Agent Skills Hub

implement-feature

Guide for implementing features using architecture-first design, TDD, rich domain models, and Swift 6.2 patterns, ensuring a clean separation between Domain, Infrastructure, and App layers.

transit-least-squares

Transit Least Squares (TLS) algorithm for detecting exoplanet transits in light curve data, offering higher sensitivity than Lomb-Scargle for transit-shaped signals.

casadi-ipopt-nlp

Nonlinear optimization toolkit using CasADi and IPOPT. Ideal for building complex NLP models, defining symbolic variables, constraints, and solvers, with specialized support for power systems optimization patterns.

langchain-architecture

Architect production-grade LLM applications using LangChain 1.x and LangGraph. Implement stateful AI agents, multi-step workflows, and custom memory systems for complex conversational and automation tasks.

serp-analysis

Analyze search results (SERP) to classify user intent, identify feature opportunities, and conduct competitive intelligence for content strategy.

tzurot-council-mcp

Multi-perspective AI consultation for technical architecture, complex refactoring, and structured debugging.

tool-design

Expert framework for designing agent-facing tools, optimizing tool descriptions, enforcing contract-based APIs, and implementing architectural reduction for reliable AI agent tool selection.

bilibili-chapter-generator

Generate Bilibili-compatible video chapter lists from SRT subtitle files with strict format validation.

flaky-detect

Identify, categorize, and troubleshoot flaky tests by analyzing CI history, execution patterns, and code structure to improve test suite reliability.

github-project-management

AI-driven GitHub project management using swarm coordination, automated issue triage, project board synchronization, and intelligent task decomposition for efficient development workflows.

light-curve-preprocessing

Preprocessing and cleaning astronomical light curves using Lightkurve. Tools for outlier removal, flattening, trend detrending, and quality flag handling for time-series analysis.

backend-rag-implementation

Build RAG systems to ground LLMs in proprietary data. Includes vector database integration, embedding strategies, hybrid search, and advanced retrieval patterns for FastAPI backends.