Intent Layer: A context engineering skill for AI agents
A skill that sets up hierarchical AGENTS.md files so AI agents navigate your codebase like senior engineers.
Intent Layer: A context engineering skill for AI agents launched on January 19, 2026 with 29 votes, #644 of 14,584 launches that month and more than 95% of that year's launches.
In plain words
Intent Layer is a context engineering skill designed for AI agents that helps them better understand and respond to user requests. Developed by Railly, it functions as a specialized tool that improves how AI systems interpret and process information. The product is intended for developers and teams building AI agent systems who need more sophisticated context handling capabilities. Intent Layer focuses on bridging the gap between raw user input and meaningful agent responses through improved contextual awareness.
written from the facts on this page · September 2026
From the sources
A skill that sets up hierarchical AGENTS.md files so AI agents navigate your codebase like senior engineers. Today I’m releasing /intent-layer , the first skill from Crafter Station . npx skills add crafter-station/skills --skill intent-layer -g Works with Claude Code, Codex, Cursor, Copilot, and 10+ more agents . I’ve been using Claude Code daily for months. Same model, same prompts, completely different results depending on the repo. Your best engineers don’t grep randomly. They have a mental map: Intent Layer solves the first piece: system prompt infrastructure . The skill helps you set up AGENTS.md files at folder boundaries. Simple markdown that gives agents the context they can’t get…from railly.dev
Does a similar job
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AI Context FlowNov 2025 · chromewebstore.google.com · ▲431Reusable AI Memory for Smarter Prompts Anywhere
Context OverflowMar 2026 · ctxoverflow.dev · ▲109Hey HN, After using a bunch of agents, my friend and I noticed a major issue: an agent could spend a bunch of time solving a tricky task, but when the session ended all that knowledge vanishes. Even if I told my agent to remember it locally, there was no way for agents to learn from each other. We built Context Overflow as a way to turn isolated AI sessions into shared, reusable context. With Context Overflow, agents can: - Search through past solutions when starting a task - Ask questions when they get stuck - Share findings when they solve a non-trivial problem We support a variety of ways…
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