fabric

Fabric Fabric is an open-source AI prompt orchestration framework by Daniel Miessler. It provides a library of reusable AI prompts called Patterns — each designed for a specific real-world task — wired into a simple Unix pipeline with stdin/stdout. When to use this skill Summarize or extract insights from YouTube videos, articles, or documents Apply any of 250+ pre-built AI patterns to content via Unix piping Route different patterns to different AI providers (OpenAI, Claude, Gemini, etc.) Create custom patterns for repeatable AI workflows Run Fabric as a REST API server for integration with other tools Process command output, files, or clipboard content through AI patterns Use as an AI agent utility — pipe any tool output through patterns for intelligent summarization Instructions Step 1: Install Fabric # macOS/Linux (one-liner) curl -fsSL https://raw.githubusercontent.com/danielmiessler/fabric/main/scripts/installer/install.sh | bash # macOS via Homebrew brew install fabric-ai # Windows winget install danielmiessler.Fabric # After install — configure API keys and default model fabric --setup Step 2: Learn the core pipeline workflow Fabric works as a Unix pipe. Feed content through stdin and specify a pattern: # Summarize a file cat article.txt | fabric -p summarize # Stream output in real time cat document.txt | fabric -p extract_wisdom --stream # Pipe any command output through a pattern git log --oneline -20 | fabric -p summarize # Process clipboard (macOS) pbpaste | fabric -p summarize # Pipe from curl curl -s https://example.com/article | fabric -p summarize Step 3: Discover patterns # List all available patterns fabric -l # Update patterns from the repository fabric -u # Search patterns by keyword fabric -l | grep summary fabric -l | grep code fabric -l | grep security Key patterns: Pattern Purpose summarize Summarize any content into key points extract_wisdom Extract insights, quotes, habits, and lessons analyze_paper Break down academic papers into actionable insights explain_code Explain code in plain language write_essay Write essays from a topic or rough notes clean_text Remove noise and formatting from raw text analyze_claims Fact-check and assess credibility of claims create_summary Create a structured, markdown summary rate_content Rate and score content quality label_and_rate Categorize and score content improve_writing Polish and improve text clarity create_tags Generate relevant tags for content ask_secure_by_design Review code or systems for security issues capture_thinkers_work Extract the core ideas of a thinker or author create_investigation_visualization Create a visual map of complex investigations Step 4: Process YouTube videos # Summarize a YouTube video fabric -y "https://youtube.com/watch?v=VIDEO_ID" -p summarize # Extract key insights from a video fabric -y "https://youtube.com/watch?v=VIDEO_ID" -p extract_wisdom # Get transcript only (no pattern applied) fabric -y "https://youtube.com/watch?v=VIDEO_ID" --transcript # Transcript with timestamps fabric -y "https://youtube.com/watch?v=VIDEO_ID" --transcript-with-timestamps Step 5: Create custom patterns Each pattern is a directory with a system.md file inside ~/.config/fabric/patterns/. The body should follow this structure: mkdir -p ~/.config/fabric/patterns/my-pattern cat > ~/.config/fabric/patterns/my-pattern/system.md << 'EOF' # IDENTITY AND PURPOSE You are an expert at [task]. Your job is to [specific goal]. Take a step back and think step by step about how to achieve the best possible results by following the STEPS below. # STEPS 1. [Step 1] 2. [Step 2] # OUTPUT INSTRUCTIONS - Only output Markdown. - [Format instruction 2] - Do not give warnings or notes; only output the requested sections. # INPUT INPUT: EOF Use it immediately: echo "input text" | fabric -p my-pattern cat file.txt | fabric -p my-pattern --stream Step 6: Multi-provider routing and advanced usage # Run as REST API server (port 8080 by default) fabric --serve # Use web search capability fabric -p analyze_claims --search "claim to verify" # Per-pattern model routing in /.config/fabric/.env FABRIC_MODEL_PATTERN_SUMMARIZE=anthropic|claude-opus-4-5 FABRIC_MODEL_PATTERN_EXTRACT_WISDOM=openai|gpt-4o FABRIC_MODEL_PATTERN_EXPLAIN_CODE=google|gemini-2.0-flash # Create shell aliases for frequently used patterns alias summarize="fabric -p summarize" alias wisdom="fabric -p extract_wisdom" alias explain="fabric -p explain_code" # Chain patterns cat paper.txt | fabric -p summarize | fabric -p extract_wisdom # Save output cat document.txt | fabric -p extract_wisdom > insights.md Step 7: Use in AI agent workflows Fabric is a powerful utility for AI agents — pipe any tool output through patterns for intelligent analysis: # Analyze test failures npm test 2>&1 | fabric -p analyze_logs # Summarize git history for a PR description git log --oneline origin/main..HEAD | fabric -p create_summary # Explain a code diff git diff HEAD1 | fabric -p explain_code # Summarize build errors make build 2>&1 | fabric -p summarize # Analyze security vulnerabilities in code cat src/auth.py | fabric -p ask_secure_by_design # Process log files cat /var/log/app.log | tail -100 | fabric -p analyze_logs REST API server mode Run Fabric as a microservice and call it from other tools: # Start server fabric --serve --port 8080 # Call via HTTP curl -X POST http://localhost:8080/chat \ -H "Content-Type: application/json" \ -d '{"prompts":[{"userInput":"Summarize this","patternName":"summarize"}]}' Best practices Run fabric -u before first use and regularly to get the latest community patterns. Use --stream for long content to see results progressively instead of waiting. Create shell aliases (alias wisdom="fabric -p extract_wisdom") for your most-used patterns. Use per-pattern model routing to optimize cost vs. quality for each task type. Keep custom patterns in ~/.config/fabric/patterns/ — they persist across updates. For YouTube, transcript extraction works best with videos that have captions enabled. Chain patterns with Unix pipes for multi-step processing workflows. Follow the IDENTITY → STEPS → OUTPUT INSTRUCTIONS structure when creating custom patterns. References Fabric GitHub Pattern Library Installation Guide Custom Pattern Guide Weekly Installs232Repositorysupercent-io/sk…templateGitHub Stars58First Seen8 days agoSecurity AuditsGen Agent Trust HubFailSocketPassSnykWarnInstalled ongemini-cli201codex198opencode189kimi-cli186github-copilot186amp186