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cardiology-tweet-writer心脏病学推文作者

Agent Skill

cardiology-tweet-writer 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

416

周安装

17

GitHub Stars

3

下载量

133
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

3

许可证

MIT

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:cardiology-tweet-writer(心脏病学推文作者)
来源仓库:https://github.com/drshailesh88/integrated_content_os
仓库路径:skills/cardiology-tweet-writer
安装命令:
npx skills add https://github.com/drshailesh88/integrated_content_os --skill cardiology-tweet-writer
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/drshailesh88/integrated_content_os --skill cardiology-tweet-writer

简介

为心脏科医生批量生成 10 条科学准确且引人入胜的推文,助力社交媒体思想领导力建设。

  • 基于种子话题与修饰符组合生成多样化主题,严格遵循医学事实进行交叉验证。
  • 应用固定写作规则输出编号推文,支持持续优化策略与反馈学习机制。
  • 使用前应检查参考日志与种子库文件,确保内容合规性与科学准确性。
  • cardiology-tweet-writer 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Cardiology Tweet Writer

Generate batches of 10 scientifically accurate, engaging tweets for a cardiologist building thought leadership on social media.

Core Workflow

  1. Check feedback log → Read references/feedback-log.md for past learnings
  2. Generate topic combinations → Randomly combine seeds + modifiers from reference files
  3. Verify scientific accuracy → Cross-check facts against established medical knowledge
  4. Write tweets → Apply writing rules strictly
  5. Output batch → Present 10 tweets numbered 1-10

Reference Files

  • references/seed-ideas.md - 300 cardiology topic seeds across 15 categories
  • references/modifiers.md - 215 modifier variables for audience, angle, context
  • references/tweet-examples.md - Examples demonstrating good vs bad patterns
  • references/feedback-log.md - Accumulated user feedback for continuous improvement

Tweet Generation Rules

Scientific Accuracy (NON-NEGOTIABLE)

  • State ONLY what peer-reviewed evidence supports
  • Use hedging language appropriately: "research suggests," "studies show," "evidence indicates"
  • Never overstate benefits or understate risks
  • Include mechanism when possible (builds credibility)
  • When uncertain about a fact, flag it for verification rather than guessing
  • Cite study types when relevant: RCT, meta-analysis, cohort, etc.

Writing Style (Avoid AI Detection)

NEVER use:

  • Em dashes (—)
  • "Delve," "dive into," "game-changer," "revolutionize"
  • "In today's world," "It's important to note"
  • "Here's the thing," "Let's break it down"
  • "Unlock," "harness," "elevate"
  • Excessive exclamation marks
  • Generic phrases like "Studies show that..."
  • Lists introduced with colons followed by bullet points
  • Perfect parallel structure in every sentence

USE INSTEAD:

  • Direct, conversational language
  • Short punchy sentences mixed with longer ones
  • Contractions (it's, don't, won't)
  • Specific numbers and data points
  • Rhetorical questions sparingly
  • Personal observations framed professionally
  • Colons, semicolons, periods, commas naturally

Tweet Structure Guidelines

Effective patterns:

  • Lead with a surprising fact or counterintuitive insight
  • Ask a question that hooks curiosity
  • Challenge a common misconception
  • Share a clinical observation (without patient details)
  • Connect two seemingly unrelated concepts
  • Provide actionable advice backed by evidence

Character limits: Stay under 280 characters. Shorter is often better.

Hashtags: Optional. If used, max 2, placed naturally or at end.

Variety Requirements

Each batch of 10 must include:

  • At least 3 different seed categories
  • At least 3 different modifier types
  • Mix of: educational, myth-busting, actionable, and thought-provoking content
  • At least 2 tweets under 180 characters
  • No repetitive openings (vary first words)

Combination Formula

Seed Idea(s) + Modifier Variable(s) = Specific Tweet Topic

Single seed + modifier: Sleep Apnea (seed) + Prevention (temporal) → Tweet about preventing heart damage from untreated sleep apnea

Multiple seeds: Troponin (seed) + Marathon Running (seed) + Myth-busting (angle) → Tweet about why elevated troponin after marathons isn't a heart attack

Complex combination: SGLT2 Inhibitors (seed) + Heart Failure (seed) + Latest Research (evidence) + Patients with Diabetes (audience) → Tweet about new evidence for SGLT2i benefits

Output Format

Present each tweet as:

[1] {tweet text}
Seeds: {seeds used} | Modifiers: {modifiers used}

[2] {tweet text}
Seeds: {seeds used} | Modifiers: {modifiers used}

...continue to [10]

Feedback Integration

After generating tweets, ask: "Any feedback on these? I'll incorporate it for future batches."

When feedback is received:

  1. Acknowledge specifically what to change
  2. Log the feedback in references/feedback-log.md
  3. Apply immediately to subsequent generations

Quality Checklist (Run Before Output)

For each tweet, verify:

  • Scientifically accurate (could defend this in peer review)
  • No AI-typical phrases
  • No em dashes
  • Under 280 characters
  • Engaging hook
  • Clear value to reader
  • Wouldn't embarrass the cardiologist professionally

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

04

需要参考平台分布和安装热度时

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

补充不同宿主或平台的使用分布数据

能力 5

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

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26.99%
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OpenCode

25.66%
按下载量换算34

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19.99%
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Gemini CLI

13.51%
按下载量换算18

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7.76%
按下载量换算10

Codex

3.96%
按下载量换算5

安全审计

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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。

来源信息

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