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.
npxskills add sickn33/antigravity-awesome-skills--skill ai-agents-architectLoading…
Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration.
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
When to use: Simple tool use with clear action-observation flow
Plan first, then execute steps
When to use: Complex tasks requiring multi-step planning
Dynamic tool discovery and management
When to use: Many tools or tools that change at runtime
Multi-level memory for different purposes
When to use: Long-running agents needing context
Supervisor agent orchestrates specialist agents
When to use: Complex tasks requiring multiple skills
Save state for resumption after failures
When to use: Long-running tasks that may fail
Severity: CRITICAL
Situation: Agent runs until 'done' without max iterations
Symptoms:
Why this breaks: Agents can get stuck in loops, repeating the same actions, or spiral into endless tool calls. Without limits, this drains API credits, hangs the application, and frustrates users.
Recommended fix:
Always set limits:
Severity: HIGH
Situation: Tool descriptions don't explain when/how to use
Symptoms:
Why this breaks: Agents choose tools based on descriptions. Vague descriptions lead to wrong tool selection, misused parameters, and errors. The agent literally can't know what it doesn't see in the description.
Recommended fix:
Write complete tool specs:
Severity: HIGH
Situation: Catching tool exceptions silently
Symptoms:
Why this breaks: When tool errors are swallowed, the agent continues with bad or missing data, compounding errors. The agent can't recover from what it can't see. Silent failures become loud failures later.
Recommended fix:
Explicit error handling:
Severity: MEDIUM
Situation: Appending all observations to memory without filtering
Symptoms:
Why this breaks: Memory fills with irrelevant details, old information, and noise. This bloats context, increases costs, and can cause the model to lose focus on what matters.
Recommended fix:
Selective memory:
Severity: MEDIUM
Situation: Giving agent 20+ tools for flexibility
Symptoms:
Why this breaks: More tools means more confusion. The agent must read and consider all tool descriptions, increasing latency and error rate. Long tool lists get cut off or poorly understood.
Recommended fix:
Curate tools per task:
Severity: MEDIUM
Situation: Starting with multi-agent architecture for simple tasks
Symptoms:
Why this breaks: Multi-agent adds coordination overhead, communication failures, debugging complexity, and cost. Each agent handoff is a potential failure point. Start simple, add agents only when proven necessary.
Recommended fix:
Justify multi-agent:
Severity: MEDIUM
Situation: Running agents without logging thoughts/actions
Symptoms:
Why this breaks: When agents fail, you need to see what they were thinking, which tools they tried, and where they went wrong. Without observability, debugging is guesswork.
Recommended fix:
Implement tracing:
Severity: MEDIUM
Situation: Regex or exact string matching on LLM output
Symptoms:
Why this breaks: LLMs don't produce perfectly consistent output. Minor format variations break brittle parsers. This causes agent crashes or incorrect behavior from parsing errors.
Recommended fix:
Robust output handling:
Works well with: rag-engineer, prompt-engineer, backend, mcp-builder
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).