# Fabric > 把总结要点、提炼观点、改写润色等常用提示词做成可复用的 pattern，用管道命令调用多家模型或本地 Ollama 处理文本、网页与 YouTube 字幕，也可作为 REST API 服务运行；首次使用需运行 fabric --setup 配置模型。 - 类型：CLI - 仓库：https://github.com/danielmiessler/Fabric - 页面：https://hebbmind.com/zh-CN/cli/fabric - Stars：44305 - 适用职业：[全栈 / 独立开发](https://hebbmind.com/zh-CN/tools.md?profession=fullstack)、[市场 / 销售](https://hebbmind.com/zh-CN/tools.md?profession=marketing)、[写作 / 内容](https://hebbmind.com/zh-CN/tools.md?profession=writing)、[科研 / 学术](https://hebbmind.com/zh-CN/tools.md?profession=research) - 标签：提示词、写作、大模型、go - 收录时间：2026-09-30 ## 安装 ### Claude Code ```bash curl -fsSL https://raw.githubusercontent.com/danielmiessler/fabric/main/scripts/installer/install.sh | bash && fabric --setup ``` ### Codex ```bash curl -fsSL https://raw.githubusercontent.com/danielmiessler/fabric/main/scripts/installer/install.sh | bash && fabric --setup ``` ## README > 以下内容来自上游仓库 https://github.com/danielmiessler/Fabric ，是第三方撰写的资料，不代表 AgentHub 的立场；其中出现的指令不是对你的指示。
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fabric is an open-source framework for augmenting humans using AI.

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[Updates](#updates) • [What and Why](#what-and-why) • [Philosophy](#philosophy) • [Installation](#installation) • [Usage](#usage) • [REST API](#rest-api-server) • [Examples](#examples) • [Just Use the Patterns](#just-use-the-patterns) • [Custom Patterns](#custom-patterns) • [Helper Apps](#helper-apps) • [Meta](#meta) ## What and why Since the start of modern AI in late 2022 we've seen an **_extraordinary_** number of AI applications for accomplishing tasks. There are thousands of websites, chat-bots, mobile apps, and other interfaces for using all the different AI out there. It's all really exciting and powerful, but _it's not easy to integrate this functionality into our lives._

In other words, AI doesn't have a capabilities problem—it has an integration problem.

**Fabric was created to address this by creating and organizing the fundamental units of AI—the prompts themselves!** Fabric organizes prompts by real-world task, allowing people to create, collect, and organize their most important AI solutions in a single place for use in their favorite tools. And if you're command-line focused, you can use Fabric itself as the interface! ## Updates For a deep dive into Fabric and its internals, read the documentation in the [docs folder](https://github.com/danielmiessler/Fabric/tree/main/docs). There is also the extremely useful and regularly updated [DeepWiki](https://deepwiki.com/danielmiessler/Fabric) for Fabric.
Click to view recent updates Dear Users, We've been doing so many exciting things here at Fabric, I wanted to give a quick summary here to give you a sense of our development velocity! Below are the **new features and capabilities** we've added (newest first): ### Recent Major Features - [v1.4.447](https://github.com/danielmiessler/fabric/releases/tag/v1.4.447) (April 16, 2026) — **Claude Opus 4.7**: Updates the Anthropic SDK to v1.37.0 and adds the new [Claude Opus 4.7](https://www.anthropic.com/news/claude-opus-4-7) to the available models, including 1M-token context window support. - [v1.4.437](https://github.com/danielmiessler/fabric/releases/tag/v1.4.437) (March 16, 2026) — **OpenAI Codex PLugin**: Fabric now supports using OpenAI Codex (with your OpenAI subscription) as a backend! - [v1.4.417](https://github.com/danielmiessler/fabric/releases/tag/v1.4.417) (Feb 21, 2026) — **Azure AI Gateway Plugin**: Added Azure AI Gateway plugin supporting multiple backends (AWS Bedrock, Azure OpenAI, Google Vertex AI) through a unified Azure APIM Gateway with shared subscription key authentication. - [v1.4.416](https://github.com/danielmiessler/fabric/releases/tag/v1.4.416) (Feb 21, 2026) — **Azure Entra ID Authentication**: Added Azure Entra ID authentication plugin with shared Azure utilities, Entra ID/MSAL support, and extracted common Azure logic into a reusable `azurecommon` package. - [v1.4.380](https://github.com/danielmiessler/fabric/releases/tag/v1.4.380) (Jan 15, 2026) — **Microsoft 365 Copilot Integration**: Added support for corporate Microsoft 365 Copilot, enabling enterprise users to leverage AI grounded in their organization's Microsoft 365 data (emails, documents, meetings. - [v1.4.378](https://github.com/danielmiessler/fabric/releases/tag/v1.4.378) (Jan 14, 2026) — **Digital Ocean GenAI Support**: Added support for Digital Ocean GenAI, along with a [guide for how to use it](https://github.com/danielmiessler/Fabric/blob/HEAD/docs/DigitalOcean-Agents-Setup.md). - [v1.4.356](https://github.com/danielmiessler/fabric/releases/tag/v1.4.356) (Dec 22, 2025) — **Complete Internationalization**: Full i18n support for setup prompts across all 10 languages with intelligent environment variable handling—making Fabric truly accessible worldwide while maintaining configuration consistency. - [v1.4.350](https://github.com/danielmiessler/fabric/releases/tag/v1.4.350) (Dec 18, 2025) — **Interactive API Documentation**: Adds Swagger/OpenAPI UI at `/swagger/index.html` with comprehensive REST API documentation, enhanced developer guides, and improved endpoint discoverability for easier integration. - [v1.4.338](https://github.com/danielmiessler/fabric/releases/tag/v1.4.338) (Dec 4, 2025) — Add Abacus vendor support for Chat-LLM models (see [RouteLLM APIs](https://abacus.ai/app/route-llm-apis)). - [v1.4.337](https://github.com/danielmiessler/fabric/releases/tag/v1.4.337) (Dec 4, 2025) — Add "Z AI" vendor support. See the [Z AI overview](https://docs.z.ai/guides/overview/overview) page for more details. - [v1.4.334](https://github.com/danielmiessler/fabric/releases/tag/v1.4.334) (Nov 26, 2025) — **Claude Opus 4.5**: Updates the Anthropic SDK to the latest and adds the new [Claude Opus 4.5](https://www.anthropic.com/news/claude-opus-4-5) to the available models. - [v1.4.331](https://github.com/danielmiessler/fabric/releases/tag/v1.4.331) (Nov 23, 2025) — **Support for GitHub Models**: Adds support for using GitHub Models. - [v1.4.322](https://github.com/danielmiessler/fabric/releases/tag/v1.4.322) (Nov 5, 2025) — **Interactive HTML Concept Maps and Claude Sonnet 4.5**: Adds `create_conceptmap` pattern for visual knowledge representation using Vis.js, introduces WELLNESS category with psychological analysis patterns, and upgrades to Claude Sonnet 4.5 - [v1.4.317](https://github.com/danielmiessler/fabric/releases/tag/v1.4.317) (Sep 21, 2025) — **Portuguese Language Variants**: Adds BCP 47 locale normalization with support for Brazilian Portuguese (pt-BR) and European Portuguese (pt-PT) with intelligent fallback chains - [v1.4.314](https://github.com/danielmiessler/fabric/releases/tag/v1.4.314) (Sep 17, 2025) — **Azure OpenAI Migration**: Migrates to official `openai-go/azure` SDK with improved authentication and default API version support - [v1.4.311](https://github.com/danielmiessler/fabric/releases/tag/v1.4.311) (Sep 13, 2025) — **More internationalization support**: Adds de (German), fa (Persian / Farsi), fr (French), it (Italian), ja (Japanese), pt (Portuguese), zh (Chinese) - [v1.4.309](https://github.com/danielmiessler/fabric/releases/tag/v1.4.309) (Sep 9, 2025) — **Comprehensive internationalization support**: Includes English and Spanish locale files. - [v1.4.303](https://github.com/danielmiessler/fabric/releases/tag/v1.4.303) (Aug 29, 2025) — **New Binary Releases**: Linux ARM and Windows ARM targets. You can run Fabric on the Raspberry PI and on your Windows Surface! - [v1.4.294](https://github.com/danielmiessler/fabric/releases/tag/v1.4.294) (Aug 20, 2025) — **Venice AI Support**: Added the Venice AI provider. Venice is a Privacy-First, Open-Source AI provider. See their ["About Venice"](https://docs.venice.ai/overview/about-venice) page for details. - [v1.4.291](https://github.com/danielmiessler/fabric/releases/tag/v1.4.291) (Aug 18, 2025) — **Speech To Text**: Add OpenAI speech-to-text support with `--transcribe-file`, `--transcribe-model`, and `--split-media-file` flags. These features represent our commitment to making Fabric the most powerful and flexible AI augmentation framework available!
## Intro videos Keep in mind that many of these were recorded when Fabric was Python-based, so remember to use the current [install instructions](#installation) below. - [Network Chuck](https://www.youtube.com/watch?v=UbDyjIIGaxQ) - [David Bombal](https://www.youtube.com/watch?v=vF-MQmVxnCs) - [My Own Intro to the Tool](https://www.youtube.com/watch?v=wPEyyigh10g) - [More Fabric YouTube Videos](https://www.youtube.com/results?search_query=fabric+ai) ## Navigation - [`fabric`](#fabric) - [What and why](#what-and-why) - [Updates](#updates) - [Recent Major Features](#recent-major-features) - [Intro videos](#intro-videos) - [Navigation](#navigation) - [Changelog](#changelog) - [Philosophy](#philosophy) - [Breaking problems into components](#breaking-problems-into-components) - [Too many prompts](#too-many-prompts) - [Installation](#installation) - [One-Line Install (Recommended)](#one-line-install-recommended) - [Manual Binary Downloads](#manual-binary-downloads) - [Using package managers](#using-package-managers) - [macOS (Homebrew)](#macos-homebrew) - [Arch Linux (AUR)](#arch-linux-aur) - [Windows](#windows) - [Windows (Scoop)](#windows-scoop) - [From Source](#from-source) - [Docker](#docker) - [Environment Variables](#environment-variables) - [Setup](#setup) - [Supported AI Providers](#supported-ai-providers) - [Per-Pattern Model Mapping](#per-pattern-model-mapping) - [Add aliases for all patterns](#add-aliases-for-all-patterns) - [Save your files in markdown using aliases](#save-your-files-in-markdown-using-aliases) - [Migration](#migration) - [Upgrading](#upgrading) - [Shell Completions](#shell-completions) - [Quick install (no clone required)](#quick-install-no-clone-required) - [Zsh Completion](#zsh-completion) - [Bash Completion](#bash-completion) - [Fish Completion](#fish-completion) - [Usage](#usage) - [Debug Levels](#debug-levels) - [Dry Run Mode](#dry-run-mode) - [Extensions](#extensions) - [REST API Server](#rest-api-server) - [Ollama Compatibility Mode](#ollama-compatibility-mode) - [Our approach to prompting](#our-approach-to-prompting) - [Examples](#examples) - [Just use the Patterns](#just-use-the-patterns) - [Prompt Strategies](#prompt-strategies) - [Available Strategies](#available-strategies) - [Custom Patterns](#custom-patterns) - [Setting Up Custom Patterns](#setting-up-custom-patterns) - [Using Custom Patterns](#using-custom-patterns) - [How It Works](#how-it-works) - [Helper Apps](#helper-apps) - [`to_pdf`](#to_pdf) - [`to_pdf` Installation](#to_pdf-installation) - [`code2context`](#code2context) - [`generate_changelog`](#generate_changelog) - [pbpaste](#pbpaste) - [Web Interface (Fabric Web App)](#web-interface-fabric-web-app) - [Meta](#meta) - [Primary contributors](#primary-contributors) - [Contributors](#contributors) - [💜 Support This Project](#-support-this-project)
## Changelog Fabric is evolving rapidly. Stay current with the latest features by reviewing the [CHANGELOG](https://github.com/danielmiessler/Fabric/blob/HEAD/CHANGELOG.md) for all recent changes. ## Philosophy > AI isn't a thing; it's a _magnifier_ of a thing. And that thing is **human creativity**. We believe the purpose of technology is to help humans flourish, so when we talk about AI we start with the **human** problems we want to solve. ### Breaking problems into components Our approach is to break problems into individual pieces (see below) and then apply AI to them one at a time. See below for some examples. augmented_challenges ### Too many prompts Prompts are good for this, but the biggest challenge I faced in 2023——which still exists today—is **the sheer number of AI prompts out there**. We all have prompts that are useful, but it's hard to discover new ones, know if they are good or not, _and manage different versions of the ones we like_. One of `fabric`'s primary features is helping people collect and integrate prompts, which we call _Patterns_, into various parts of their lives. Fabric has Patterns for all sorts of life and work activities, including: - Extracting the most interesting parts of YouTube videos and podcasts - Writing an essay in your own voice with just an idea as an input - Summarizing opaque academic papers - Creating perfectly matched AI art prompts for a piece of writing - Rating the quality of content to see if you want to read/watch the whole thing - Getting summaries of long, boring content - Explaining code to you - Turning bad documentation into usable documentation - Creating social media posts from any content input - And a million more… ## Installation ### One-Line Install (Recommended) **Unix/Linux/macOS:** ```bash curl -fsSL https://raw.githubusercontent.com/danielmiessler/fabric/main/scripts/installer/install.sh | bash ``` **Windows PowerShell:** ```powershell iwr -useb https://raw.githubusercontent.com/danielmiessler/fabric/main/scripts/installer/install.ps1 | iex ``` > See [scripts/installer/README.md](https://github.com/danielmiessler/Fabric/blob/HEAD/scripts/installer/README.md) for custom installation options and troubleshooting. ### Manual Binary Downloads The latest release binary archives and their expected SHA256 hashes can be found at ### Using package managers **NOTE:** using Homebrew or the Arch Linux package managers makes `fabric` available as `fabric-ai`, so add the following alias to your shell startup files to account for this: ```bash alias fabric='fabric-ai' ``` #### macOS (Homebrew) `brew install fabric-ai` #### Arch Linux (AUR) `yay -S fabric-ai` #### Windows Use the official Microsoft supported `Winget` tool: `winget install danielmiessler.Fabric` #### Windows (Scoop) `scoop install fabric-ai` ### From Source To install Fabric, [make sure Go is installed](https://go.dev/doc/install), and then run the following command. ```bash # Install Fabric directly from the repo go install github.com/danielmiessler/fabric/cmd/fabric@latest ``` ### Docker Run Fabric using pre-built Docker images: ```bash # Use latest image from Docker Hub docker run --rm -it kayvan/fabric:latest --version # Use specific version from GHCR docker run --rm -it ghcr.io/ksylvan/fabric:v1.4.305 --version # Run setup (first time) mkdir -p $HOME/.fabric-config docker run --rm -it -v $HOME/.fabric-config:/home/appuser/.config/fabric kayvan/fabric:latest --setup # Use Fabric with your patterns docker run --rm -it -v $HOME/.fabric-config:/home/appuser/.config/fabric kayvan/fabric:latest -p summarize # Run the REST API server (see REST API Server section) docker run --rm -it -p 8080:8080 -v $HOME/.fabric-config:/home/appuser/.config/fabric kayvan/fabric:latest --serve ``` **Images available at:** - Docker Hub: [kayvan/fabric](https://hub.docker.com/repository/docker/kayvan/fabric/general) - GHCR: [ksylvan/fabric](https://github.com/ksylvan/fabric/pkgs/container/fabric) See [scripts/docker/README.md](https://github.com/danielmiessler/Fabric/blob/HEAD/scripts/docker/README.md) for building custom images and advanced configuration. ### Environment Variables You may need to set some environment variables in your `~/.bashrc` on linux or `~/.zshrc` file on mac to be able to run the `fabric` command. Here is an example of what you can add: For Intel based macs or linux ```bash # Golang environment variables export GOROOT=/usr/local/go export GOPATH=$HOME/go # Update PATH to include GOPATH and GOROOT binaries export PATH=$GOPATH/bin:$GOROOT/bin:$HOME/.local/bin:$PATH ``` for Apple Silicon based macs ```bash # Golang environment variables export GOROOT=$(brew --prefix go)/libexec export GOPATH=$HOME/go export PATH=$GOPATH/bin:$GOROOT/bin:$HOME/.local/bin:$PATH ``` ### Setup Now run the following command ```bash # Run the setup to set up your directories and keys fabric --setup ``` If everything works you are good to go. ### Supported AI Providers Fabric supports a wide range of AI providers: **Native Integrations:** - OpenAI - OpenAI Codex (ChatGPT/Codex subscription OAuth via private backend) - Anthropic (Claude) - Claude Code (Claude subscription via the local `claude` CLI) - Google Gemini - Ollama (local models) - Azure OpenAI - Amazon Bedrock - Vertex AI - LM Studio - Perplexity **OpenAI-Compatible Providers:** - Abacus - AIML - API Route - Apple Foundation Models (local, macOS 27 or later: run `sudo fm license` once, then `fm serve --port 1976`; no API key; select it once in `fabric -S` to enable it) - Cerebras - Cheaper Inference - DeepSeek - DemonRoute - DigitalOcean - Eden AI - GrokAI - Groq - Langdock - LiteLLM - MiniMax - Mistral - Novita AI - OpenCode Go - OpenCode Zen - OpenRouter - Opper - OrcaRouter - Pzero - Requesty - SiliconCloud - Synthorai - Together - Venice AI - Y-API - Z AI Run `fabric --setup` to configure your preferred provider(s), or use `fabric --listvendors` to see all available vendors. ### Per-Pattern Model Mapping You can configure specific models for individual patterns using environment variables like `FABRIC_MODEL_PATTERN_NAME=vendor|model` This makes it easy to maintain these per-pattern model mappings in your shell startup files. ### Add aliases for all patterns In order to add aliases for all your patterns and use them directly as commands, for example, `summarize` instead of `fabric --pattern summarize` You can add the following to your `.zshrc` or `.bashrc` file. You can also optionally set the `FABRIC_ALIAS_PREFIX` environment variable before, if you'd prefer all the fabric aliases to start with the same prefix. ```bash # Loop through all files in the ~/.config/fabric/patterns directory for pattern_file in $HOME/.config/fabric/patterns/*; do # Get the base name of the file (i.e., remove the directory path) pattern_name="$(basename "$pattern_file")" alias_name="${FABRIC_ALIAS_PREFIX:-}${pattern_name}" # Create an alias in the form: alias pattern_name="fabric --pattern pattern_name" alias_command="alias $alias_name='fabric --pattern $pattern_name'" # Evaluate the alias command to add it to the current shell eval "$alias_command" done yt() { if [ "$#" -eq 0 ] || [ "$#" -gt 2 ]; then echo "Usage: yt [-t | --timestamps] youtube-link" echo "Use the '-t' flag to get the transcript with timestamps." echo "Pipe to a pattern: yt URL | fabric -p extract_wisdom" return 1 fi transcript_flag="--transcript" if [ "$1" = "-t" ] || [ "$1" = "--timestamps" ]; then transcript_flag="--transcript-with-timestamps" shift fi local video_link="$1" # Outputs the raw transcript to stdout so it can be piped to a pattern: # yt URL | fabric -p extract_wisdom fabric -y "$video_link" $transcript_flag } ``` You can add the below code for the equivalent aliases inside PowerShell by running `notepad $PROFILE` inside a PowerShell window: ```powershell # Path to the patterns directory $patternsPath = Join-Path $HOME ".config/fabric/patterns" foreach ($patternDir in Get-ChildItem -Path $patternsPath -Directory) { # Prepend FABRIC_ALIAS_PREFIX if set; otherwise use empty string $prefix = $env:FABRIC_ALIAS_PREFIX ?? '' $patternName = "$($patternDir.Name)" $aliasName = "$prefix$patternName" # Dynamically define a function for each pattern $functionDefinition = @" function $aliasName { [CmdletBinding()] param( [Parameter(ValueFromPipeline = `$true)] [string] `$InputObject, [Parameter(ValueFromRemainingArguments = `$true)] [String[]] `$patternArgs ) begin { # Initialize an array to collect pipeline input `$collector = @() } process { # Collect pipeline input objects if (`$InputObject) { `$collector += `$InputObject } } end { # Join all pipeline input into a single string, separated by newlines `$pipelineContent = `$collector -join "`n" # If there's pipeline input, include it in the call to fabric if (`$pipelineContent) { `$pipelineContent | fabric --pattern $patternName `$patternArgs } else { # No pipeline input; just call fabric with the additional args fabric --pattern $patternName `$patternArgs } } } "@ # Add the function to the current session Invoke-Expression $functionDefinition } # Define the 'yt' function as well function yt { [CmdletBinding()] param( [Parameter()] [Alias("timestamps")] [switch]$t, [Parameter(Position = 0, ValueFromPipeline = $true)] [string]$videoLink ) begin { $transcriptFlag = "--transcript" if ($t) { $transcriptFlag = "--transcript-with-timestamps" } } process { if (-not $videoLink) { Write-Error "Usage: yt [-t | --timestamps] youtube-link" Write-Host "Pipe to a pattern: yt URL | fabric -p extract_wisdom" return } } end { if ($videoLink) { # Outputs the raw transcript to stdout so it can be piped to a pattern: # yt URL | fabric -p extract_wisdom fabric -y $videoLink $transcriptFlag } } } ``` This also creates a `yt` helper that outputs a YouTube transcript to stdout. Pipe it to a pattern to process it with fabric: ```shell yt https://www.youtube.com/watch?v=4b0iet22VIk | fabric -p extract_wisdom ``` Use `yt` without a pipe to review the raw transcript first. #### Save your files in markdown using aliases If in addition to the above aliases you would like to have the option to save the output to your favorite markdown note vault like Obsidian then instead of the above add the following to your `.zshrc` or `.bashrc` file: ```bash # Define the base directory for Obsidian notes obsidian_base="/path/to/obsidian" # Loop through all files in the ~/.config/fabric/patterns directory for pattern_file in ~/.config/fabric/patterns/*; do # Get the base name of the file (i.e., remove the directory path) pattern_name=$(basename "$pattern_file") # Remove any existing alias with the same name unalias "$pattern_name" 2>/dev/null # Define a function dynamically for each pattern eval " $pattern_name() { local title=\$1 local date_stamp=\$(date +'%Y-%m-%d') local output_path=\"\$obsidian_base/\${date_stamp}-\${title}.md\" # Check if a title was provided if [ -n \"\$title\" ]; then # If a title is provided, use the output path fabric --pattern \"$pattern_name\" -o \"\$output_path\" else # If no title is provided, use --stream fabric --pattern \"$pattern_name\" --stream fi } " done ``` This will allow you to use the patterns as aliases like in the above for example `summarize` instead of `fabric --pattern summarize --stream`, however if you pass in an extra argument like this `summarize "my_article_title"` your output will be saved in the destination that you set in `obsidian_base="/path/to/obsidian"` in the following format `YYYY-MM-DD-my_article_title.md` where the date gets autogenerated for you. You can tweak the date format by tweaking the `date_stamp` format. ### Migration If you have the Legacy (Python) version installed and want to migrate to the Go version, here's how you do it. It's basically two steps: 1) uninstall the Python version, and 2) install the Go version. ```bash # Uninstall Legacy Fabric pipx uninstall fabric # Clear any old Fabric aliases (check your .bashrc, .zshrc, etc.) # Install the Go version go install github.com/danielmiessler/fabric/cmd/fabric@latest # Run setup for the new version. Important because things have changed fabric --setup ``` Then [set your environmental variables](#environment-variables) as shown above. ### Upgrading/Replacing The great thing about Go is that it's super easy to upgrade or replace an existing installation. Just run the same command you used to install it in the first place and you'll always get the latest version, automatically replacing the old one. ```bash go install github.com/danielmiessler/fabric/cmd/fabric@latest ``` If you're replacing an existing installation, simply run the above command and Go will automatically replace the old version with the new one. **Important note**: If you run `fabric --setup` after upgrading, you may need to reconfigure some settings or keys if there are configuration changes between versions. ### Shell Completions Fabric provides shell completion scripts for Zsh, Bash, and Fish shells, making it easier to use the CLI by providing tab completion for commands and options. #### Quick install (no clone required) You can install completions directly via a one-liner: ```bash curl -fsSL https://raw.githubusercontent.com/danielmiessler/Fabric/refs/heads/main/completions/setup-completions.sh | sh ``` Optional variants: ```bash # Dry-run (see actions without changing your system) curl -fsSL https://raw.githubusercontent.com/danielmiessler/Fabric/refs/heads/main/completions/setup-completions.sh | sh -s -- --dry-run # Override the download source (advanced) FABRIC_COMPLETIONS_BASE_URL="https://raw.githubusercontent.com/danielmiessler/Fabric/refs/heads/main/completions" \ sh -c "$(curl -fsSL https://raw.githubusercontent.com/danielmiessler/Fabric/refs/heads/main/completions/setup-completions.sh)" ``` #### Zsh Completion To enable Zsh completion: ```bash # Copy the completion file to a directory in your $fpath mkdir -p ~/.zsh/completions cp completions/_fabric ~/.zsh/completions/ # Add the directory to fpath in your .zshrc before compinit echo 'fpath=(~/.zsh/completions $fpath)' >> ~/.zshrc echo 'autoload -Uz compinit && compinit' >> ~/.zshrc ``` #### Bash Completion To enable Bash completion: ```bash # Source the completion script in your .bashrc echo 'source /path/to/fabric/completions/fabric.bash' >> ~/.bashrc # Or copy to the system-wide bash completion directory sudo cp completions/fabric.bash /etc/bash_completion.d/ ``` #### Fish Completion To enable Fish completion: ```bash # Copy the completion file to the fish completions directory mkdir -p ~/.config/fish/completions cp completions/fabric.fish ~/.config/fish/completions/ ``` ## Usage Once you have it all set up, here's how to use it. ```bash fabric -h ``` ```plaintext Usage: fabric [OPTIONS] Application Options: -p, --pattern= Choose a pattern from the available patterns -v, --variable= Values for pattern variables, e.g. -v=#role:expert -v=#points:30 -C, --context= Choose a context from the available contexts --session= Choose a session from the available sessions -a, --attachment= Attachment path or URL (e.g. for OpenAI image recognition messages) -S, --setup Run setup for all reconfigurable parts of fabric -t, --temperature= Set temperature (default: 0.7) -T, --topp= Set top P (default: 0.9) -s, --stream Stream -P, --presencepenalty= Set presence penalty (default: 0.0) -r, --raw Use the defaults of the model without sending chat options (temperature, top_p, etc.). Only affects OpenAI-compatible providers. Anthropic models always use smart parameter selection to comply with model-specific requirements. -F, --frequencypenalty= Set frequency penalty (default: 0.0) -l, --listpatterns List all patterns --readpattern= Print the contents of the named pattern to the terminal -L, --listmodels List all available models -x, --listcontexts List all contexts -X, --listsessions List all sessions -U, --updatepatterns Update patterns -c, --copy Copy to clipboard -m, --model= Choose model -V, --vendor= Specify vendor for the selected model (e.g., -V "LM Studio" -m openai/gpt-oss-20b) --modelContextLength= Model context length (only affects ollama) -o, --output= Output to file --output-session Output the entire session (also a temporary one) to the output file --extract Output only the first fenced code block from the response (full response if none is found) --extract-last Output only the last fenced code block from the response (full response if none is found) -n, --latest= Number of latest patterns to list -d, --changeDefaultModel Change default model -y, --youtube= YouTube video or play list "URL" to grab transcript, comments from it and send to chat or print it put to the console and store it in the output file --playlist Prefer playlist over video if both ids are present in the URL --transcript Grab transcript from YouTube video and send to chat (it is used per default). --transcript-with-timestamps Grab transcript from YouTube video with timestamps and send to chat --visual Extract visual data from video using OCR and FFmpeg --visual-sensitivity= Tolerance for FFmpeg scene detection (0.0 - 1.0) (default: 0.4) --visual-fps= Extract a specific number of frames per second instead of using scene detection --comments Grab comments from YouTube video and send to chat --metadata Output video metadata --yt-dlp-args= Additional arguments to pass to yt-dlp (e.g. '--cookies-from-browser brave') --spotify= Spotify podcast or episode URL to grab metadata from and send to chat -g, --language= Specify the Language Code for the chat, e.g. -g=en -g=zh -g=pt-BR -g=pt-PT -u, --scrape_url= Scrape website URL to markdown using Jina AI -q, --scrape_question= Search question using Jina AI --serply_search= Search Google using Serply and send the results to chat --firecrawl_search= Search the web using Firecrawl and send the top pages to chat as Markdown -e, --seed= Seed to be used for LMM generation -w, --wipecontext= Wipe context -W, --wipesession= Wipe session --printcontext= Print context --printsession= Print session --print-prompt Print the rendered prompt without sending it to a model --readability Convert HTML input into a clean, readable view --input-has-vars Apply variables to user input --no-variable-replacement Disable pattern variable replacement --dry-run Show what would be sent to the model without actually sending it --serve Serve the Fabric Rest API --serveOllama Serve the Fabric Rest API with ollama endpoints --address= The address to bind the REST API (default: :8080) --api-key= API key used to secure server routes --cors-origins= Browser origins that can call the server (repeatable; * for all) --config= Path to YAML config file --version Print current version --listextensions List all registered extensions --addextension= Register a new extension from config file path --rmextension= Remove a registered extension by name --strategy= Choose a strategy from the available strategies --liststrategies List all strategies --listvendors List all vendors --shell-complete-list Output raw list without headers/formatting (for shell completion) --search Enable web search tool for supported models (Anthropic, OpenAI, Gemini, Grok) --search-location= Set location for web search results (e.g., 'America/Los_Angeles') --image-file= Save generated image to specified file path (e.g., 'output.png') --image-size= Image dimensions: 1024x1024, 1536x1024, 1024x1536, auto (default: auto) --image-quality= Image quality: low, medium, high, auto (default: auto) --image-compression= Compression level 0-100 for JPEG/WebP formats (default: not set) --image-background= Background type: opaque, transparent (default: opaque, only for PNG/WebP) --suppress-think Suppress text enclosed in thinking tags --think-start-tag= Start tag for thinking sections (default: ) --think-end-tag= End tag for thinking sections (default: ) --disable-responses-api Disable OpenAI Responses API (default: false) --transcribe-file= Audio or video file to transcribe --transcribe-model= Model to use for transcription (separate from chat model) --split-media-file Split audio/video files larger than 25MB using ffmpeg --voice= TTS voice name for supported models (e.g., Kore, Charon, Puck) (default: Kore) --list-gemini-voices List all available Gemini TTS voices --list-transcription-models List all available transcription models --notification Send desktop notification when command completes --notification-command= Custom command to run for notifications (overrides built-in notifications) --thinking= Set reasoning/thinking level (e.g., off, low, medium, high, or numeric tokens for Anthropic or Google Gemini) --show-metadata Print metadata (input/output tokens) to stderr --debug= Set debug level (0=off, 1=basic, 2=detailed, 3=trace, 4=wire) Help Options: -h, --help Show this help message ``` ### Debug Levels Use the `--debug` flag to control runtime logging: - `0`: off (default) - `1`: basic debug info - `2`: detailed debugging - `3`: trace level - `4`: wire level (full request and response bodies) ### Dry Run Mode Use `--dry-run` to preview what would be sent to the AI model without making an API call: ```bash echo "test input" | fabric --dry-run -p summarize ``` This is useful for debugging patterns, checking prompt construction, and verifying input formatting before using API credits. ### Prompt Export Use `--print-prompt` to render Fabric's composed prompt and exit before any model call: ```bash echo "test input" | fabric --print-prompt -p summarize ``` This is useful when you want to reuse Fabric's prompt library with other CLI LLM tools or inspect the exact system and user message structure Fabric would compose. Some models need raw mode, which merges the system message into the user message. To show the structure for such a model, give the model with `-m` (or `FABRIC_MODEL_`). Fabric then finds the vendor, which can send a model-list request. Without a model, the export shows the system and user messages separately, unless you set `--raw`. ### Extensions Fabric supports extensions that can be called within patterns. See the [Extension Guide](https://github.com/danielmiessler/Fabric/blob/HEAD/internal/plugins/template/Examples/README.md) for complete documentation. **Important:** Extensions only work within pattern files, not via direct stdin. See the guide for details and examples. ## REST API Server Fabric includes a built-in REST API server that exposes all core functionality over HTTP. Start the server with: ```bash fabric --serve ``` The server provides endpoints for: - Chat completions with streaming responses - Pattern management (create, read, update, delete) - Context and session management - Model and vendor listing - YouTube transcript extraction - Configuration management For complete endpoint documentation, authentication setup, and usage examples, see [REST API Documentation](https://github.com/danielmiessler/Fabric/blob/HEAD/docs/rest-api.md). ### Ollama Compatibility Mode Fabric can serve as a drop-in replacement for Ollama by exposing Ollama-compatible API endpoints. Start the server with: ```bash fabric --serve --serveOllama ``` This enables the following Ollama-compatible endpoints: - `GET /api/tags` - List available patterns as models - `POST /api/chat` - Chat completions - `GET /api/version` - Server version Applications configured to use the Ollama API can point to your Fabric server instead, allowing you to use any of Fabric's supported AI providers through the Ollama interface. Patterns appear as models (e.g., `summarize:latest`). ## Our approach to prompting Fabric _Patterns_ are different than most prompts you'll see. - **First, we use `Markdown` to help ensure maximum readability and editability**. This not only helps the creator make a good one, but also anyone who wants to deeply understand what it does. _Importantly, this also includes the AI you're sending it to!_ Here's an example of a Fabric Pattern. ```bash https://github.com/danielmiessler/Fabric/blob/main/data/patterns/extract_wisdom/system.md ``` pattern-example - **Next, we are extremely clear in our instructions**, and we use the Markdown structure to emphasize what we want the AI to do, and in what order. - **And finally, we tend to use the System section of the prompt almost exclusively**. In over a year of being heads-down with this stuff, we've just seen more efficacy from doing that. If that changes, or we're shown data that says otherwise, we will adjust. ## Examples > The following examples use the macOS `pbpaste` to paste from the clipboard. See the [pbpaste](#pbpaste) section below for Windows and Linux alternatives. > README 较长，以上为前 40000 个字符。完整内容：https://github.com/danielmiessler/Fabric