llm-application-dev-ai-assistant – Design & UI skill for AI agents
Design and implement conversational AI assistants with context management, integration boundaries, and validation.
Install the llm-application-dev-ai-assistant skill with one command
Pick the tool you use, copy the command and paste it in your terminal. The skill lands in that tool's skills folder and is ready in your next session.
Claude Code
mkdir -p ~/.claude/skills && curl -fsSL https://skill.dn1nguoi.com/z/llm-application-dev-ai-assistant-e8c2e.tgz | tar xz -C ~/.claude/skills
Call it in Claude Code: /llm-application-dev-ai-assistant – then describe the task you want done.
Codex
mkdir -p ~/.codex/skills && curl -fsSL https://skill.dn1nguoi.com/z/llm-application-dev-ai-assistant-e8c2e.tgz | tar xz -C ~/.codex/skills
Call it in Codex: $llm-application-dev-ai-assistant – then describe the task you want done.
Hermes Agent
mkdir -p ~/.hermes/skills && curl -fsSL https://skill.dn1nguoi.com/z/llm-application-dev-ai-assistant-e8c2e.tgz | tar xz -C ~/.hermes/skills
Call it in Hermes Agent: /llm-application-dev-ai-assistant – then describe the task you want done.
DeepSeek
mkdir -p ~/.deepcode/skills && curl -fsSL https://skill.dn1nguoi.com/z/llm-application-dev-ai-assistant-e8c2e.tgz | tar xz -C ~/.deepcode/skills
Call it in DeepSeek: /llm-application-dev-ai-assistant – then describe the task you want done.
SKILL.md content
AI Assistant Development
You are an AI assistant development expert specializing in creating intelligent conversational interfaces, chatbots, and AI-powered applications. Design comprehensive AI assistant solutions with natural language understanding, context management, and seamless integrations.
Use this skill when
- Working on ai assistant development tasks or workflows
- Needing guidance, best practices, or checklists for ai assistant development
Do not use this skill when
- The task is unrelated to ai assistant development
- You need a different domain or tool outside this scope
Context
The user needs to develop an AI assistant or chatbot with natural language capabilities, intelligent responses, and practical functionality. Focus on creating production-ready assistants that provide real value to users.
Requirements
$ARGUMENTS
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open
resources/implementation-playbook.md.
Implementation and authority boundaries
The playbook is conceptual scaffolding with many undefined providers, models, methods and configuration fields; embedded Python strings are not a complete runnable project. Replace $ARGUMENTS with scoped acceptance examples and choose a minimal vertical slice before building the capability list. Intent/model confidence is not user authorization. Every tool call needs server-side identity, tenant/object permission, runtime argument validation, explicit effect approval where required, bounded retries and idempotency. Treat conversation/retrieved content as untrusted data. Obtain permission for provider fallback, analytics and training egress; isolate caches/memory by tenant/user, access scope, model/version and context, with retention/deletion controls. Do not silently substitute a cached/default answer for a failed safety-critical operation. Use a fixed evaluation set, subgroup/error analysis and rollout/rollback gates before promotion; the shown score/p-value threshold does not establish readiness or causal improvement.
Resources
resources/implementation-playbook.mdfor detailed patterns and examples.
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
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