Claude Skill Workflow and Patterns Explained | Rakesh Gohel

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Claude Skill Workflow and Patterns Explained | Rakesh Gohel Your AI agents are smart. Your Skill files aren't. The description field alone decides if they trigger… Most teams using Claude AI Skills are burning money without knowing it. Here's the mistake I see everywhere. They treat Skills like a bigger system prompt. Load everything. Hope for the best. Pay for all of it. That's backwards. 📌 Skills have a 3-tier architecture built specifically to NOT load everything: → Metadata (~100 words) - always in context → SKILL.md body -loads ONLY when triggered → Bundled assets - loads ONLY when the task needs them This is intentional design. When you fight it, you pay 60–90% more tokens per session. When you work with it, costs drop dramatically and execution speeds up. The trigger is everything. Claude matches the user's query against your skill description. Vague description = skill never fires. Precise description = Claude activates exactly the right skill at the right time. "Helps with code" → never triggers reliably "Reviews Python for security issues when user asks for code review" → triggers every time 📌 Once you nail descriptions, there are 3 design patterns worth knowing: 1. Generator: Core skill + output_style.md + guide.md. Delivers consistent quality outputs. Best for: Security reviews, code audits. 2. Inversion: Asks all clarifying questions BEFORE execution. No gaps. No assumptions. Best for: Full-stack app builds. 3. Chained Inversion: Asks questions AND acts simultaneously. Builds as it clarifies. Best for: Large codebase refactors. The workflow that makes this composable: User query → Claude → Skill triggered → Skill Manager retrieves depth → Output The Skill Manager sits between Claude and execution. Each agent carries minimum context and pulls depth only when needed. That separation is what makes multi-agent systems actually scalable. The most underrated part of any skill file? The description field. Not the rules. Not the steps

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