ai-collaboration-standards
Prevents AI hallucination and ensures evidence-based, verifiable outputs when analyzing code, reviewing technical documents, or providing recommendations.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
325 skills found
Prevents AI hallucination and ensures evidence-based, verifiable outputs when analyzing code, reviewing technical documents, or providing recommendations.
Expert Kokoro TTS implementation skill for real-time, secure, and offline voice synthesis in JARVIS-style assistants. Features streaming output, prosody control, and performance-optimized audio generation.
Designer's eye QA: detects and automates fixes for visual inconsistencies, spacing, hierarchy, and UI polish issues. Iteratively verifies with before/after screenshots.
Generate optimized SQL queries from natural language. Supports BigQuery, PostgreSQL, MySQL, and Snowflake. Analyze database schemas, interpret business requirements, and output ready-to-run queries with explanations.
Implement ReasoningBank adaptive learning with AgentDB's ultra-fast vector backend. Features trajectory tracking, verdict judgment, memory distillation, and pattern recognition for self-learning autonomous agents.
Automates the synchronization of new infographic templates by updating project documentation, gallery mappings, and AI playground prompts.
Create and test AI-ready MCP tools for any web application. Inject code, automate browser interactions, and turn websites into intelligent agents.
An automated memory middleware for AI agents, implementing a Retrieve-Respond-Save loop to maintain long-term persistent context across conversations.
Automate quality observability with DORA metrics, defect density tracking, and intelligent quality gate configuration for continuous delivery pipelines.
Generate realistic virtual product try-on visualizations to help customers evaluate fit, drape, and scale before purchasing.
Expert-level guidance for ffuf web fuzzing, enabling automated discovery of hidden directories, files, parameters, and vulnerabilities during penetration testing.
Enhance workflow efficiency by performing manual context compaction at logical task boundaries instead of relying on unpredictable auto-compaction.