Fabric is an open-source framework for augmenting humans using AI. It provides a modular system of crowdsourced prompt patterns that solve specific problems—from summarizing content to extracting wisdom to analyzing security threats—all usable from the command line.
Scanned 6/8/2026
Install to Claude Code
npx -y skills add agentskillexchange/skills --skill fabric-ai-prompt-pattern-framework --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Fabric Ai Prompt Pattern Framework?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/agentskillexchange-fabric-ai-prompt-pattern-framework)More formats (shields.io, HTML) on the badges page.
---
name: "Fabric AI Prompt Pattern Framework"
slug: "fabric-ai-prompt-pattern-framework"
description: "Fabric is an open-source framework for augmenting humans using AI. It provides a modular system of crowdsourced prompt patterns that solve specific problems—from summarizing content to extracting wisdom to analyzing security threats—all usable from the command line."
github_stars: 40278
verification: "security_reviewed"
source: "https://github.com/danielmiessler/fabric"
category: "Developer Tools"
framework: "Custom Agents"
tool_ecosystem:
github_repo: "danielmiessler/fabric"
github_stars: 40278
---
# Fabric AI Prompt Pattern Framework
Fabric is an open-source framework for augmenting humans using AI. It provides a modular system of crowdsourced prompt patterns that solve specific problems—from summarizing content to extracting wisdom to analyzing security threats—all usable from the command line.
## Installation
Use the upstream install or setup path that matches your environment:
- brew install fabric-ai
- go install github.com/danielmiessler/fabric/cmd/fabric@latest
- docker run --rm -it kayvan/fabric:latest --version
- docker run --rm -it ghcr.io/ksylvan/fabric:v1.4.305 --version
Requirements and caveats from upstream:
- Keep in mind that many of these were recorded when Fabric was Python-based, so remember to use the current [install instructions](#installation) below.
- [Docker](#docker)
- ### Docker
Basic usage or getting-started notes:
- [Usage](#usage) •
- [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!
- [Usage](#usage)
- Source: https://github.com/danielmiessler/fabric
- Extracted from upstream docs: https://raw.githubusercontent.com/danielmiessler/fabric/HEAD/README.md
## Source
- [Agent Skill Exchange](https://agentskillexchange.com/skills/fabric-ai-prompt-pattern-framework/)
No comments yet. Be the first to comment!
Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.
Adversarial multi-agent planning skill. Self-orchestrates 5 hostile category members (unspecified-low, unspecified-high, deep, ultrabrain, artistry) via team-mode for ruthless cross-critique debate, distills only the defensible insights, then MANDATORILY hands the distilled insight bundle to the `plan` agent for executable plan formalization. Use when planning needs maximum rigor and surfacing of weak assumptions, blind spots, and over-engineering. Triggers: 'hyperplan', 'hpp', '/hyperplan', ...
**Complete production-ready guide for Google Gemini embeddings API** This skill provides comprehensive coverage of the `gemini-embedding-001` model for generating text embeddings, including SDK usage, REST API patterns, batch processing, RAG integration with Cloudflare Vectorize, and advanced use cases like semantic search and document clustering. ---
Interview, source-challenge, verify, save, and ADR-gate fuzzy coding requests into Codex-ready implementation specs. Use when a feature, bugfix, refactor, migration, repo-wide change, or architecture task needs user-verified requirements, source-backed decisions, durable architecture decisions, acceptance criteria, validation commands, rollout notes, saved spec/ADR files, and a Codex execution prompt. Do not use when already fully specified or when the user wants direct implementation now.
Use when a repo needs CodeGraph plus ast-grep for Codex MCP setup, exploration, impact analysis, structural search, or safe refactor planning.