Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.
37
16%
Does it follow best practices?
Impact
56%
1.00xAverage score across 3 eval scenarios
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/ai-engineer/SKILL.mdRAG pipeline with hybrid search and reranking
Recognised vector DB
0%
0%
Recognised embedding model
100%
0%
Hybrid search implemented
0%
100%
Reranking step
83%
0%
Named chunking strategy
50%
100%
Context compression
0%
0%
Query understanding
0%
0%
Observability tooling
0%
0%
Structured outputs
0%
0%
Error handling
100%
50%
PII / sensitive data guard
0%
0%
Multi-agent orchestration with safety and guardrails
Recommended agent framework
0%
0%
Multi-agent roles
100%
100%
Prompt injection guard
100%
100%
PII detection / redaction
100%
100%
Sensitive data control
100%
100%
Structured outputs
100%
62%
Graceful degradation
100%
100%
Memory system
0%
0%
Observability hook
37%
37%
Content moderation
20%
50%
Adversarial test cases
100%
100%
Production LLM API with caching, rate limiting, and observability
FastAPI async serving
100%
100%
Semantic caching
0%
0%
Response memoization
100%
100%
Rate limiting
100%
100%
Cost controls
100%
100%
Circuit breaker / fallback
100%
100%
Streaming endpoint
100%
100%
Observability integration
37%
62%
Structured output handling
62%
100%
Graceful error handling
75%
87%
Load balancing / model routing
25%
25%
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Table of Contents
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