Agent Skills by Tianjun Zheng
Showing 27 vetted skills indexed across 1 GitHub repositories.
api-design-principles
Design REST and GraphQL APIs with schema-first patterns and governance.
rag-implementation
Build RAG systems with vector stores, embeddings, retrieval, and reranking.
context-engineering-collection
Aggregate and index Agent Skills for context engineering semantic search.
auth-implementation-patterns
Implement JWT, OAuth2, session management, and RBAC for REST or GraphQL APIs.
embedding-strategies
Compare embedding models and recommend chunk sizes for RAG pipelines.
stripe-integration
Automate Stripe payment integration for checkout, subscriptions, webhooks, and refunds in web and mobile apps.
react-state-management
Implement React state management with Redux Toolkit, Zustand, or Jotai.
senior-prompt-engineer
Optimize prompts and evaluate RAG pipelines with Python scripts.
similarity-search-patterns
Implement vector upsert and similarity search across Pinecone, Qdrant, pgvector, and Weaviate.
debugging-strategies
Guide structured debugging through reproduction, hypothesis formation, and verification.
code-review-excellence
Guide engineers through structured pull request reviews with actionable feedback.
sql-optimization-patterns
Analyze slow SQL queries with EXPLAIN ANALYZE and apply indexing optimizations.
revenuecat
Implement and manage in-app subscriptions with RevenueCat across iOS, Android, Flutter, React Native, and web.
webapp-testing
Automate local web application testing with Python Playwright scripts.
frontend-design
Create distinctive frontend interfaces with bold aesthetics across React, Vue, or HTML/CSS.
skill-template
Create standardized Agent Skills with a reusable SKILL.md template.
digital-brain
Load only task-relevant modules from a personal operating system.
multi-agent-patterns
Design multi-agent architectures that distribute workload across specialized LLMs.
context-degradation
Diagnose context degradation and generate remediation recommendations for long-running agent systems.
context-compression
Compress long-running AI agent session context with anchored iterative summarization.
memory-systems
Persist agent state across sessions using vector memories and a temporal knowledge graph.
advanced-evaluation
Evaluate LLM outputs with direct scoring, pairwise comparisons, and rubric generation.
context-fundamentals
Implement progressive disclosure and context budgeting for language-model agents in Python.
project-development
Plan, validate, and execute LLM-powered projects with a canonical pipeline.