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AI深度分析语音记录，揭示行为模式、认知偏见和战略盲点。不只是数据汇总，而是直击本质的战略洞察和否定性指导。提取可沉淀的核心原则和工作偏好，构建个人工作模式知识库。
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
Basado en la clasificación ocupacional SOC
仿写/复刻单篇小红书笔记的写作风格：从“单个帖子样本”（标题+正文+标签+可选图片/排版特征）抽取风格指纹，并结合用户给定主题要点与可选知识库链接，生成同风格但不抄袭的新笔记（标题/正文/话题标签/可选配图建议）。当用户说“仿写这篇小红书/按这篇的风格写一篇/复刻口吻排版/基于这条笔记生成同风格内容”，或提供 info.json、detail.txt、链接、截图等作为风格样本时使用。
A generic requirement/problem clarification workflow. Use when a user/client request is ambiguous, underspecified, or hard to execute/verify. The skill reframes the request, defines scope, constraints, inputs, and acceptance criteria, then outputs a concise one-page definition and a minimal question set to close gaps. Suitable for product/feature requests, content requests, ops tasks, research asks, and decision-making prompts.
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
统一的推文内容获取技能。支持单条/批量推文提取，输出结构化数据。当用户提供 Twitter/X 链接并希望获取内容时自动触发。
从 Markdown 文件中自动提取并下载 Twitter 图片，生成可直接发布的版本。 当用户说"下载推文图片"、"提取Twitter图片"、"download twitter images"时触发。
通用写作技能：从数据到文章的自动化写作工作流。 支持多种写作模板（专题、教程、分析、总结、文档），可根据数据源或交互式生成文章。 当用户说"写作"、"生成文章"、"写文档"、"universal-writer"时触发。
| name | daily-ai-workflow-analyzer |
| description | AI深度分析语音记录，揭示行为模式、认知偏见和战略盲点。不只是数据汇总，而是直击本质的战略洞察和否定性指导。提取可沉淀的核心原则和工作偏好，构建个人工作模式知识库。 |
从语音记录中提取深度战略洞察，揭示认知偏见和行为盲点
不只是数据汇总，而是：
知识沉淀 > 数据堆积
每次分析后：
python3 /Users/douba/.claude/skills/daily-ai-workflow-analyzer/scripts/analyze_voice_workflow.py --days 1 --all
python3 /Users/douba/.claude/skills/daily-ai-workflow-analyzer/scripts/analyze_voice_workflow.py --days 1 --app Antigravity
步骤1：提取语音记录
步骤2：AI深度分析（核心改进）
步骤3：自动导出到Obsidian
知识体系/个人工作模式/微信聊天记录_YYYY-MM-DD.mdAntigravity开发记录_YYYY-MM-DD.mdAlma使用记录_YYYY-MM-DD.md知识体系/个人工作模式/
├── 微信聊天记录_2026-01-12.md ← 每日分析结果
├── Antigravity开发记录_2026-01-12.md ← 编程App分析
├── Alma使用记录_2026-01-12.md ← 文档工具分析
├── 用户习惯清单.md ← 自动积累
├── 工作模式配置.md ← 自动积累
└── 偏好设置.md ← 自动积累
Launch the interactive dashboard:
/Users/douba/.claude/skills/daily-ai-workflow-analyzer/scripts/start_dashboard.sh
Then visit: http://localhost:8080
Dashboard Features:
Static Framework (One-time creation)
├── analysis_dashboard.html (HTML structure, CSS, JS logic)
│ └── JavaScript: loadData() → fetch from API → renderDashboard()
└── Never regenerated, only data changes
Dynamic Service (Flask API)
├── analysis_server.py (lightweight backend)
│ ├── GET /api/data → Return all apps data
│ ├── POST /api/analyze → Trigger Python analysis
│ ├── GET /api/report/<app> → Return markdown report
│ └── GET /api/status → Real-time analysis status
└── Handles Python script execution and status polling
Data Processing (Python Scripts)
├── extract_voice_records.py (Query Typeless DB)
├── group_by_app.py (Group records by app)
├── generate_analysis_report.py (Generate structured reports)
└── analyze_voice_workflow.py (Master workflow controller)
~/Library/Application Support/Typeless/typeless.db~/Library/Application Support/alma/workspaces/temp-voice-extraction/by_app/*.json~/Library/Application Support/alma/workspaces/temp-voice-extraction/analysis_reports/Located at: templates/analysis_framework.json
Dimensions（根据App类型自动适配）：
extract_voice_records.py: Extract voice records from Typeless SQLite DBgroup_by_app.py: Group records by focused_app_name into separate JSON filesgenerate_analysis_report.py: Generate structured analysis reports (markdown)analyze_voice_workflow.py: Master workflow controller (orchestrates extraction, grouping, reporting)analysis_server.py: Flask API server for dashboardstart_dashboard.sh: One-click startup script for dashboardanalysis_report_template.md: Standardized template for analysis reportsanalysis_framework.json: Configuration for scene/stage/pattern recognitiontypeless_db_schema.md: Complete schema of Typeless.app SQLite databaseobsidian_organization_guidelines.md: Guidelines for structuring Obsidian notes (future integration)When you ask AI to "analyze voice records" or "generate analysis report", it will:
Example commands:
What AI does automatically:
# Check and start Flask server (background)
# Execute full analysis
python3 auto_analyze.py --all
# Read and display report content
You see:
If you want to use the visual dashboard:
start_dashboard.shAnalyze all apps:
python3 /Users/douba/.claude/skills/daily-ai-workflow-analyzer/scripts/auto_analyze.py --all
Analyze specific app:
python3 /Users/douba/.claude/skills/daily-ai-workflow-analyzer/scripts/auto_analyze.py --app Antigravity
Start dashboard only:
/Users/douba/.claude/skills/daily-ai-workflow-analyzer/scripts/start_dashboard.sh
不只是数据汇总，而是深度战略洞察
报告采用"直面本质"的分析框架，旨在：
| 对比维度 | v1.0 (旧版本) | v2.0 (深度洞察版) |
|---|---|---|
| 核心目标 | 数据汇总和行为描述 | 深度洞察和战略指导 |
| 分析深度 | 表面模式识别 | 认知偏见和战略盲点 |
| 行动指导 | 只有正向建议 | 包含否定性指导 |
| 价值产出 | SOP和规则清单 | 原则和偏好固化 |
| 问题导向 | "用户做了什么" | "用户为什么这么做" |
| 反馈机制 | 简单评分 | 多维度评估和反馈闭环 |
生成报告时必须满足：
Each report includes a feedback section:
This feedback is collected to iteratively refine the analysis framework and report structure.
The generated analysis reports can be synced to Obsidian vault:
Currently in MVP phase—focus on report quality and value extraction before automation.