playbooks

home / skills / zpankz / mcp-skillset / fabric

fabric skill

/fabric

This skill automatically selects the right Fabric pattern from 242+ prompts to enhance threat modeling, summarization, analysis, and content transformation.

This is most likely a fork of the fabric skill from danielmiessler
npx playbooks add skill zpankz/mcp-skillset --skill fabric

Review the files below or copy the command above to add this skill to your agents.

Files (737)
SKILL.md
13.4 KB
---
name: fabric
description: Intelligent pattern selection for Fabric CLI. Automatically selects the right pattern from 242+ specialized prompts based on your intent - threat modeling, analysis, summarization, content creation, extraction, and more. USE WHEN processing content, analyzing data, creating summaries, threat modeling, or transforming text.
---

# Fabric Skill

## Setup Check - Fabric Repository

**IMPORTANT: Before using this skill, verify the Fabric repository is available:**

```bash
# Check if Fabric repo exists
if [ ! -d "$HOME/.claude/skills/fabric/fabric-repo" ]; then
  echo "Fabric repository not found. Cloning..."
  cd "$HOME/.claude/skills/fabric"
  git clone https://github.com/danielmiessler/fabric.git fabric-repo
  echo "Fabric repository cloned successfully."
else
  echo "Fabric repository found at $HOME/.claude/skills/fabric/fabric-repo"
fi
```

**If the repo doesn't exist, clone it immediately before proceeding with any pattern selection.**

## When to Activate This Skill

**Primary Use Cases:**
- "Create a threat model for..."
- "Summarize this article/video/paper..."
- "Extract wisdom/insights from..."
- "Analyze this [code/malware/claims/debate]..."
- "Improve my writing/code/prompt..."
- "Create a [visualization/summary/report]..."
- "Rate/review/judge this content..."

**The Goal:** Select the RIGHT pattern from 242+ available patterns based on what you're trying to accomplish.

## 🎯 Pattern Selection Strategy

When a user requests Fabric processing, follow this decision tree:

### 1. Identify Intent Category

**Threat Modeling & Security:**
- Threat model → `create_threat_model` or `create_stride_threat_model`
- Threat scenarios → `create_threat_scenarios`
- Security update → `create_security_update`
- Security rules → `create_sigma_rules`, `write_nuclei_template_rule`, `write_semgrep_rule`
- Threat analysis → `analyze_threat_report`, `analyze_threat_report_trends`

**Summarization:**
- General summary → `summarize`
- 5-sentence summary → `create_5_sentence_summary`
- Micro summary → `create_micro_summary` or `summarize_micro`
- Meeting → `summarize_meeting`
- Paper/research → `summarize_paper`
- Video/YouTube → `youtube_summary`
- Newsletter → `summarize_newsletter`
- Code changes → `summarize_git_changes` or `summarize_git_diff`

**Wisdom Extraction:**
- General wisdom → `extract_wisdom`
- Article wisdom → `extract_article_wisdom`
- Book ideas → `extract_book_ideas`
- Insights → `extract_insights` or `extract_insights_dm`
- Main idea → `extract_main_idea`
- Recommendations → `extract_recommendations`
- Controversial ideas → `extract_controversial_ideas`

**Analysis:**
- Malware → `analyze_malware`
- Code → `analyze_code` or `review_code`
- Claims → `analyze_claims`
- Debate → `analyze_debate`
- Logs → `analyze_logs`
- Paper → `analyze_paper`
- Threat report → `analyze_threat_report`
- Product feedback → `analyze_product_feedback`
- Sales call → `analyze_sales_call`

**Content Creation:**
- PRD → `create_prd`
- Design document → `create_design_document`
- User story → `create_user_story`
- Visualization → `create_visualization`, `create_mermaid_visualization`, `create_markmap_visualization`
- Essay → `write_essay`
- Report finding → `create_report_finding`
- Newsletter entry → `create_newsletter_entry`

**Improvement:**
- Writing → `improve_writing`
- Academic writing → `improve_academic_writing`
- Prompt → `improve_prompt`
- Report finding → `improve_report_finding`
- Code → `review_code`

**Rating/Evaluation:**
- AI response → `rate_ai_response`
- Content quality → `rate_content`
- Value assessment → `rate_value`
- General judgment → `judge_output`

### 2. Execute Pattern

```bash
# Basic format
fabric [input] -p [selected_pattern]

# From URL
fabric -u "URL" -p [pattern]

# From YouTube
fabric -y "YOUTUBE_URL" -p [pattern]

# From file
cat file.txt | fabric -p [pattern]

# Direct text
fabric "your text here" -p [pattern]
```

## 📚 Pattern Categories (242 Total)

### Threat Modeling & Security (15 patterns)
- `create_threat_model` - General threat modeling
- `create_stride_threat_model` - STRIDE methodology
- `create_threat_scenarios` - Threat scenario generation
- `create_security_update` - Security update documentation
- `create_sigma_rules` - SIGMA detection rules
- `write_nuclei_template_rule` - Nuclei scanner templates
- `write_semgrep_rule` - Semgrep static analysis rules
- `analyze_threat_report` - Threat report analysis
- `analyze_threat_report_cmds` - Extract commands from threat reports
- `analyze_threat_report_trends` - Identify threat trends
- `t_threat_model_plans` - Threat model for plans
- `ask_secure_by_design_questions` - Secure by design questions
- `create_network_threat_landscape` - Network threat landscape
- `analyze_incident` - Incident analysis
- `analyze_risk` - Risk analysis

### Summarization (20 patterns)
- `summarize` - General summarization
- `create_5_sentence_summary` - Ultra-concise 5-line summary
- `create_micro_summary` - Micro summary
- `create_summary` - Detailed summary
- `summarize_micro` - Micro summarization
- `summarize_meeting` - Meeting notes summary
- `summarize_paper` - Academic paper summary
- `summarize_lecture` - Lecture summary
- `summarize_newsletter` - Newsletter summary
- `summarize_debate` - Debate summary
- `summarize_legislation` - Legislation summary
- `summarize_rpg_session` - RPG session summary
- `summarize_board_meeting` - Board meeting summary
- `summarize_git_changes` - Git changes summary
- `summarize_git_diff` - Git diff summary
- `summarize_pull-requests` - PR summary
- `summarize_prompt` - Prompt summary
- `youtube_summary` - YouTube video summary
- `create_ul_summary` - Unsupervised Learning summary
- `create_cyber_summary` - Cybersecurity summary

### Extraction (30+ patterns)
- `extract_wisdom` - General wisdom extraction
- `extract_article_wisdom` - Article-specific wisdom
- `extract_book_ideas` - Book ideas
- `extract_insights` - General insights
- `extract_insights_dm` - Daniel Miessler style insights
- `extract_main_idea` - Core message
- `extract_recommendations` - Recommendations
- `extract_ideas` - Ideas from content
- `extract_questions` - Questions raised
- `extract_predictions` - Predictions made
- `extract_controversial_ideas` - Controversial points
- `extract_business_ideas` - Business opportunities
- `extract_skills` - Skills mentioned
- `extract_patterns` - Patterns identified
- `extract_sponsors` - Sponsor mentions
- `extract_references` - References cited
- `extract_instructions` - Instructions from content
- `extract_jokes` - Humor extraction
- `extract_primary_problem` - Main problem
- `extract_primary_solution` - Main solution
- `extract_product_features` - Product features
- `extract_core_message` - Core message
- `extract_algorithm_update_recommendations` - Algorithm recommendations
- `extract_extraordinary_claims` - Extraordinary claims
- `extract_most_redeeming_thing` - Most valuable aspect

### Analysis (35+ patterns)
- `analyze_claims` - Claim analysis
- `analyze_malware` - Malware analysis
- `analyze_code` - Code analysis
- `analyze_paper` - Paper analysis
- `analyze_logs` - Log analysis
- `analyze_debate` - Debate analysis
- `analyze_incident` - Incident analysis
- `analyze_comments` - Comment analysis
- `analyze_answers` - Answer analysis
- `analyze_email_headers` - Email header analysis
- `analyze_military_strategy` - Military strategy
- `analyze_mistakes` - Mistake analysis
- `analyze_personality` - Personality analysis
- `analyze_presentation` - Presentation analysis
- `analyze_product_feedback` - Product feedback
- `analyze_proposition` - Proposition analysis
- `analyze_prose` - Prose analysis
- `analyze_risk` - Risk analysis
- `analyze_sales_call` - Sales call analysis
- `analyze_spiritual_text` - Spiritual text analysis
- `analyze_tech_impact` - Tech impact analysis
- `analyze_threat_report` - Threat report analysis
- `analyze_bill` - Legislation analysis
- `analyze_candidates` - Candidate analysis
- `analyze_cfp_submission` - CFP submission analysis
- `analyze_terraform_plan` - Terraform plan analysis
- `analyze_interviewer_techniques` - Interviewer technique analysis

### Creation (50+ patterns)
- `create_prd` - Product Requirements Document
- `create_design_document` - Design documentation
- `create_user_story` - User stories
- `create_coding_project` - Coding project
- `create_coding_feature` - Code features
- `create_mermaid_visualization` - Mermaid diagrams
- `create_markmap_visualization` - Markmap mindmaps
- `create_visualization` - General visualizations
- `create_threat_model` - Threat models
- `create_stride_threat_model` - STRIDE threat models
- `create_threat_scenarios` - Threat scenarios
- `create_report_finding` - Report findings
- `create_newsletter_entry` - Newsletter content
- `create_keynote` - Keynote presentations
- `create_academic_paper` - Academic papers
- `create_flash_cards` - Study flashcards
- `create_quiz` - Quizzes
- `create_graph_from_input` - Graphs
- `create_tags` - Content tags
- `create_art_prompt` - Art generation prompts
- `create_command` - CLI commands
- `create_pattern` - Fabric patterns
- `create_logo` - Logo designs
- `create_podcast_image` - Podcast imagery
- `create_sigma_rules` - SIGMA rules
- `create_video_chapters` - Video chapters
- `create_upgrade_pack` - Upgrade documentation

### Improvement (10 patterns)
- `improve_writing` - General writing improvement
- `improve_academic_writing` - Academic writing
- `improve_prompt` - Prompt engineering
- `improve_report_finding` - Report findings
- `review_code` - Code review
- `review_design` - Design review
- `refine_design_document` - Design refinement
- `humanize` - Humanize AI text
- `enrich_blog_post` - Blog enhancement
- `clean_text` - Text cleanup

### Rating/Judgment (8 patterns)
- `rate_ai_response` - Rate AI outputs
- `rate_ai_result` - Rate AI results
- `rate_content` - Rate content quality
- `rate_value` - Rate value proposition
- `judge_output` - General judgment
- `label_and_rate` - Label and rate
- `check_agreement` - Agreement checking
- `arbiter-evaluate-quality` - Quality evaluation

## 🔄 Updating Patterns

The Fabric repository is included in this skill at `${PAI_DIR}/skills/fabric/fabric-repo/`.

**To update patterns:**

```bash
cd ${PAI_DIR}/skills/fabric/fabric-repo
git pull origin main
```

**To see all available patterns:**

```bash
ls ${PAI_DIR}/skills/fabric/fabric-repo/data/patterns/
# OR from your local Fabric install:
ls ~/.config/fabric/patterns/
```

## 💡 Usage Examples

**Threat Modeling:**
```bash
# User: "Create a threat model for our new API"
fabric "API that handles user authentication and payment processing" -p create_threat_model
```

**Summarization:**
```bash
# User: "Summarize this blog post"
fabric -u "https://example.com/blog-post" -p summarize

# User: "Give me a 5-sentence summary"
fabric -u "https://example.com/article" -p create_5_sentence_summary
```

**Wisdom Extraction:**
```bash
# User: "Extract wisdom from this video"
fabric -y "https://youtube.com/watch?v=..." -p extract_wisdom

# User: "What are the main ideas?"
fabric -u "URL" -p extract_main_idea
```

**Analysis:**
```bash
# User: "Analyze this code for issues"
fabric "$(cat code.py)" -p analyze_code

# User: "Analyze these security claims"
fabric "security claims text" -p analyze_claims
```

## 🎯 Pattern Selection Decision Matrix

| User Request Contains | Likely Intent | Recommended Patterns |
|----------------------|---------------|----------------------|
| "threat model" | Security modeling | `create_threat_model`, `create_stride_threat_model` |
| "summarize", "summary" | Summarization | `summarize`, `create_5_sentence_summary` |
| "extract wisdom", "insights" | Wisdom extraction | `extract_wisdom`, `extract_insights` |
| "analyze [X]" | Analysis | `analyze_[X]` (match X to pattern) |
| "improve", "enhance" | Improvement | `improve_writing`, `improve_prompt` |
| "create [visualization]" | Visualization | `create_mermaid_visualization`, `create_markmap_visualization` |
| "rate", "judge", "evaluate" | Rating | `rate_content`, `judge_output` |
| "main idea", "core message" | Core extraction | `extract_main_idea`, `extract_core_message` |

## 🚀 Advanced Usage

**Pipe content through Fabric:**
```bash
cat article.txt | fabric -p extract_wisdom
pbpaste | fabric -p summarize
curl -s "https://..." | fabric -p analyze_claims
```

**Process YouTube videos:**
```bash
# Fabric handles download + transcription + processing
fabric -y "https://youtube.com/watch?v=..." -p youtube_summary
```

**Chain patterns (manual):**
```bash
# Extract then summarize
fabric -u "URL" -p extract_wisdom > wisdom.txt
cat wisdom.txt | fabric -p create_5_sentence_summary
```

## 📖 Supplementary Resources

**Full Pattern List:** `ls ${PAI_DIR}/skills/fabric/fabric-repo/data/patterns/`
**Fabric Repo:** `${PAI_DIR}/skills/fabric/fabric-repo/`
**Fabric Documentation:** https://github.com/danielmiessler/fabric
**Pattern Templates:** See `${PAI_DIR}/skills/fabric/fabric-repo/data/patterns/official_pattern_template/`

## 🔑 Key Insight

**The skill's value is in selecting the RIGHT pattern for the task.**

When user says "Create a threat model using Fabric", your job is to:
1. Recognize "threat model" intent
2. Know available options: `create_threat_model`, `create_stride_threat_model`, `create_threat_scenarios`
3. Select the best match (usually `create_threat_model` unless STRIDE specified)
4. Execute: `fabric "[content]" -p create_threat_model`

**Not:** "Here are the patterns, pick one"
**Instead:** "I'll use `create_threat_model` for this" → execute immediately

Overview

This skill intelligently selects the correct Fabric CLI pattern from 242+ specialized prompts to match your intent. It routes user requests like threat modeling, summarization, extraction, analysis, and content creation to the optimal pattern and runs Fabric with the chosen pattern. The result is faster, more accurate processing with minimal manual pattern hunting.

How this skill works

The skill inspects the user's request to identify an intent category (threat modeling, summarization, extraction, analysis, creation, improvement, or rating). It maps that intent to a recommended pattern (for example, create_threat_model, summarize, extract_wisdom, analyze_code) and then executes Fabric using the selected pattern and input source (text, URL, file, or YouTube). It supports piping content and chaining patterns for multi-step workflows.

When to use it

  • When you need the best Fabric pattern chosen automatically for a task.
  • When processing content from text, URLs, files, or YouTube for summaries or insights.
  • When creating threat models, SIGMA rules, Semgrep or Nuclei templates.
  • When analyzing code, malware, logs, papers, or claims for detailed findings.
  • When extracting main ideas, recommendations, controversial points, or business ideas.

Best practices

  • Provide a concise description of the input and desired outcome to improve pattern selection.
  • Indicate explicit formats when needed (e.g., 'STRIDE' for STRIDE threat models or '5-sentence' for ultra-concise summaries).
  • Pipe clean text or provide a URL/YouTube link instead of raw screenshots for better results.
  • Update local patterns periodically to use the latest templates before running complex tasks.
  • Chain patterns manually for multi-step workflows (extract then summarize) when you need staged outputs.

Example use cases

  • Create a threat model for a new API using create_threat_model or create_stride_threat_model.
  • Summarize a long article or YouTube video with summarize or youtube_summary.
  • Extract key insights and recommendations from a research paper with extract_insights or extract_recommendations.
  • Analyze suspicious code or malware with analyze_code or analyze_malware and receive actionable findings.
  • Generate SIGMA, Semgrep, or Nuclei detection rules from observables using the relevant security patterns.

FAQ

How do I provide content to Fabric?

You can pass direct text, pipe file contents, supply a URL with -u, or provide a YouTube link with -y; the skill runs Fabric with the selected pattern.

Can I override the selected pattern?

Yes. If you prefer a different pattern, specify it explicitly. The skill recommends a best match but respects direct pattern choices.