{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-fabric","slug":"fabric","name":"fabric","type":"cli","url":"https://github.com/danielmiessler/fabric/","page_url":"https://unfragile.ai/fabric","categories":["automation"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-fabric__cap_0","uri":"capability://text.generation.language.prompt.pattern.library.application.via.cli","name":"prompt-pattern library application via cli","description":"Applies curated, community-maintained prompt patterns to user input through a command-line interface. Fabric maintains a versioned library of tested prompts (stored as markdown files with embedded instructions) that users invoke by name, passing stdin or file content as context. The CLI resolves pattern names to prompt templates, injects user input, and routes to configured LLM backends (OpenAI, Anthropic, Ollama, etc.), returning structured or unstructured output based on pattern definition.","intents":["Apply a specific prompt pattern to text without writing the prompt myself","Reuse battle-tested prompts across multiple documents or projects","Chain multiple prompt patterns together in a shell pipeline","Discover what prompt patterns are available for my use case"],"best_for":["Terminal-native developers and DevOps engineers who want AI assistance without leaving the shell","Teams standardizing on prompt patterns across projects","Users building shell-based automation workflows with AI steps"],"limitations":["Pattern library is community-maintained, so quality and coverage vary by domain","No built-in versioning or rollback for patterns — updates apply globally unless manually pinned","CLI-first design means limited GUI discoverability compared to web-based prompt marketplaces","Pattern composition requires manual shell piping; no declarative workflow definition language"],"requires":["Python 3.8+ or Node.js 16+ (depending on implementation)","API key for at least one LLM provider (OpenAI, Anthropic, Ollama, etc.)","Git (to clone/update the fabric patterns repository)","Unix-like shell (bash, zsh, fish) for piping and integration"],"input_types":["plain text (stdin or file)","code snippets","markdown documents","structured data (JSON, YAML)"],"output_types":["plain text","markdown","JSON (if pattern specifies structured output)","code"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fabric__cap_1","uri":"capability://tool.use.integration.multi.backend.llm.abstraction.layer","name":"multi-backend llm abstraction layer","description":"Provides a unified CLI interface that abstracts away differences between multiple LLM providers (OpenAI, Anthropic, Ollama, local models, etc.). Fabric detects or accepts a configured backend, translates prompt patterns into provider-specific API calls (handling token limits, model-specific parameters, and response formats), and normalizes output regardless of backend. This allows users to swap providers without rewriting patterns or CLI commands.","intents":["Use the same prompt pattern with different LLM providers without rewriting the pattern","Switch from OpenAI to a local Ollama instance to reduce costs or improve privacy","Fall back to a secondary provider if the primary one is rate-limited or unavailable","Compare outputs from different models on the same prompt without manual API calls"],"best_for":["Cost-conscious teams wanting to experiment with cheaper or local models","Privacy-focused organizations requiring on-premises LLM inference","Developers building LLM applications who want provider portability"],"limitations":["Abstraction layer cannot fully hide provider-specific capabilities (e.g., vision models, function calling) — patterns may need provider-specific variants","Response normalization adds latency (~50-200ms) for format translation and error handling","No built-in load balancing or automatic failover — requires manual configuration or wrapper scripts","Token counting and cost estimation vary by provider; no unified metering"],"requires":["Configuration file specifying default provider and API keys (e.g., ~/.fabric/config.yml)","API credentials for at least one supported provider","Network access to provider APIs (or local Ollama instance running on localhost:11434)"],"input_types":["plain text","code","markdown"],"output_types":["plain text","structured data (provider-dependent)"],"categories":["tool-use-integration","llm-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fabric__cap_2","uri":"capability://search.retrieval.pattern.discovery.and.listing","name":"pattern discovery and listing","description":"Provides CLI commands to list, search, and describe available prompt patterns in the local or remote pattern library. Fabric scans the patterns directory (typically ~/.fabric/patterns or a cloned Git repository), parses pattern metadata (name, description, tags), and presents them via commands like `fabric --list` or `fabric --search <keyword>`. Users can inspect pattern definitions before applying them, reducing trial-and-error.","intents":["Find a prompt pattern for a specific task (e.g., 'summarize', 'extract', 'refactor')","View the full prompt template before running it to understand what it will do","See all available patterns and their descriptions to discover new use cases","Search patterns by tag or keyword to narrow down options"],"best_for":["New users onboarding to fabric who need to explore available patterns","Teams standardizing on a curated subset of patterns and needing discoverability","Developers building custom patterns who want to avoid naming conflicts"],"limitations":["Search is basic (keyword/tag matching) — no semantic search or ranking by relevance","Pattern metadata is manually maintained in each pattern file; inconsistent formatting reduces discoverability","No built-in rating or usage statistics to help users choose between similar patterns","Listing all patterns can be slow if the library is very large (100+ patterns)"],"requires":["Local patterns directory populated (via Git clone or manual setup)","Read permissions on pattern files"],"input_types":["search query (text)"],"output_types":["pattern list (text table or JSON)","pattern definition (markdown)"],"categories":["search-retrieval","cli-interface"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fabric__cap_3","uri":"capability://automation.workflow.stdin.stdout.piping.and.shell.integration","name":"stdin/stdout piping and shell integration","description":"Integrates deeply with Unix pipes and shell redirection, accepting input via stdin, file arguments, or clipboard, and outputting results to stdout for further processing. Fabric is designed as a filter in a shell pipeline, allowing users to chain multiple patterns or combine fabric with other CLI tools (grep, sed, jq, etc.) without intermediate files. This enables workflows like `cat document.txt | fabric --pattern summarize | fabric --pattern extract-entities | jq`.","intents":["Pipe text through a prompt pattern as part of a larger shell command","Chain multiple prompt patterns together in a single pipeline","Integrate fabric output with other CLI tools for post-processing","Process files in bulk using shell loops or xargs without writing custom scripts"],"best_for":["DevOps engineers and system administrators building shell-based automation","Data engineers processing large text datasets with AI steps","Terminal-native developers who prefer CLI tools over GUI applications"],"limitations":["Piping large files (>10MB) may cause memory issues or timeout if LLM processing is slow","No built-in streaming output — entire response is buffered before printing, limiting real-time feedback","Error handling in pipelines can be fragile; a failed LLM call breaks the entire pipeline unless explicitly handled with shell conditionals","Binary data (images, audio) cannot be piped directly; requires file-based input"],"requires":["Unix-like shell (bash, zsh, fish, etc.)","Standard Unix utilities (cat, grep, sed, jq, xargs, etc.) for advanced pipelines"],"input_types":["stdin (text)","file path (text, code, markdown)","clipboard (if supported)"],"output_types":["stdout (text, JSON, code)"],"categories":["automation-workflow","cli-interface"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fabric__cap_4","uri":"capability://text.generation.language.pattern.templating.and.variable.substitution","name":"pattern templating and variable substitution","description":"Supports embedding variables or placeholders in prompt patterns that are substituted at runtime based on user input, environment variables, or pattern arguments. Patterns can define required or optional parameters (e.g., `{{LANGUAGE}}`, `{{TONE}}`) that users provide via CLI flags or environment variables, allowing a single pattern to be customized for different contexts without duplication. Fabric parses pattern files for template syntax and performs substitution before sending to the LLM.","intents":["Customize a prompt pattern for a specific language, tone, or domain without creating a new pattern","Parameterize patterns so teams can enforce consistent behavior across different use cases","Inject environment-specific values (e.g., company name, project context) into patterns dynamically","Reduce pattern duplication by creating a single parameterized pattern instead of multiple variants"],"best_for":["Teams standardizing on patterns and needing flexibility without pattern explosion","Developers building reusable patterns for internal libraries","Organizations with domain-specific requirements (e.g., legal, medical) that need context injection"],"limitations":["Template syntax is simple (likely basic string substitution) — no conditional logic or loops within patterns","No validation of required parameters at pattern definition time; missing parameters may cause LLM errors","Complex templating scenarios require wrapper scripts or external tools","Documentation of pattern parameters may be inconsistent across the library"],"requires":["Pattern files with template syntax (e.g., `{{VARIABLE}}`)","CLI flags or environment variables to provide parameter values"],"input_types":["pattern definition (markdown with template syntax)","parameter values (CLI flags or env vars)"],"output_types":["instantiated prompt (text)"],"categories":["text-generation-language","templating"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fabric__cap_5","uri":"capability://automation.workflow.local.pattern.repository.management","name":"local pattern repository management","description":"Manages a local Git repository of prompt patterns, allowing users to clone the official fabric patterns library, pull updates, and optionally fork or create custom patterns. Fabric provides commands to initialize, update, and manage the patterns directory, treating it as a version-controlled artifact. Users can pin specific pattern versions, create local overrides, or contribute patterns back to the community via Git workflows.","intents":["Clone the official fabric patterns library to get started with curated prompts","Update patterns to the latest version without manually downloading files","Create custom patterns locally and version-control them alongside application code","Fork the patterns library and maintain a private or organization-specific variant"],"best_for":["Teams wanting to maintain a curated, version-controlled set of patterns","Organizations with custom patterns that need to be shared across projects","Developers contributing patterns back to the fabric community"],"limitations":["Requires Git knowledge; non-technical users may struggle with cloning, branching, and merging","No built-in conflict resolution for pattern updates — manual merging required if local patterns diverge from upstream","Pattern versioning is Git-based; no semantic versioning or changelog for individual patterns","Large pattern libraries (100+ patterns) can slow down Git operations"],"requires":["Git installed and configured","Network access to clone from GitHub (or alternative Git hosting)","Write permissions to the patterns directory"],"input_types":["Git repository URL","pattern file (markdown)"],"output_types":["local patterns directory","Git status/log"],"categories":["automation-workflow","version-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fabric__cap_6","uri":"capability://automation.workflow.batch.processing.and.bulk.pattern.application","name":"batch processing and bulk pattern application","description":"Supports applying a single prompt pattern to multiple input files or documents in sequence, with options for parallel execution or sequential processing. Fabric can iterate over a directory of files, apply a pattern to each, and aggregate or save results. This is typically achieved via shell loops or xargs integration, but fabric may provide built-in batch commands to simplify common scenarios like 'summarize all PDFs in a directory' or 'extract entities from all logs'.","intents":["Apply a summarization pattern to 100 documents without writing a loop","Extract structured data from a batch of files and aggregate results into a single output","Process large datasets with AI patterns while managing rate limits and costs","Parallelize pattern application across multiple files to reduce total runtime"],"best_for":["Data engineers processing large text datasets","Content teams bulk-processing documents (e.g., summarizing articles, extracting metadata)","DevOps engineers automating log analysis or report generation"],"limitations":["Batch processing is primarily shell-based (loops, xargs) rather than a native feature; no built-in progress tracking or error recovery","No automatic rate limiting or cost management — users must manually throttle requests to avoid API quota exhaustion","Parallel execution requires careful shell scripting to avoid overwhelming the LLM API","Output aggregation is manual; no built-in merging or deduplication of results"],"requires":["Shell scripting knowledge (for loops, xargs, GNU parallel, etc.)","Sufficient API quota or rate limits to handle batch volume"],"input_types":["file paths (text, code, markdown)","directory paths"],"output_types":["aggregated results (text, JSON, CSV)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fabric__cap_7","uri":"capability://tool.use.integration.configuration.management.and.provider.setup","name":"configuration management and provider setup","description":"Provides a configuration system (typically YAML or JSON files) where users specify default LLM provider, API keys, model preferences, and other settings. Fabric reads configuration from standard locations (e.g., ~/.fabric/config.yml) and allows per-command overrides via CLI flags. Configuration supports multiple provider profiles, enabling users to switch between OpenAI, Anthropic, Ollama, etc. without editing files each time.","intents":["Set up fabric once with my preferred LLM provider and never think about credentials again","Switch between different LLM providers (e.g., OpenAI for complex tasks, Ollama for simple ones) via CLI flags","Manage API keys securely without hardcoding them in shell scripts or patterns","Configure model-specific parameters (temperature, max tokens, etc.) per provider"],"best_for":["Individual developers setting up fabric for personal use","Teams deploying fabric across multiple machines with consistent configuration","Organizations with multiple LLM provider accounts and complex credential management"],"limitations":["Configuration is file-based; no built-in encryption for API keys (users must rely on OS-level file permissions or external secret managers)","No validation of configuration syntax at startup — invalid configs may cause runtime errors","Environment variable support may be limited; complex credential scenarios require external tools (e.g., pass, 1Password)","Per-command overrides can become verbose for complex setups; no profile-based switching"],"requires":["Configuration file in a standard location (e.g., ~/.fabric/config.yml)","API keys for at least one LLM provider","File system permissions to read/write config files"],"input_types":["configuration file (YAML or JSON)","CLI flags (for overrides)"],"output_types":["resolved configuration (used internally)"],"categories":["tool-use-integration","configuration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fabric__cap_8","uri":"capability://text.generation.language.output.formatting.and.result.post.processing","name":"output formatting and result post-processing","description":"Allows users to specify output format (plain text, JSON, markdown, code) and optionally apply post-processing transformations (e.g., pretty-printing, filtering, extraction). Fabric may support format flags like `--format json` or `--format markdown`, and can pipe output through external tools (jq, sed, etc.) for further transformation. Some patterns may define their own output format expectations.","intents":["Get LLM output in a specific format (JSON, markdown, code) for downstream processing","Pretty-print or reformat output for readability or compatibility with other tools","Extract specific fields from structured output without manual parsing","Save output in a format suitable for documentation, reports, or data pipelines"],"best_for":["Developers building data pipelines that consume fabric output","Content creators needing output in specific formats (markdown for docs, JSON for APIs)","Teams automating report generation or data extraction"],"limitations":["Output format depends on LLM response; no guarantee that JSON output is valid JSON unless the pattern explicitly enforces it","Post-processing is primarily shell-based (jq, sed, etc.); no built-in transformation language","Format conversion (e.g., markdown to HTML) requires external tools","Some patterns may not support all output formats; format compatibility is pattern-specific"],"requires":["CLI flags or pattern definition specifying desired output format","External tools (jq, sed, pandoc, etc.) for advanced post-processing"],"input_types":["LLM response (text)"],"output_types":["formatted output (plain text, JSON, markdown, code, HTML)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-fabric__cap_9","uri":"capability://automation.workflow.pattern.contribution.and.community.sharing","name":"pattern contribution and community sharing","description":"Provides workflows for users to create, test, and contribute new patterns back to the fabric community via Git pull requests. Fabric may include documentation or templates for pattern creation, and the GitHub repository accepts community contributions. This enables a decentralized pattern library where users can discover, use, and improve patterns collaboratively.","intents":["Create a custom pattern for a specific use case and share it with the fabric community","Improve an existing pattern by fixing bugs or adding features, then contribute the fix upstream","Discover patterns created by other users and learn from their prompt engineering","Build a curated set of patterns for a specific domain (e.g., legal, medical) and share it as a fork"],"best_for":["Prompt engineers and AI enthusiasts wanting to share their work","Communities building domain-specific pattern libraries (e.g., legal tech, healthcare)","Organizations maintaining internal pattern libraries and wanting to contribute back"],"limitations":["Contribution process requires Git and GitHub knowledge; barriers to entry for non-technical users","No built-in code review or quality assurance for contributed patterns; community-driven quality control may be inconsistent","Pattern naming and organization are informal; no strict guidelines for pattern structure or metadata","Discoverability of new patterns is limited to GitHub notifications and manual browsing"],"requires":["GitHub account and Git knowledge","Understanding of pattern file format and structure","Ability to write clear documentation and test patterns"],"input_types":["pattern definition (markdown)","pattern metadata (description, tags, examples)"],"output_types":["pull request (GitHub)","merged pattern (in official library)"],"categories":["automation-workflow","community"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+ or Node.js 16+ (depending on implementation)","API key for at least one LLM provider (OpenAI, Anthropic, Ollama, etc.)","Git (to clone/update the fabric patterns repository)","Unix-like shell (bash, zsh, fish) for piping and integration","Configuration file specifying default provider and API keys (e.g., ~/.fabric/config.yml)","API credentials for at least one supported provider","Network access to provider APIs (or local Ollama instance running on localhost:11434)","Local patterns directory populated (via Git clone or manual setup)","Read permissions on pattern files","Unix-like shell (bash, zsh, fish, etc.)"],"failure_modes":["Pattern library is community-maintained, so quality and coverage vary by domain","No built-in versioning or rollback for patterns — updates apply globally unless manually pinned","CLI-first design means limited GUI discoverability compared to web-based prompt marketplaces","Pattern composition requires manual shell piping; no declarative workflow definition language","Abstraction layer cannot fully hide provider-specific capabilities (e.g., vision models, function calling) — patterns may need provider-specific variants","Response normalization adds latency (~50-200ms) for format translation and error handling","No built-in load balancing or automatic failover — requires manual configuration or wrapper scripts","Token counting and cost estimation vary by provider; no unified metering","Search is basic (keyword/tag matching) — no semantic search or ranking by relevance","Pattern metadata is manually maintained in each pattern file; inconsistent formatting reduces discoverability","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.039Z","last_scraped_at":"2026-05-03T14:00:23.056Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=fabric","compare_url":"https://unfragile.ai/compare?artifact=fabric"}},"signature":"yCNNy0A3BDLm7eWJzQXwMFDFd9kJq3JZRWlrnJwc72veTfl9bcvhfhyhlxdc9wfxF481mNzriT0qMPplQtsjAQ==","signedAt":"2026-06-22T04:06:26.894Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/fabric","artifact":"https://unfragile.ai/fabric","verify":"https://unfragile.ai/api/v1/verify?slug=fabric","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}