andrewyng/context-hub
Agent skills in the GitHub repository andrewyng/context-hub, each with its code score out of 100.
- Repository score
- 59 / 100 C
- Skills
- 10
- Verified
- 0
- Average skill score
- 62
- GitHub stars
- 13,985
- License
- MIT
Open andrewyng/context-hub on GitHub
10 skills in this repository
-
document-workflows
Use this skill for building end-to-end document processing workflows and pipelines using LandingAI ADE. Trigger when users need to: (1) Process batches of documents in parallel or…
75B -
get-api-docs
Use this skill to get documentation for third-party APIs, SDKs or libraries before writing code that uses them to ensure you have the latest, most accurate documentation. This is…
73B -
riverpod
Use when working with Flutter Riverpod state management. Covers providers, consumers, refs, containers, overrides, async state, code generation, testing, and safe defaults.
67C -
bloc-cubit
Use when working with Flutter Bloc/Cubit state management. Covers when to choose Bloc vs Cubit, how to use bloc and flutter_bloc together, lifecycle, testing, and safe defaults.
66C -
new-project
Build a new AI agent with Olakai monitoring from scratch — project setup, SDK integration, KPI configuration, and end-to-end validation
63C -
integrate
Add Olakai monitoring to existing AI code — wrap your LLM client, configure custom KPIs, and validate the integration end-to-end
61C -
document-extraction
Use this skill for intelligent document processing and content extraction using LandingAI's Agentic Document Extraction (ADE). Trigger when users need to (1) Parse documents (PDFs…
58C -
electronics-sourcing
Guide for AI agents to source electronic components using parts-mcp — tool sequencing, decision patterns, and multi-step workflows
58C -
tavily-best-practices
Build production-ready Tavily integrations with best practices for web search, content extraction, crawling, and research in agentic workflows, RAG systems, and autonomous agents
52D -
login-flows
Common login automation patterns for web apps using Playwright
47D
Scores come from a snapshot of 300 public GitHub repositories taken on 12 November 2026. Every score is the result of fixed checks run by code; no AI is involved. How scoring works.