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Found 18 Skills
AI Agent Harness Design Patterns - Memory, Permission, Context Engineering, Delegation, Skill, Hook, Bootstrap. Chinese Version.
Agent harness architecture — structure a project's agent context across layers for effective AI-assisted development. Covers CLAUDE.md, skills, design docs, hooks, and all artifacts that shape how an agent understands and operates in a codebase. Use when setting up or improving a project's agent configuration, when agent context feels bloated or disorganized, when onboarding a new project for AI-assisted development, or when the agent keeps losing architectural awareness mid-task. Trigger on phrases like "set up claude", "improve CLAUDE.md", "agent keeps forgetting", "context is too long", "harness setup", "organize agent context", "how should I structure my prompts". Supports arguments: `/harness audit` to evaluate an existing project's context architecture, `/harness init` to set up harness from scratch.
Install and configure LLMem for an agent harness. Handles CLI install, plugin deployment, skill registration, and provider setup. Triggers on: "install llmem", "set up memory", "configure memory", "add llmem to harness", "memory setup".
Design multi-agent harnesses for long-running autonomous coding tasks. Covers generator/evaluator loops, context reset strategy, sprint contracts, and the planner-generator-evaluator architecture from Anthropic's harness research.
Design and build multi-agent harness architectures for long-running AI application development. GAN-inspired Generator-Evaluator pattern, Sprint Contract negotiation, context management, quality criteria calibration. Based on Anthropic Engineering patterns. Use when: "build a harness", "multi-agent architecture", "agent orchestration", "generator-evaluator", "long-running app", "harness design", "agent pipeline", "quality evaluation loop", "sprint contract", "build app with agents", "Claude Agent SDK architecture", or when building complex full-stack apps that need planning → generation → evaluation cycles. Also use when discussing context degradation, self-evaluation bias, or assumption testing in AI workflows.
Side-by-side comparison of ruflo vs HAL vs other GAIA harnesses — capability gaps, design decisions, and improvement roadmap