Agent-Skills.md

Agent Skills: Fabric Skill

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.

UncategorizedID: danielmiessler/PAIPlugin/fabric

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danielmiessler/PAIPlugin
danielmiesslerLicense: MIT
305

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skills/fabric/SKILL.md

Skill Metadata

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:

# 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

# 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:

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

To see all available patterns:

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

💡 Usage Examples

Threat Modeling:

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

Summarization:

# 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:

# 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:

# 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:

cat article.txt | fabric -p extract_wisdom
pbpaste | fabric -p summarize
curl -s "https://..." | fabric -p analyze_claims

Process YouTube videos:

# Fabric handles download + transcription + processing
fabric -y "https://youtube.com/watch?v=..." -p youtube_summary

Chain patterns (manual):

# 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

Fabric Skill Skill | Agent Skills