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Context enrichment for agentic AI application development using LangChain, Vercel AI SDK, and assistant-ui. Use when building AI agents, chat interfaces, tool-calling pipelines, RAG systems, or multi-step AI workflows.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Lightweight implementation orchestrator for low-complexity work — fixes, refactors, doc changes, or single-AC features that do not warrant a phase plan or factory decomposition.
Linear phase-loop orchestrator for single-feature implementation plans. Use for medium-complexity work where transparent human-in-the-loop phase review is preferred over factory automation.
Implementation entry point. Use to execute a completed specification. Auto-detects the decomposition tier (Direct, Incremental, or Factory) from spec artifacts and dispatches to the matching execution sub-skill.
Decompose a single-feature specification into a linear, phase-by-phase implementation plan. Use this for medium-complexity work — single feature, one or two components — where transparent human-in-the-loop phase review is preferred over factory automation.
Scaffold, status-check, and manage specification directories. Use when creating a new spec, reading spec status, transitioning between phases, or logging decisions on a spec in .start/specs/.
Create a comprehensive specification from a brief description. Runs requirements gathering, solution design, and decomposition — routing decomposition to one of three tiers based on a complexity classifier: Direct (no plan), Incremental (linear phase plan), or Factory (parallel units with holdout scenarios).
| name | agentic-patterns |
| description | Context enrichment for agentic AI application development using LangChain, Vercel AI SDK, and assistant-ui. Use when building AI agents, chat interfaces, tool-calling pipelines, RAG systems, or multi-step AI workflows. |
Act as an agentic AI development specialist who enriches implementation context with current framework documentation and proven integration patterns.
Development Target: $ARGUMENTS
AgenticContext { frameworks: string[] pattern: AGENT | CHAT_UI | RAG | TOOL_CALLING | MULTI_STEP | EVALUATION }
State { target = $ARGUMENTS detectedFrameworks = [] }
Always:
Never:
Identify which frameworks are relevant from the development target. Fetch the corresponding reference documentation.
Combine fetched documentation into actionable guidance:
Provide framework-specific guidance integrated with the development target.
