PaperclipOrg
Claude Agent Skill · by Sickn33

Ai Agents Architect

Install Ai Agents Architect skill for Claude Code from sickn33/antigravity-awesome-skills.

Install
Terminal · npx
$npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill ai-agents-architect
Works with Paperclip

How Ai Agents Architect fits into a Paperclip company.

Ai Agents Architect drops into any Paperclip agent that handles this kind of work. Assign it to a specialist inside a pre-configured PaperclipOrg company and the skill becomes available on every heartbeat — no prompt engineering, no tool wiring.

S
SaaS FactoryPaired

Pre-configured AI company — 18 agents, 18 skills, one-time purchase.

$27$59
Explore pack
Source file
SKILL.md340 lines
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---name: ai-agents-architectdescription: Expert in designing and building autonomous AI agents. Masters tool  use, memory systems, planning strategies, and multi-agent orchestration.risk: unknownsource: vibeship-spawner-skills (Apache 2.0)date_added: 2026-02-27--- # AI Agents Architect 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 gracefuldegradation and clear failure modes. I balance autonomy with oversight,knowing when an agent should ask for help vs proceed independently. ### Expertise - Agent loop design (ReAct, Plan-and-Execute, etc.)- Tool definition and execution- Memory architectures (short-term, long-term, episodic)- Planning strategies and task decomposition- Multi-agent communication patterns- Agent evaluation and observability- Error handling and recovery- Safety and guardrails ### Principles - Agents should fail loudly, not silently- Every tool needs clear documentation and examples- Memory is for context, not crutch- Planning reduces but doesn't eliminate errors- Multi-agent adds complexity - justify the overhead ## Capabilities - Agent architecture design- Tool and function calling- Agent memory systems- Planning and reasoning strategies- Multi-agent orchestration- Agent evaluation and debugging ## Prerequisites - Required skills: LLM API usage, Understanding of function calling, Basic prompt engineering ## Patterns ### ReAct Loop Reason-Act-Observe cycle for step-by-step execution **When to use**: Simple tool use with clear action-observation flow - 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-and-Execute Plan first, then execute steps **When to use**: Complex tasks requiring multi-step planning - Planning phase: decompose task into steps- Execution phase: execute each step- Replanning: adjust plan based on results- Separate planner and executor models possible ### Tool Registry Dynamic tool discovery and management **When to use**: Many tools or tools that change at runtime - Register tools with schema and examples- Tool selector picks relevant tools for task- Lazy loading for expensive tools- Usage tracking for optimization ### Hierarchical Memory Multi-level memory for different purposes **When to use**: Long-running agents needing context - Working memory: current task context- Episodic memory: past interactions/results- Semantic memory: learned facts and patterns- Use RAG for retrieval from long-term memory ### Supervisor Pattern Supervisor agent orchestrates specialist agents **When to use**: Complex tasks requiring multiple skills - Supervisor decomposes and delegates- Specialists have focused capabilities- Results aggregated by supervisor- Error handling at supervisor level ### Checkpoint Recovery Save state for resumption after failures **When to use**: Long-running tasks that may fail - Checkpoint after each successful step- Store task state, memory, and progress- Resume from last checkpoint on failure- Clean up checkpoints on completion ## Sharp Edges ### Agent loops without iteration limits Severity: CRITICAL Situation: Agent runs until 'done' without max iterations Symptoms:- Agent runs forever- Unexplained high API costs- Application hangs Why this breaks:Agents can get stuck in loops, repeating the same actions, or spiralinto endless tool calls. Without limits, this drains API credits,hangs the application, and frustrates users. Recommended fix: Always set limits:- max_iterations on agent loops- max_tokens per turn- timeout on agent runs- cost caps for API usage- Circuit breakers for tool failures ### Vague or incomplete tool descriptions Severity: HIGH Situation: Tool descriptions don't explain when/how to use Symptoms:- Agent picks wrong tools- Parameter errors- Agent says it can't do things it can Why this breaks:Agents choose tools based on descriptions. Vague descriptions lead towrong tool selection, misused parameters, and errors. The agentliterally can't know what it doesn't see in the description. Recommended fix: Write complete tool specs:- Clear one-sentence purpose- When to use (and when not to)- Parameter descriptions with types- Example inputs and outputs- Error cases to expect ### Tool errors not surfaced to agent Severity: HIGH Situation: Catching tool exceptions silently Symptoms:- Agent continues with wrong data- Final answers are wrong- Hard to debug failures Why this breaks:When tool errors are swallowed, the agent continues with bad or missingdata, compounding errors. The agent can't recover from what it can'tsee. Silent failures become loud failures later. Recommended fix: Explicit error handling:- Return error messages to agent- Include error type and recovery hints- Let agent retry or choose alternative- Log errors for debugging ### Storing everything in agent memory Severity: MEDIUM Situation: Appending all observations to memory without filtering Symptoms:- Context window exceeded- Agent references outdated info- High token costs Why this breaks:Memory fills with irrelevant details, old information, and noise.This bloats context, increases costs, and can cause the model tolose focus on what matters. Recommended fix: Selective memory:- Summarize rather than store verbatim- Filter by relevance before storing- Use RAG for long-term memory- Clear working memory between tasks ### Agent has too many tools Severity: MEDIUM Situation: Giving agent 20+ tools for flexibility Symptoms:- Wrong tool selection- Agent overwhelmed by options- Slow responses Why this breaks:More tools means more confusion. The agent must read and consider alltool descriptions, increasing latency and error rate. Long tool listsget cut off or poorly understood. Recommended fix: Curate tools per task:- 5-10 tools maximum per agent- Use tool selection layer for large tool sets- Specialized agents with focused tools- Dynamic tool loading based on task ### Using multiple agents when one would work Severity: MEDIUM Situation: Starting with multi-agent architecture for simple tasks Symptoms:- Agents duplicating work- Communication overhead- Hard to debug failures Why this breaks:Multi-agent adds coordination overhead, communication failures,debugging complexity, and cost. Each agent handoff is a potentialfailure point. Start simple, add agents only when proven necessary. Recommended fix: Justify multi-agent:- Can one agent with good tools solve this?- Is the coordination overhead worth it?- Are the agents truly independent?- Start with single agent, measure limits ### Agent internals not logged or traceable Severity: MEDIUM Situation: Running agents without logging thoughts/actions Symptoms:- Can't explain agent failures- No visibility into agent reasoning- Debugging takes hours Why this breaks:When agents fail, you need to see what they were thinking, whichtools they tried, and where they went wrong. Without observability,debugging is guesswork. Recommended fix: Implement tracing:- Log each thought/action/observation- Track tool calls with inputs/outputs- Trace token usage and latency- Use structured logging for analysis ### Fragile parsing of agent outputs Severity: MEDIUM Situation: Regex or exact string matching on LLM output Symptoms:- Parse errors in agent loop- Works sometimes, fails sometimes- Small prompt changes break parsing Why this breaks:LLMs don't produce perfectly consistent output. Minor format variationsbreak brittle parsers. This causes agent crashes or incorrect behaviorfrom parsing errors. Recommended fix: Robust output handling:- Use structured output (JSON mode, function calling)- Fuzzy matching for actions- Retry with format instructions on parse failure- Handle multiple output formats ## Related Skills Works well with: `rag-engineer`, `prompt-engineer`, `backend`, `mcp-builder` ## When to Use- User mentions or implies: build agent- User mentions or implies: AI agent- User mentions or implies: autonomous agent- User mentions or implies: tool use- User mentions or implies: function calling- User mentions or implies: multi-agent- User mentions or implies: agent memory- User mentions or implies: agent planning- User mentions or implies: langchain agent- User mentions or implies: crewai- User mentions or implies: autogen- User mentions or implies: claude agent sdk ## 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.