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134 skills found
Applies cognitive science frameworks for creative thinking to generate genuinely novel research directions in computer science and AI.
Architect and optimize production-grade RAG systems. Master embedding models, vector databases, chunking strategies, and retrieval pipelines for high-accuracy LLM applications.
Implement production-grade AI agents with LangGraph, tool-calling guardrails, SSE streaming, and episodic memory. Includes anti-patterns, fix pairs, and stateful architecture patterns.
Build systematic evaluation frameworks for AI agents using multi-dimensional rubrics, LLM-as-a-judge, and regression testing to measure performance, quality, and context engineering effectiveness.
Foundational guidelines for context engineering: optimizing token budgets, attention mechanics, and system architecture for AI agents.
Systematic cloud cost optimization for AWS, Azure, GCP, and OCI through resource rightsizing, automated governance, pricing model analysis, and architectural best practices.
SPARC methodology for multi-agent development: systematic Specification, Pseudocode, Architecture, Refinement, and Completion workflows via Claude Flow orchestration.
Database schema validation, data integrity testing, migration validation, transaction isolation, and query performance testing. Ensure ACID compliance and referential integrity for data-driven applications.
Manage Vibesafe units to scan, generate, test, and verify AI-written code with cryptographically-secure hash-locked checkpoints.
Build no-code MCP servers that orchestrate tools as directed graphs using YAML for data transformation, conditional routing, and automated workflows.
A reinforcement learning-inspired tracker for YouTube performance, using systematic logging to optimize thumbnails, titles, and hooks.
Guides agent memory system implementation, compares frameworks (Mem0, Zep, Letta, LangMem, Cognee), and designs persistence architectures for cross-session knowledge retention.