A distillation of the Claude Fable 5 workflow into actionable skills designed to guide AI agents through a systematic think, act, and prove methodology. The framework improves agentic reliability by enforcing specific sequences like classifying tasks, gathering parallel evidence from primary sources, making surgical edits rather than broad changes, and undergoing adversarial verification via an automated judge. It includes specialized domain adapters for sectors such as coding, research, marketing, and DevOps to tailor the reasoning process to specific professional requirements. The method is specifically designed to mitigate common LLM failures like reward hacking, silent code errors in tests, and false claims of task completion.
>"Before Fable 5 was deprecated, it wrote down its own problem-solving method. Step by step. How it classifies a task, defines "done," gathers evidence, commits to one recommendation, makes the smallest correct change, verifies by observation, and reports the outcome honestly."
- Core skills: fable-method (thinking), fable-loop (orchestration), fable-judge (verification), and fable-domain (adapter generation)
- Focuses on preventing unauthorized staging or "reward hacking" through strict observation cycles
- Validated against 260+ agent runs using blind LLM judges to verify results via code execution rather than reports
>"Before Fable 5 was deprecated, it wrote down its own problem-solving method. Step by step. How it classifies a task, defines "done," gathers evidence, commits to one recommendation, makes the smallest correct change, verifies by observation, and reports the outcome honestly."
- Core skills: fable-method (thinking), fable-loop (orchestration), fable-judge (verification), and fable-domain (adapter generation)
- Focuses on preventing unauthorized staging or "reward hacking" through strict observation cycles
- Validated against 260+ agent runs using blind LLM judges to verify results via code execution rather than reports
A directory of specialized scripts and capabilities designed for AI agents within the agent-scripts repository. These skills provide automated workflows across various domains including web browsing, software development processes like code review and debugging, system maintenance, and integrations with platforms such as WhatsApp, Discord, and Sonos.
Main topics include:
Browser automation and web interaction
Developer productivity tools for GitHub and coding workflows
Platform-specific automations for messaging and smart home devices
System utility scripts for macOS and developer environments
Main topics include:
Browser automation and web interaction
Developer productivity tools for GitHub and coding workflows
Platform-specific automations for messaging and smart home devices
System utility scripts for macOS and developer environments
This tutorial demonstrates how to construct a complete skill-based agent system for large language models using Python. It explores structuring modular capabilities similar to an operating system, where reusable skills are defined with metadata and schemas, registered centrally, and orchestrated through dynamic tool calling and multi-step reasoning. The implementation covers composing multiple skills for advanced workflows, hot-loading new capabilities at runtime, and monitoring performance via an observability dashboard.
How to use AI skills—reusable packages of instructions and files—to automate repetitive data science workflows. By moving beyond simple prompting into structured skills, users can maintain shorter context windows while ensuring consistent, high-quality outputs for complex tasks like data visualization or metric investigation.
* A skill consists of a SKILL.md file with metadata and detailed instructions to guide an AI through specific recurring processes.
* Using skills helps keep the main LLM context lightweight by only loading detailed resources when they are relevant to the task.
* The author demonstrates this by automating a weekly visualization habit, reducing a one-hour manual process to less than ten minutes.
* Building effective skills requires iterative testing, incorporating personal domain knowledge, and researching external best practices.
* Combining skills with Model Context Protocol (MCP) allows AI to both follow specific procedural playbooks and access external data tools seamlessly.
* A skill consists of a SKILL.md file with metadata and detailed instructions to guide an AI through specific recurring processes.
* Using skills helps keep the main LLM context lightweight by only loading detailed resources when they are relevant to the task.
* The author demonstrates this by automating a weekly visualization habit, reducing a one-hour manual process to less than ten minutes.
* Building effective skills requires iterative testing, incorporating personal domain knowledge, and researching external best practices.
* Combining skills with Model Context Protocol (MCP) allows AI to both follow specific procedural playbooks and access external data tools seamlessly.
A collection of specialized skills designed to improve how AI coding agents handle frontend development. Instead of producing generic or uninspired interfaces, these instructions enable AI tools to generate modern, premium designs characterized by high visual quality, proper spacing, and sophisticated animations. The system is framework-agnostic and works across major AI agents like Cursor, Claude Code, and GitHub Copilot via a simple CLI installation.
Main features include:
- Specialized skill variants for different design aesthetics such as soft UI, minimalist editorial styles, and brutalist interfaces.
- A three-dial parameterization system to adjust design variance, motion intensity, and visual density.
- An output-skill designed to prevent AI laziness by stopping placeholder comments and skipped code blocks.
Main features include:
- Specialized skill variants for different design aesthetics such as soft UI, minimalist editorial styles, and brutalist interfaces.
- A three-dial parameterization system to adjust design variance, motion intensity, and visual density.
- An output-skill designed to prevent AI laziness by stopping placeholder comments and skipped code blocks.
Meta’s new “semi-formal reasoning” technique boosts LLM accuracy for code tasks (review, bug detection, patching) by having the AI reason through code instead of running it. This involves stating assumptions, tracing steps, and drawing conclusions – a structured process that improves results (up to 93% accuracy) and lowers computing costs.
This article provides a hands-on coding guide to explore nanobot, a lightweight personal AI agent framework. It details recreating core subsystems like the agent loop, tool execution, memory persistence, skills loading, session management, subagent spawning, and cron scheduling. The tutorial uses OpenAI’s gpt-4o-mini and demonstrates building a multi-step research pipeline capable of file operations, long-term memory storage, and concurrent background tasks. The goal is to understand not just how to *use* nanobot, but how to *extend* it with custom tools and architectures.
Starlette 1.0 has been released, and Simon Willison explores its new features by leveraging Claude’s skill‑building capabilities. He demonstrates how Claude can clone the Starlette repository, generate a comprehensive skill document with code examples, and even create a fully functional task‑management app complete with database, API endpoints, and Jinja2 templates—all generated and tested by Claude itself. The article highlights the practical benefits of integrating an LLM as a coding agent, showcases the new lifespan mechanism, and reflects on the growing popularity of Starlette as the foundation of FastAPI.
Developers are replacing bloated MCP servers with Markdown skill files — cutting token costs by 100x. This article explores a two-layer architecture emerging in production AI systems, separating knowledge from execution. It details how skills (Markdown files) encode stable knowledge, while MCP servers handle runtime API interactions. The piece advocates for a layered approach to optimize context window usage, reduce costs, and improve agent reasoning by prioritizing knowledge representation in a version-controlled, accessible format.
Agent Skills are a simple, open format for giving agents new capabilities and expertise. They are folders of instructions, scripts, and resources that agents can discover and use to do things more accurately and efficiently.
