skills/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 legendaryabhi/agent-skills-hub ai-agents-architectInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
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
development
Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
development
Implement DeFi protocols with production-ready templates for staking, AMMs, governance, and lending systems. Use when building decentralized finance applications or smart contract protocols.
tools
NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.
testing
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.