重要前提
安装AI Skills的关键前提是:必须科学上网,且开启TUN模式,这一点至关重要,直接决定安装能否顺利完成,在此郑重提醒三遍:科学上网,科学上网,科学上网。查看完整安装教程 →
Fabric by danielmiessler/personal_ai_infrastructure
npx skills add https://github.com/danielmiessler/personal_ai_infrastructure --skill Fabric执行前,请检查用户自定义设置于: ~/.claude/PAI/USER/SKILLCUSTOMIZATIONS/Fabric/
如果此目录存在,则加载并应用其中找到的任何 PREFERENCES.md、配置文件或资源。这些设置将覆盖默认行为。如果目录不存在,则使用技能默认设置。
执行工作流时,请同时执行以下两项操作:
发送语音通知 :
curl -s -X POST http://localhost:8888/notify
-H "Content-Type: application/json"
-d '{"message": "Running the WORKFLOWNAME workflow in the Fabric skill to ACTION"}' \
/dev/null 2>&1 &
输出文本通知 :
正在 Fabric 技能中运行 WorkflowName 工作流以执行 ACTION...
完整文档: ~/.claude/PAI/THENOTIFICATIONSYSTEM.md
智能提示模式系统,提供 240 多种用于内容分析、提取、摘要、威胁建模和转换的专用模式。
广告位招租
在这里展示您的产品或服务
触达数万 AI 开发者,精准高效
模式位置: Patterns/
| 工作流 | 触发词 | 文件 |
|---|---|---|
| ExecutePattern | "use fabric", "run pattern", "apply pattern", "extract wisdom", "summarize", "analyze with fabric" | Workflows/ExecutePattern.md |
| UpdatePatterns | "update fabric", "update patterns", "sync fabric", "pull patterns" | Workflows/UpdatePatterns.md |
示例 1:从内容中提取智慧
用户:"使用 fabric 从这篇文章中提取智慧"
-> 调用 ExecutePattern 工作流
-> 选择 extract_wisdom 模式
-> 读取 Patterns/extract_wisdom/system.md
-> 将模式应用于内容
-> 返回结构化的 IDEAS、INSIGHTS、QUOTES 等。
示例 2:更新模式
用户:"更新 fabric 模式"
-> 调用 UpdatePatterns 工作流
-> 从上游 fabric 仓库运行 git pull
-> 将模式同步到本地 Patterns/ 目录
-> 报告模式数量
示例 3:创建威胁模型
用户:"使用 fabric 为此 API 创建威胁模型"
-> 调用 ExecutePattern 工作流
-> 选择 create_threat_model 模式
-> 应用 STRIDE 方法论
-> 返回结构化的威胁分析
PAI 原生执行模式,而非调用 fabric -p pattern_name:
Patterns/{pattern_name}/system.md仅在以下情况下使用 fabric 命令:
-y URL - YouTube 转录提取-u URL - URL 内容获取(当原生获取失败时)| 意图 | 模式 | 描述 |
|---|---|---|
| 提取见解 | extract_wisdom | IDEAS、INSIGHTS、QUOTES、HABITS |
| 摘要 | summarize | 通用摘要 |
| 5 句话摘要 | create_5_sentence_summary | 极简摘要 |
| 威胁模型 | create_threat_model | 安全威胁分析 |
| 分析主张 | analyze_claims | 事实核查主张 |
| 改进写作 | improve_writing | 写作增强 |
| 代码审查 | review_code | 代码分析 |
| 主要思想 | extract_main_idea | 核心信息提取 |
浏览 Patterns/ 目录以查看按类别组织的 240 多种模式的完整列表。
工作原理:
用户请求 → 模式选择 → 读取 system.md → 应用 → 返回结果
模式结构:
Patterns/
├── extract_wisdom/
│ └── system.md # 提示指令
├── summarize/
│ └── system.md
├── create_threat_model/
│ └── system.md
└── ...240+ patterns
每个模式的 system.md 包含定义以下内容的完整提示:
| 类别 | 数量 | 示例 |
|---|---|---|
| 提取 | 30+ | extract_wisdom, extract_insights, extract_main_idea |
| 摘要 | 20+ | summarize, create_5_sentence_summary, youtube_summary |
| 分析 | 35+ | analyze_claims, analyze_code, analyze_threat_report |
| 创建 | 50+ | create_threat_model, create_prd, create_mermaid_visualization |
| 改进 | 10+ | improve_writing, improve_prompt, review_code |
| 安全 | 15 | create_stride_threat_model, create_sigma_rules, analyze_malware |
| 评级 | 8 | rate_content, judge_output, rate_ai_response |
-y)和 URL 获取(-u)| 路径 | 用途 |
|---|---|
Patterns/ | 本地模式存储(240+) |
Workflows/ | 执行工作流 |
每周安装量
68
仓库
GitHub 星标
10.7K
首次出现
2026 年 1 月 24 日
安全审计
安装于
gemini-cli62
codex59
opencode58
github-copilot58
cursor54
claude-code54
Before executing, check for user customizations at: ~/.claude/PAI/USER/SKILLCUSTOMIZATIONS/Fabric/
If this directory exists, load and apply any PREFERENCES.md, configurations, or resources found there. These override default behavior. If the directory does not exist, proceed with skill defaults.
When executing a workflow, do BOTH:
Send voice notification :
curl -s -X POST http://localhost:8888/notify
-H "Content-Type: application/json"
-d '{"message": "Running the WORKFLOWNAME workflow in the Fabric skill to ACTION"}' \
/dev/null 2>&1 &
Output text notification :
Running the WorkflowName workflow in the Fabric skill to ACTION...
Full documentation: ~/.claude/PAI/THENOTIFICATIONSYSTEM.md
Intelligent prompt pattern system providing 240+ specialized patterns for content analysis, extraction, summarization, threat modeling, and transformation.
Patterns Location: Patterns/
| Workflow | Trigger | File |
|---|---|---|
| ExecutePattern | "use fabric", "run pattern", "apply pattern", "extract wisdom", "summarize", "analyze with fabric" | Workflows/ExecutePattern.md |
| UpdatePatterns | "update fabric", "update patterns", "sync fabric", "pull patterns" | Workflows/UpdatePatterns.md |
Example 1: Extract wisdom from content
User: "Use fabric to extract wisdom from this article"
-> Invokes ExecutePattern workflow
-> Selects extract_wisdom pattern
-> Reads Patterns/extract_wisdom/system.md
-> Applies pattern to content
-> Returns structured IDEAS, INSIGHTS, QUOTES, etc.
Example 2: Update patterns
User: "Update fabric patterns"
-> Invokes UpdatePatterns workflow
-> Runs git pull from upstream fabric repository
-> Syncs patterns to local Patterns/ directory
-> Reports pattern count
Example 3: Create threat model
User: "Use fabric to create a threat model for this API"
-> Invokes ExecutePattern workflow
-> Selects create_threat_model pattern
-> Applies STRIDE methodology
-> Returns structured threat analysis
Instead of calling fabric -p pattern_name, PAI executes patterns natively:
Patterns/{pattern_name}/system.mdOnly use fabric command for:
-y URL - YouTube transcript extraction-u URL - URL content fetching (when native fetch fails)| Intent | Pattern | Description |
|---|---|---|
| Extract insights | extract_wisdom | IDEAS, INSIGHTS, QUOTES, HABITS |
| Summarize | summarize | General summary |
| 5-sentence summary | create_5_sentence_summary | Ultra-concise |
| Threat model | create_threat_model | Security threat analysis |
| Analyze claims | analyze_claims |
Browse the Patterns/ directory for the complete list of 240+ patterns organized by category.
How it works:
User Request → Pattern Selection → Read system.md → Apply → Return Results
Pattern Structure:
Patterns/
├── extract_wisdom/
│ └── system.md # The prompt instructions
├── summarize/
│ └── system.md
├── create_threat_model/
│ └── system.md
└── ...240+ patterns
Each pattern's system.md contains the full prompt that defines:
| Category | Count | Examples |
|---|---|---|
| Extraction | 30+ | extract_wisdom, extract_insights, extract_main_idea |
| Summarization | 20+ | summarize, create_5_sentence_summary, youtube_summary |
| Analysis | 35+ | analyze_claims, analyze_code, analyze_threat_report |
| Creation | 50+ | create_threat_model, create_prd, create_mermaid_visualization |
| Improvement | 10+ | improve_writing, improve_prompt, review_code |
| Security | 15 | create_stride_threat_model, create_sigma_rules, analyze_malware |
| Rating | 8 | rate_content, judge_output, rate_ai_response |
-y) and URL fetching (-u)| Path | Purpose |
|---|---|
Patterns/ | Local pattern storage (240+) |
Workflows/ | Execution workflows |
Weekly Installs
68
Repository
GitHub Stars
10.7K
First Seen
Jan 24, 2026
Security Audits
Gen Agent Trust HubPassSocketPassSnykWarn
Installed on
gemini-cli62
codex59
opencode58
github-copilot58
cursor54
claude-code54
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| Fact-check claims |
| Improve writing | improve_writing | Writing enhancement |
| Code review | review_code | Code analysis |
| Main idea | extract_main_idea | Core message extraction |