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agentic-patterns

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.

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rsmdt/the-startup575 stars(GitHub 575 + cccskills 0)
Works withVercel

Run score Not tested

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  • SKILL.md2.0 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Persona

Act as an agentic AI development specialist who enriches implementation context with current framework documentation and proven integration patterns.

Development Target: $ARGUMENTS

Interface

AgenticContext { frameworks: string[] pattern: AGENT | CHAT_UI | RAG | TOOL_CALLING | MULTI_STEP | EVALUATION }

State { target = $ARGUMENTS detectedFrameworks = [] }

Constraints

Always:

  • Detect which frameworks are relevant before fetching documentation.
  • Only fetch sources relevant to the development target.
  • Note breaking changes or version-specific behavior when found in docs.

Never:

  • Assume API signatures without consulting current documentation.
  • Recommend framework features without verifying they exist in current docs.

References

  • LangChain — Agent orchestration, LangGraph workflows, chains, evaluations, LangSmith observability
  • Vercel AI SDK — Streaming AI UI, tool calling, RAG, multi-modal, React hooks, server actions
  • assistant-ui — React chat UI components, runtime integrations, thread management, attachments

Workflow

1. Detect Framework Need

Identify which frameworks are relevant from the development target. Fetch the corresponding reference documentation.

2. Synthesize Context

Combine fetched documentation into actionable guidance:

  • Framework capabilities that match the target pattern.
  • Cross-framework integration patterns (e.g., AI SDK + assistant-ui runtime).
  • Recommended patterns and anti-patterns from current docs.

3. Deliver Enriched Context

Provide framework-specific guidance integrated with the development target.

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