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explainx.ai

home/skills/tag/implementation
skill tag

implementation▌

32 indexed skills · max 10 per page

skills (32)

hybrid-search-implementation

sickn33/antigravity-awesome-skills · Productivity

0

$22

graphql-implementation

aj-geddes/useful-ai-prompts · Productivity

0

Implement GraphQL APIs with proper schema design, resolver patterns, error handling, and performance optimization for flexible client-server communication.

react-flow-implementation

existential-birds/beagle · Frontend

0

See ADDITIONAL_COMPONENTS.md for MiniMap, Controls, Background, NodeToolbar, NodeResizer.

oauth-implementation

aj-geddes/useful-ai-prompts · Productivity

0

Implement industry-standard OAuth 2.0 and OpenID Connect authentication flows with JWT tokens, refresh tokens, and secure session management.

design-patterns-implementation

aj-geddes/useful-ai-prompts · Frontend

0

Apply proven design patterns to create maintainable, extensible, and testable code architectures.

notion-spec-to-implementation

davila7/claude-code-templates · Productivity

0

Convert a Notion spec into linked implementation plans, tasks, and ongoing status updates.

prd-implementation-precheck

charon-fan/agent-playbook · Productivity

0

Perform a short PRD precheck, present issues and questions, then implement only after the user confirms or adjusts the PRD.

openspec-implementation

forztf/open-skilled-sdd · Productivity

0

Systematically implements approved spec proposals by executing tasks sequentially with proper testing and validation.

websocket-implementation

aj-geddes/useful-ai-prompts · Productivity

0

Build scalable WebSocket systems for real-time communication with proper connection management, message routing, error handling, and horizontal scaling support.

rag-implementation

sickn33/antigravity-awesome-skills · Productivity

0

Complete workflow for building RAG systems from embedding selection through evaluation and optimization. \n \n Covers eight sequential phases: requirements analysis, embedding selection, vector database setup, chunking strategy, retrieval implementation, LLM integration, caching, and evaluation \n Includes actionable steps for each phase with specific skills to invoke and copy-paste prompts for agent commands \n Addresses core RAG concerns: embedding quality, vector indexing, chunk overlap handl

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