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wechat-article-writer

Hybridcontent.writing

Analyze the writing style of reference WeChat articles and generate new articles matching that style on a given topic. Supports style extraction (tone, structure, rhetoric), outline generation, and full article writing via LLM.

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表现型

输入

属性类型必填描述
topicstring✓The topic or subject for the new article to be generated
apiKeysobject✓
sectionsintegerNumber of sections/paragraphs. If omitted, auto-determined from reference style.
wordCountinteger = 2000Target word count for the generated article
outputFormatmarkdown | plaintext | html = markdownOutput format of the article
requirementsstringAdditional requirements: e.g. 'add more data', 'keep it casual', 'include a call-to-action'
referenceArticlesarray✓One or more reference articles for style analysis. Paste the full text of each article.

输出

属性类型必填描述
titlestring✓Generated article title
articlestring✓The full generated article
outlinearray✓Article outline with section titles and key points
summarystringBrief summary of the generated article
subtitlestringOptional subtitle
wordCountinteger✓
styleProfileobject✓Extracted style characteristics from reference articles
原始 JSON Schema

inputSchema

{
  "type": "object",
  "required": [
    "referenceArticles",
    "topic",
    "apiKeys"
  ],
  "properties": {
    "topic": {
      "type": "string",
      "maxLength": 500,
      "minLength": 2,
      "description": "The topic or subject for the new article to be generated"
    },
    "apiKeys": {
      "type": "object",
      "required": [
        "llm"
      ],
      "properties": {
        "llm": {
          "type": "object",
          "required": [
            "apiKey"
          ],
          "properties": {
            "model": {
              "type": "string",
              "default": "deepseek-chat"
            },
            "apiKey": {
              "type": "string",
              "description": "LLM API key"
            },
            "baseUrl": {
              "type": "string",
              "description": "Custom API base URL"
            },
            "provider": {
              "enum": [
                "deepseek",
                "openai",
                "anthropic"
              ],
              "type": "string",
              "default": "deepseek"
            }
          },
          "description": "LLM provider for style analysis and article generation (default: DeepSeek)"
        }
      }
    },
    "sections": {
      "type": "integer",
      "maximum": 20,
      "minimum": 1,
      "description": "Number of sections/paragraphs. If omitted, auto-determined from reference style."
    },
    "wordCount": {
      "type": "integer",
      "default": 2000,
      "maximum": 10000,
      "minimum": 300,
      "description": "Target word count for the generated article"
    },
    "outputFormat": {
      "enum": [
        "markdown",
        "plaintext",
        "html"
      ],
      "type": "string",
      "default": "markdown",
      "description": "Output format of the article"
    },
    "requirements": {
      "type": "string",
      "maxLength": 1000,
      "description": "Additional requirements: e.g. 'add more data', 'keep it casual', 'include a call-to-action'"
    },
    "referenceArticles": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "maxItems": 10,
      "minItems": 1,
      "description": "One or more reference articles for style analysis. Paste the full text of each article."
    }
  }
}

outputSchema

{
  "type": "object",
  "required": [
    "title",
    "article",
    "styleProfile",
    "outline",
    "wordCount"
  ],
  "properties": {
    "title": {
      "type": "string",
      "description": "Generated article title"
    },
    "article": {
      "type": "string",
      "description": "The full generated article"
    },
    "outline": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "section": {
            "type": "string"
          },
          "keyPoints": {
            "type": "array",
            "items": {
              "type": "string"
            }
          }
        }
      },
      "description": "Article outline with section titles and key points"
    },
    "summary": {
      "type": "string",
      "description": "Brief summary of the generated article"
    },
    "subtitle": {
      "type": "string",
      "description": "Optional subtitle"
    },
    "wordCount": {
      "type": "integer"
    },
    "styleProfile": {
      "type": "object",
      "properties": {
        "tone": {
          "type": "string",
          "description": "e.g. 严肃专业 / 轻松幽默 / 温暖治愈"
        },
        "summary": {
          "type": "string",
          "description": "One-paragraph style summary"
        },
        "rhetoric": {
          "type": "array",
          "items": {
            "type": "string"
          },
          "description": "Common rhetorical devices used"
        },
        "structure": {
          "type": "string",
          "description": "e.g. 总分总 / 递进式 / 并列式"
        },
        "vocabulary": {
          "type": "string",
          "description": "Vocabulary level and characteristics"
        },
        "closingStyle": {
          "type": "string",
          "description": "How articles typically close"
        },
        "openingStyle": {
          "type": "string",
          "description": "How articles typically open"
        },
        "sentenceStyle": {
          "type": "string",
          "description": "Sentence length and rhythm pattern"
        }
      },
      "description": "Extracted style characteristics from reference articles"
    }
  }
}

竞技场历史

日期适应度安全分调用数
3月20日0.50001.001