cli-tool/components/skills/ai-research/ai-agents-architect/SKILL.md
Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build agent, AI agent, autonomous agent, tool use, function calling.
npx skillsauth add davila7/claude-code-templates ai-agents-architectInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Role: AI Agent Systems Architect
I build AI systems that can act autonomously while remaining controllable. I understand that agents fail in unexpected ways - I design for graceful degradation and clear failure modes. I balance autonomy with oversight, knowing when an agent should ask for help vs proceed independently.
Reason-Act-Observe cycle for step-by-step execution
- Thought: reason about what to do next
- Action: select and invoke a tool
- Observation: process tool result
- Repeat until task complete or stuck
- Include max iteration limits
Plan first, then execute steps
- Planning phase: decompose task into steps
- Execution phase: execute each step
- Replanning: adjust plan based on results
- Separate planner and executor models possible
Dynamic tool discovery and management
- Register tools with schema and examples
- Tool selector picks relevant tools for task
- Lazy loading for expensive tools
- Usage tracking for optimization
| Issue | Severity | Solution | |-------|----------|----------| | Agent loops without iteration limits | critical | Always set limits: | | Vague or incomplete tool descriptions | high | Write complete tool specs: | | Tool errors not surfaced to agent | high | Explicit error handling: | | Storing everything in agent memory | medium | Selective memory: | | Agent has too many tools | medium | Curate tools per task: | | Using multiple agents when one would work | medium | Justify multi-agent: | | Agent internals not logged or traceable | medium | Implement tracing: | | Fragile parsing of agent outputs | medium | Robust output handling: |
Works well with: rag-engineer, prompt-engineer, backend, mcp-builder
tools
No-code automation democratizes workflow building. Zapier and Make (formerly Integromat) let non-developers automate business processes without writing code. But no-code doesn't mean no-complexity - these platforms have their own patterns, pitfalls, and breaking points. This skill covers when to use which platform, how to build reliable automations, and when to graduate to code-based solutions. Key insight: Zapier optimizes for simplicity and integrations (7000+ apps), Make optimizes for power
tools
Use only when the user explicitly asks to stage, commit, push, and open a GitHub pull request in one flow using the GitHub CLI (`gh`).
tools
Workflow automation is the infrastructure that makes AI agents reliable. Without durable execution, a network hiccup during a 10-step payment flow means lost money and angry customers. With it, workflows resume exactly where they left off. This skill covers the platforms (n8n, Temporal, Inngest) and patterns (sequential, parallel, orchestrator-worker) that turn brittle scripts into production-grade automation. Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility
development
Trigger.dev expert for background jobs, AI workflows, and reliable async execution with excellent developer experience and TypeScript-first design. Use when: trigger.dev, trigger dev, background task, ai background job, long running task.