enhanced prompt MUST end with:

  • A verification step: "After implementing, run X to confirm it works"
  • A done signal: "You're done when [specific observable condition]"
  • A halt condition: "If you hit [blocker], stop and ask instead of guessing"

Read references/code-prompt-patterns.md for code-specific patterns. Read references/failure-modes.md for common agent failure modes to pre-empt.

Step 4 — Present and offer depth

Show the enhanced prompt in a code block. Below it, one line explaining the key improvement.

Then always end with:

Want me to dig deeper? I can add more context, tighten the scope, or research the topic first.

If the user says "run it" — execute the enhanced prompt directly. Reset framing completely.

Effort calibration

Input qualityAction
Already good (clear intent, specific, has constraints)Light touch: add halt condition + verification only. Say "This is solid — I just added a done signal."
Decent intent, vague on detailsModerate: add narrative arc + failure pre-emption + halt
Raw idea or stream-of-consciousnessFull restructure: all 5 layers. Show the transformation.

Decision rules

  • If the prompt is already excellent: say so, add only the halt condition, offer to run as-is
  • If the prompt is for a coding agent: assume filesystem access, skip explaining tools it has
  • If the prompt mentions a specific technology: don't research it unless accuracy is critical (e.g., API versions). The agent doing the work can look things up itself.
  • If the prompt is a system/agent prompt (defines behavior, not requests a task): preserve "You are..." framing, add edge-case handling and escalation criteria
  • Never add filler techniques for completeness — every addition must fix a specific failure mode

What NOT to do

  • No five-shot examples unless the user asks for them
  • No "agent writes its own prompt" meta-prompting
  • No 26-principle dumps or academic prompt engineering
  • No more than 2 ask-user rounds, ever — and no more than 4 questions per round (Claude Code cap; keep portable)
  • No forcing Round 1 on surgical one-liners — skip it when Step 1 diagnosis is unambiguous
  • No "Option A / B / C" filler labels — every option must be a meaningful choice the user can compare
  • No manually-added "Other" option — Claude Code auto-provides it and most runtimes mimic this; see references/ask-user-tools.md if running on a runtime that differs
  • No rewriting the user's voice — enhance the content, preserve the tone
  • No spawning agents — this skill enhances a user message, period

Reference routing

FileRead when
references/ask-user-tools.mdDispatching the planning round — picks the right tool name for the current runtime across 16 mapped agents (Claude Code / Codex / Factory Droid / Cursor / Gemini CLI / Cline / Roo / and 9 more), with a prose fallback when no structured tool is available
references/planning-questions.mdRunning Step 2 (Round 1) — canonical axis bank, picking the "(Recommended)" option, swap rules, worked examples
references/enhancement-layers.mdApplying the 5 enhancement layers — detailed guidance per layer
references/code-prompt-patterns.mdPrompt targets a coding agent — file paths, verification, tech-stack awareness
references/failure-modes.mdPredicting and pre-empting common agent failure modes

平台分布

Codex

34.38%
按下载量换算51

Claude

31.11%
按下载量换算46

Cursor

18.51%
按下载量换算27

Gemini CLI

8.87%
按下载量换算13

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。

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