Articles, tutorials, and updates about the AI tools ecosystem.
MCP servers look like APIs on the surface, but the interaction model is fundamentally different. Understanding the distinction helps you decide when each approach makes sense.
The MCP ecosystem changes fast. New servers launch daily, existing ones get updated, and some go dormant. Here's how Skillful.sh keeps up with all of it so you don't have to.
As the ecosystem grows past hundreds of thousands of tools, discovery and curation will evolve. The approaches that work now will need to scale, adapt, and become more intelligent.
AI skills for Claude are reusable prompt-tool combinations that extend what the assistant can do. Here is a practical walkthrough of building one from scratch.
Open source AI tooling has exploded in 2026. More servers, more skills, more frameworks, and more contributors than ever. Here's what the numbers actually look like.
Database migrations are high-stakes operations where mistakes can mean data loss. AI agents can help plan and execute migrations more safely, but they need proper guardrails.
AI agents that can read your database, call your APIs, and send messages on your behalf need carefully scoped permissions. Here's how to think about access control for agents.
Dev, staging, production, and maybe a few feature environments too. AI agents can track what's deployed where, promote builds between environments, and catch configuration drift.
Finding AI tools is one challenge. Vetting them as a team with shared criteria is another. Here's how Skillful.sh supports both halves of that process.
GitHub stars are the most visible metric for open-source projects, but they are also one of the most misunderstood. What they actually tell you about an AI tool is more nuanced than it appears.
Secrets are everywhere in deployment pipelines: API keys, database passwords, certificates. AI agents can manage them more safely than manual processes, but the setup matters.
The Model Context Protocol gives AI models a standardized way to interact with external tools. Here is a clear look at what MCP is, how it works, and why the industry converged on it.
AI coding assistants are evolving from autocomplete into autonomous coding workflows that can write, test, and iterate on code with minimal human intervention. This changes what developer time is spent on.
Security scores are only useful if you understand what they measure and how they weight different factors. A transparent look at how security scoring works and what it captures.
A task that's 80% complete when something breaks is still valuable. Agents that handle partial failures return useful results instead of nothing.
A failed task attempt isn't just a failure. It's training data. Agents that capture why things went wrong and adjust their approach get better over time.
Aggregating AI tool data from over fifty different directories involves crawling, normalization, deduplication, and enrichment. A look at how the process works and why it matters.
A practical guide for developers who want to start using MCP servers with their AI assistant. Covers setup, choosing your first servers, and common patterns.
The overwhelming majority of MCP servers are open source. This is not accidental. The protocol's design and the ecosystem's dynamics strongly favor open development models.
Going from a few hundred to over a hundred thousand AI tools in under two years is unusual even by technology standards. Several factors converged to make this growth possible.