Every documentation team eventually faces the same task: renaming things. A product name changes. A feature is repositioned. Two concepts start colliding in the same ecosystem. So the docs get updated. Pages are rewritten, references are changed, navigation is adjusted, sometimes URLs too, and the old terminology slowly disappears from the documentation corpus. Technical writers are used to this. (We may not love it, but it’s part of the job 🫣.) Until recently, the goal of these renames was mostly about human clarity. If a term confused users, you changed it. If two concepts sounded too similar, you separated them. But I’ve been wondering how this practice changes as documentation starts becoming part of AI-driven developer experiences. AI assistants increasingly learn about products by reading the same documentation that humans do. The documentation corpus effectively becomes part of the product’s knowledge base. And AI systems tend to reason about concepts through semantic proximity. So a rename that solves a human UX problem may not fully resolve the underlying conceptual relationship between those terms. To a human reader, the distinction might be obvious. To an AI system interpreting the documentation corpus, the concepts might still appear closely related. This raises the following question: When documentation becomes part of the knowledge source for AI assistants, are we just renaming things for humans? Or, are we also redefining the conceptual structure that machines use to understand the product? In other words, documentation may be evolving into something slightly different. Not just the place where users learn how a product works. But the place where the meaning structure of the product is defined for both humans and machines. And if that’s true, then decisions about terminology, concept boundaries, and feature descriptions may start carrying a different kind of weight. The rename is no longer just a documentation task. It’s a conceptual design decision — one that shapes how both humans and machines understand what the product is and how its parts relate to each other. Documentation teams may need to start thinking less like editors and more like ontologists.
Rethinking Renames: Documentation's New Role in AI-Driven Products
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One of the most important ideas in agent tooling right now is the idea of skills. At a high level, skills are reusable packages of instructions, workflows, scripts, and references that an agent can load for specialized tasks instead of stuffing everything into one giant prompt. The open Agent Skills model is built around progressive disclosure: load lightweight metadata first, and only pull the full skill into context when it is relevant. We’ve now added support for this style of skills in NonBioS. What makes this especially interesting is the context-engineering philosophy behind NonBioS: Strategic Forgetting. Most AI systems try to preserve more and more context. NonBioS takes a different path. Strategic Forgetting continuously prunes memory based on relevance, temporal decay, retrievability, and source priority, so the agent keeps a lean working memory focused on what matters right now. That changes how skills should work. Because NonBioS operates in a constrained working-memory environment, our skills are intentionally short, sharp, and high-signal. They are not meant to be bloated playbooks. They are meant to capture only the instructions that truly matter at runtime. So while NonBioS is publishing its own skills, this is not a closed ecosystem. Our public skills repo follows the Agent Skills specification, and the repo explicitly notes compatibility with NonBioS, Claude AI, Cursor, and other compatible tools. In other words: NonBioS supports the broader skills standard, not just NonBioS-native skills. There’s also an important product detail about how this works today. Right now, in NonBioS, skills are learned explicitly. So to activate a skill, you will have to prompt NonBioS explicitly: “Learn skill from [SKILL.md URL]” For example, you can point NonBioS at a skill such as: github[DotCom]/nonbios-1/skills/tree/main/skills/seo-content-writing That is different from Claude’s default behavior, where skills are automatically used when relevant, though they can also be invoked directly. But even this explicit model is already powerful. If you are building UX, you can search for the latest UX skill and ask NonBioS to learn it. That can immediately improve the UX NonBioS produces. So even before seamless automatic discovery arrives, skills already act like modular capability upgrades for the agent. This is the direction we’re excited about: - not bigger prompts, - but reusable skills; - not endless memory, - but better context discipline. Claude helped make skills visible. NonBioS is exploring what skills look like inside a Strategic Forgetting system. And in that world, the best skill may not be the longest one. It may be the one that is shortest, clearest, and most deliberate. Repo: https://lnkd.in/gxXSXcGV
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AI agents are evolving beyond prompts into reusable intelligent skills. At www.jaiinfoway.com we focus on building structured capabilities that enable agents to perform tasks consistently across workflows and systems. From progressive disclosure to composable design and portable execution the real advantage lies in creating skills that scale across use cases. This shift transforms AI from reactive responses to proactive execution of complex workflows. At www.jaiinfoway.com we help organizations design skill driven AI systems that improve efficiency reduce repetition and deliver reliable outcomes across business operations. #Jaiinfoway #AIAgents #AISkills #AgenticAI #ArtificialIntelligence #AIEngineering #FutureOfAI #Innovation
Scaling with AI Agents | Expert in Agentic AI & Cloud Native Solutions| Builder | Author of Agentic AI: Reinventing Business & Work with AI Agents | Driving Innovation, Leadership, and Growth | Let’s Make It Happen! 🤝
Skills have replaced prompt engineering for AI Agents Want to build skills? This is the only guide you'll need... As I shared, Skills are the new prompt engineering for AI Agents. If you're building AI agents or using MCP integrations, understanding Skills is your next competitive advantage. And the best way to learn it is from the creators of the skills themselves. So, according to this Anthropic report, Let me break down the complete framework for building Skills for Claude: 📌 What are Skills? - A folder containing YAML frontmatter and Markdown instructions - Teaches Claude specific tasks or workflows once - Works across Claude.ai, Claude Code, and API - Uses progressive disclosure to minimize token usage while maintaining expertise 📌 Three Core Design Principles: 1" Progressive Disclosure - Level 1: YAML frontmatter (always loaded) - Level 2: SKILL.md body (loaded when relevant) - Level 3: Linked files (loaded as needed) 2" Composability - Multiple skills work simultaneously - Your skill should complement others 3" Portability - Create once, works everywhere - Same skill across all Claude surfaces 📌 Three Common Skill Categories: 1" Document & Asset Creation - Consistent output for docs, presentations, apps - Embedded style guides and templates - Example: frontend-design skill 2" Workflow Automation - Multi-step processes with consistent methodology - Step-by-step workflows with validation - Example: skill-creator skill 3" MCP Enhancement - Workflow guidance on top of MCP tool access - Coordinates multiple MCP calls in sequence - Example: sentry-code-review skill 📌 Critical Technical Requirements: File Structure: - Folder name: kebab-case only (my-skill-name) - File name: SKILL.md (exact spelling, case-sensitive) - No README.md inside skill folder YAML Frontmatter Must Include: - name: in upper-case - description: What it does + when to use it + trigger phrases 📌 Writing Effective Descriptions: ✅ Good: "Analyzes Figma design files and generates developer handoff documentation. Use when user uploads .fig files, asks for 'design specs' or 'design-to-code handoff'." ❌ Bad: "Helps with projects" (too vague, missing triggers) 📌 Five Popular Design Patterns: 1" Sequential Workflow Orchestration - Multi-step processes in a specific order 2" Multi-MCP Coordination - Workflows spanning multiple services 3" Iterative Refinement - Output quality improves with iteration 4" Context-Aware Tool Selection - Same outcome, different tools based on context 5" Domain-Specific Intelligence - Specialized knowledge beyond tool access If you want to understand AI agent concepts deeper, my free newsletter breaks down everything you need to know: https://lnkd.in/g5-QgaX4 Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents
There's an old adage in software engineering: There are only two hard things in Computer Science: cache invalidation and naming things.