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douyin-viral-content

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English
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Translation

Chinese

抖音爆款文案生成器

Douyin Viral Copy Generator

核心理念

Core Concept

用户只需说："生成新文案"，提供书籍摘录或金句内容。
所有分析、优化、打分、预估全部自动完成，直到输出5星级爆款文案。
Users only need to say: "Generate new copy" and provide book excerpts or golden quotes.
All analysis, optimization, scoring, and estimation are automatically completed until a 5-star viral copy is output.

全自动化工作流程

Fully Automated Workflow

当接收到生成文案请求时，按照以下步骤自动执行：
When a copy generation request is received, it automatically executes the following steps:

步骤1：历史数据自动读取与分析

Step 1: Automatic Reading and Analysis of Historical Data

自动扫描 
已发布/
文件夹下的所有历史文案：
  • 提取每条文案的预估播放量、实际播放量、各维度得分
  • 识别哪些爆款要素得分高的文案实际表现更好
  • 分析预估模型是否存在系统性偏差（总是高估或低估）
  • 总结账号的内容特色（更擅长哪类情感、风格、话题）
如果历史数据不足（少于3条）：使用通用模型，无需调整策略
Automatically scan all historical copies in the 
Published/
folder:
  • Extract the estimated play volume, actual play volume, and scores of each dimension for each copy
  • Identify which copies with high scores in viral factors perform better in practice
  • Analyze whether the estimation model has systematic bias (always overestimates or underestimates)
  • Summarize the content characteristics of the account (which types of emotions, styles, and topics it is better at)
If historical data is insufficient (less than 3 copies): Use the general model, no need to adjust the strategy

步骤2：策略自动校准

Step 2: Automatic Strategy Calibration

基于历史分析结果，自动调整：
  • 要素权重：提高表现好的要素权重（如"情感权力反转"高分文案播放量好，则增强该要素）
  • 预估参数：校准播放量预估模型（如历史总是高估20%，自动降低预估系数）
  • 标签策略：优化标签选择（如某些标签组合表现更好，优先使用）
  • 内容方向：强化账号擅长的内容类型
Based on historical analysis results, automatically adjust:
  • Factor Weights: Increase the weight of factors that perform well (e.g., if copies with high scores in "Emotional Power Reversal" have good play volume, enhance this factor)
  • Estimation Parameters: Calibrate the play volume estimation model (e.g., if historical data always overestimates by 20%, automatically reduce the estimation coefficient)
  • Tag Strategy: Optimize tag selection (e.g., if certain tag combinations perform better, prioritize their use)
  • Content Direction: Strengthen the content types that the account is good at

步骤3：应用9大爆款要素生成文案

Step 3: Generate Copy with 9 Viral Factors

参考 
references/viral-factors.md
，确保文案包含关键要素：
  1. 情感权力反转
  2. 认知重构
  3. 权威背书
  4. 宿命论哲学
  5. 算法友好标签
  6. 金句传播性
  7. 完播率优化
  8. 互动钩子设计
  9. BGM适配建议
融入历史学习：基于步骤2的策略调整，强化成功要素。
Refer to 
references/viral-factors.md
to ensure the copy includes key factors:
  1. Emotional Power Reversal
  2. Cognitive Restructuring
  3. Authority Endorsement
  4. Fatalistic Philosophy
  5. Algorithm-friendly Tags
  6. Golden Quote Spreadability
  7. Completion Rate Optimization
  8. Interactive Hook Design
  9. BGM Adaptation Suggestions
Integrate Historical Learning: Based on the strategy adjustments in Step 2, strengthen successful factors.

步骤4：多维度打分评估

Step 4: Multi-dimensional Scoring Evaluation

参考 
references/scoring-system.md
，自动计算：
  • 内容质量分（100分）：爆款要素覆盖度、金句质量、情感共鸣度
  • 算法适配分（100分）：标签策略、完播率预期、互动引导
  • 创新度分（100分）：角度创新性、差异化表达
  • 综合爆款指数（总分300，换算成5星制）
Refer to 
references/scoring-system.md
to automatically calculate:
  • Content Quality Score (100 points): Coverage of viral factors, quality of golden quotes, emotional resonance
  • Algorithm Adaptation Score (100 points): Tag strategy, expected completion rate, interactive guidance
  • Innovation Score (100 points): Perspective innovation, differentiated expression
  • Comprehensive Viral Index (total 300 points, converted to 5-star system)

步骤5：质量控制与自动优化迭代

Step 5: Quality Control and Automatic Optimization Iteration

质量标准：必须达到5星（240-300分）才能输出
如果首次生成未达到5星：
  1. 识别得分最低的维度（如创新度不足）
  2. 自动优化该维度（如：更换角度、增强金句、优化开头）
  3. 重新打分评估
  4. 重复迭代直到达到5星标准
自动规避风险：在生成过程中自动规避潜在风险（如文案过长、开头不够吸引、标签不够正向等），无需告知用户。
自动应用优化点：在迭代过程中直接优化文案，无需告知用户优化了什么。
Quality Standard: Must reach 5 stars (240-300 points) to output
If the first generation does not meet the 5-star standard:
  1. Identify the dimension with the lowest score (e.g., insufficient innovation)
  2. Automatically optimize that dimension (e.g., change perspective, enhance golden quotes, optimize opening)
  3. Re-score and evaluate
  4. Repeat iteration until it meets the 5-star standard
Automatic Risk Avoidance: Automatically avoid potential risks during generation (e.g., overly long copy, unengaging opening, insufficiently positive tags, etc.) without notifying the user.
Automatic Application of Optimization Points: Directly optimize the copy during iteration without notifying the user of the changes.

步骤6：播放量智能预估

Step 6: Intelligent Play Volume Estimation

参考 
references/estimation-model.md
，基于以下因素预估：
  • 内容质量得分（步骤4的打分结果）
  • 账号历史表现（平均播放量、粉丝数）
  • 赛道竞争度（情感治愈类基础流量）
  • 标签热度和组合效果
  • 预估完播率
使用校准后的模型：应用步骤2中调整的预估参数，提高预估准确性。
Refer to 
references/estimation-model.md
to estimate based on the following factors:
  • Content quality score (scoring results from Step 4)
  • Account historical performance (average play volume, number of followers)
  • Track competition (base traffic for emotional healing category)
  • Tag popularity and combination effects
  • Estimated completion rate
Use Calibrated Model: Apply the adjusted estimation parameters from Step 2 to improve estimation accuracy.

步骤7：输出优化文案文件

Step 7: Output Optimized Copy File

将文案输出到 
未发布/
文件夹，文件名格式：
[类型]_[主题]_[日期]_第X批.md
文件包含以下内容：
  1. 文案正文 + 精选标签（4-6个）
  2. 综合评分（必定是5星）
  3. 视频分析（仅展示优势，风险已自动规避）
  4. 预估播放量（基础预估 + 爆款上限）
  5. 实际播放量板块（待用户第二天填写）
Output the copy to the 
Unpublished/
folder with the file name format:
[Type]_[Topic]_[Date]_BatchX.md
The file includes the following content:
  1. Copy body + selected tags (4-6 tags)
  2. Comprehensive score (must be 5 stars)
  3. Video analysis (only show advantages, risks have been automatically avoided)
  4. Estimated play volume (base estimate + viral upper limit)
  5. Actual play volume section (to be filled by the user the next day)

输出模板结构

Output Template Structure

生成的文案文件严格按照以下结构：
markdown
undefined
The generated copy file strictly follows the structure below:
markdown
undefined

[文案主题]

[Copy Topic]

📝 文案正文

📝 Copy Body

[生成的完整优化文案]
[Generated fully optimized copy]

🏷️ 推荐标签

🏷️ Recommended Tags

#标签1 #标签2 #标签3 #标签4
#tag1 #tag2 #tag3 #tag4

🎯 综合评分

🎯 Comprehensive Score

  • 内容质量分：XX/100
  • 算法适配分：XX/100
  • 创新度分：XX/100
  • 综合爆款指数：★★★★★（XXX分）
  • Content Quality Score: XX/100
  • Algorithm Adaptation Score: XX/100
  • Innovation Score: XX/100
  • Comprehensive Viral Index: ★★★★★ (XXX points)

📊 视频分析

📊 Video Analysis

✅ 内容优势

✅ Content Advantages

  • 优势点1：[具体说明]
  • 优势点2：[具体说明]
  • 优势点3：[具体说明]
  • Advantage 1: [Specific description]
  • Advantage 2: [Specific description]
  • Advantage 3: [Specific description]

💡 创作建议

💡 Creation Suggestions

  • BGM推荐：[音乐风格建议]
  • 视觉建议：[画面/文字动画建议]
  • 节奏控制：[时长和节奏建议]
  • BGM Recommendation: [Music style suggestion]
  • Visual Suggestions: [Footage/text animation suggestions]
  • Rhythm Control: [Duration and rhythm suggestions]

📈 预估播放量

📈 Estimated Play Volume

  • 基础预估：XX-XX万
  • 爆款上限：XX万+
  • 完播率预估：XX-XX%
评分依据： [基于当前得分、历史数据、赛道特征的详细分析]
  • Base Estimate: XX-XX0,000
  • Viral Upper Limit: XX0,000+
  • Estimated Completion Rate: XX-XX%
Scoring Basis: [Detailed analysis based on current scores, historical data, and track characteristics]

📊 实际播放量（待填写）

📊 Actual Play Volume (To Be Filled)

  • 实际播放：_____
  • 点赞数：_____
  • 转发数：_____
  • 评论数：_____
  • 完播率：_____
  • 发布时间：_____
  • Actual Plays: _____
  • Likes: _____
  • Shares: _____
  • Comments: _____
  • Completion Rate: _____
  • Publication Time: _____

🔍 复盘分析

🔍 Review Analysis

[当用户填写实际播放量后，下次生成新文案时会自动分析这条数据，并融入到策略优化中]
undefined
[When the user fills in the actual play volume, this data will be automatically analyzed in the next copy generation and integrated into strategy optimization]
undefined

关键原则

Key Principles

✅ 自动化原则

✅ Automation Principle

  • 所有分析、优化、迭代全部自动完成
  • 用户无需手动触发任何子任务
  • 用户无需了解优化过程，只需看到最终5星文案
  • All analysis, optimization, and iteration are fully automated
  • Users do not need to manually trigger any subtasks
  • Users do not need to understand the optimization process, only need to see the final 5-star copy

✅ 质量控制原则

✅ Quality Control Principle

  • 必须达到5星标准才能输出
  • 自动识别问题并迭代优化
  • 自动规避风险，无需展示给用户
  • Must meet the 5-star standard before output
  • Automatically identify problems and iterate optimization
  • Automatically avoid risks without showing them to users

✅ 学习进化原则

✅ Learning and Evolution Principle

  • 每次生成都读取历史数据
  • 自动识别成功模式并强化
  • 预估模型持续校准优化
  • Read historical data in each generation
  • Automatically identify successful patterns and strengthen them
  • Continuously calibrate and optimize the estimation model

✅ 用户体验原则

✅ User Experience Principle

  • 输入极简：只需提供原始内容
  • 输出完美：保证5星质量
  • 过程隐藏：不展示中间步骤
  • Minimal input: Only need to provide original content
  • Perfect output: Guarantee 5-star quality
  • Hidden process: Do not show intermediate steps

参考资源

Reference Resources

本 skill 包含以下参考文件，按需自动读取：
  • references/viral-factors.md
    ：9大爆款要素详细分析和评分标准
  • references/scoring-system.md
    ：300分打分机制和5星换算规则
  • references/estimation-model.md
    ：播放量预估算法和参数调整方法
  • references/optimization-guide.md
    ：自动优化规则和迭代策略
  • references/learning-guide.md
    ：历史数据分析方法和策略校准指南
This skill includes the following reference files, which are automatically read as needed:
  • references/viral-factors.md
    : Detailed analysis and scoring standards for 9 viral factors
  • references/scoring-system.md
    : 300-point scoring mechanism and 5-star conversion rules
  • references/estimation-model.md
    : Play volume estimation algorithm and parameter adjustment methods
  • references/optimization-guide.md
    : Automatic optimization rules and iteration strategies
  • references/learning-guide.md
    : Historical data analysis methods and strategy calibration guidelines