'designing' 검색 결과

Review how an AI agent loads, retains, summarizes, and shares working information. This historical guide helps examine forgotten constraints, repeated actions, tool selection, and handoffs using context engineering practices published in 2024–2025.

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• Reviewing information loaded at startup versus on demand
• Investigating repeated actions or forgotten constraints
• Planning summaries and persistent task notes

Classify agent work as ready to continue, needing human judgment, or intentionally paused. Structure decision requests with evidence, a recommended choice, and clear outcomes, then connect the recorded decision to the next responsible agent.

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• Deciding whether agent work needs human review
• Preparing decision requests with evidence and recommended choices
• Designing a dashboard for pending human decisions

Design and maintain applications where OpenRig agents help run the system. Organize instructions, shared state, and artifact checks, then trace unexpected behavior to the code, skill, or schema that needs repair.

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• Designing an application operated by OpenRig agents
• Organizing instructions and shared application state
• Tracing unexpected behavior to its source

Plan and verify OpenRig startup, shutdown, and recovery. Compare reported status with actual running sessions, distinguish recovery outcomes, and define reboot tests that expose partial failures instead of hiding them behind a fresh launch.

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• Checking rig status after a host reboot
• Designing recovery tests across runtimes
• Investigating partially restored agent sessions

Design an OpenRig agent team around a project goal. Create team and agent specifications, role guidance, and collaboration rules, then validate the setup and diagnose problems with startup instructions or agent coordination.

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• Designing an implementation and QA pair
• Creating a team with development and independent review
• Writing role guidance and team collaboration rules

Guides the design and review of agent startup context, including role files and links to authoritative records. It helps identify stale guidance, missing updates, overloaded startup files, and instructions that lose the purpose of the work.

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• Designing startup files for a new agent
• Reviewing stale guidance used by running agents
• Separating role information from reusable procedures

Apply a specific set of Rails conventions to design, implementation, and code review. It keeps business logic in models, represents state changes as records, limits dependencies, and uses Minitest with fixtures, or predefined test data.

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• Designing Rails models and database changes
• Representing state changes with separate records
• Reviewing code for consistent Rails conventions

Turn a recurring workflow into a new agent skill through guided discussion. Define use cases, activation conditions, file structure, and instructions, then create and validate SKILL.md and supporting files, including workflows using external tools through MCP.

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• Turning recurring work into a reusable skill
• Standardizing report or document creation steps
• Designing workflows that use external services through MCP

Refactor React components that have too many true-or-false settings into reusable, flexible parts. This skill guides component composition, shared state, and API design, with examples and separate guidance for React 19 features.

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• Refactoring components with many boolean props
• Designing a reusable component library
• Organizing state shared by sibling components

Design appointment-based service websites with calm photography, spacious layouts, and clear treatment comparisons. Carry the same visual style through location, practitioner, and time selection, including booking confirmation, validation errors, retries, and preserved choices.

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• Creating or redesigning a salon or spa website
• Presenting treatments with prices and durations
• Designing booking steps for locations, practitioners, and times

Design or review Apache Hudi tables through questions about data volume, updates, and usage. Get architecture recommendations, a decision record explaining tradeoffs, and configuration artifacts for supported processing paths.

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• Designing a new Hudi table
• Reviewing an existing table design
• Planning a small prototype

Plan task handoffs between agents on different machines using Git to share YAML task and result files. This implementation specification describes validation, execution limits, result reporting, and GitHub Issues as an additional route for urgent requests.

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• Designing GPU task handoffs from a laptop to a DevBox
• Standardizing task and result files across machines
• Designing urgent requests through labeled GitHub Issues

Design amino acid sequences from a protein backbone stored in a PDB file using ProteinMPNN. Generate multiple candidates, redesign selected chains while preserving specified residues, and vary sampling settings to prepare sequences for downstream structure prediction.

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• Designing sequences for RFdiffusion backbones
• Redesigning a chain while keeping interface residues fixed
• Generating candidates across multiple sampling temperatures

Design amino acid sequences from a protein backbone using SolubleMPNN, a model trained on soluble protein structures. Generate candidates with a solubility bias for redesigning aggregation-prone proteins or preparing an E. coli expression screen.

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• Redesign protein candidates that aggregate
• Adapt membrane-adjacent folds for soluble expression
• Generate candidates for an E. coli expression screen

Defensive Golang coding against accidental bugs — nil panics, typed-nil interfaces, `append` backing-array aliasing, silent int64-to-int32 truncation, float `==` comparison, `defer` inside loops, defensive copies of slices and maps, and usable zero values. Use when a Go program panics on a nil map write or nil pointer dereference, when reviewing code for nil-safety, numeric conversion overflow, or resource lifecycle, or when designing a type whose zero value must be safe. Not for designing concurrent access with goroutines, channels, or sync primitives (→ See `samber/cc-skills-golang@golang-concurrency` skill), not for exploitable vulnerabilities such as injection, weak crypto, or leaked secrets (→ See `samber/cc-skills-golang@golang-security` skill), and not for debugging an already-failing program (→ See `samber/cc-skills-golang@golang-troubleshooting` skill).

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Apply Martin Kleppmann-inspired data-system rules when designing reliability, scalability, consistency, replication, transactions, streams, or schema evolution.

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Designs software architecture and selects appropriate patterns for projects. Use when designing systems, choosing architecture patterns, structuring projects, making technical decisions, or when asked about microservices, monoliths, or architectural approaches.

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Designs REST and GraphQL APIs including endpoints, error handling, versioning, and documentation. Use when creating new APIs, designing endpoints, reviewing API contracts, or when asked about REST, GraphQL, or API patterns.

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Builds voice and chat AI agents with LiveKit Agents on LiveKit Cloud or a self-hosted server. Use when the user asks to "build a voice agent", "create a LiveKit agent", "add voice AI to my app", "implement handoffs", "structure an agent workflow", "my agent is slow / too chatty", "it says it booked but nothing was saved", "make it confirm before committing", "it keeps re-asking things the caller already said", or is writing code against the LiveKit Agents SDK. Covers architecture: designing for latency, keeping context small, splitting a monolithic agent into handoffs and tasks, and designing for voice. Also covers keeping the model in charge of meaning while code owns state, approvals, and effects. For API specifics use reading-livekit-docs. To check behavior use debugging-livekit-agents and testing-livekit-agents.

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Prompt engineering and LLM evaluation. Use when optimizing prompts, designing prompt templates, evaluating LLM outputs, building agentic systems, implementing RAG, creating few- shot examples, or designing structured-output workflows.

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Designing for visionOS — spatial layout and ergonomics (60pt eye targets, field-of-view placement, dynamic scale), eyes-and-hands input with hover rules, motion and visual comfort (the 0.2 Hz rule, vection, depth-cue agreement), immersion strategy, spatial sound, and the video-format decision guide. Use when designing or reviewing any visionOS app, window, volume, or immersive experience.

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Expert guidance for designing, assessing, and optimizing Azure workloads using Azure Well Architected. Covers design review checklists, recommendations, design principles, tradeoffs, service guides, workload patterns, and assessment questions. Use when designing AI/HPC/SaaS workloads, AVD/AVS deployments, WAF assessments, or App Service/AKS/SQL architectures, and other Azure Well Architected related development tasks.

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Expert guidance for designing Azure solutions using Azure Architecture. Covers reference architectures, solution ideas, design patterns, technology choices, architecture styles, best practices, anti-patterns, example workloads, and migration guides. Use when designing RAG/GenAI, AKS, SAP or mainframe migrations, HA/DR topologies, or secure hybrid networks, and other Azure Architecture related development tasks.

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Advanced architecture stress analysis methodology for designing systems that survive unknown stresses. Load when --residuality flag is used or when designing high-uncertainty, mission-critical systems.

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