Skill UI

Automates guidance for Apdex scoring within performance testing, covering setup, best practices, code/config generation, and validation.

Automates guidance for Apdex scoring within performance testing, covering setup, best practices, code/config generation, and validation.

This tool provides comprehensive cost management for Apollo.io API usage. It optimizes credit expenditure by implementing a daily budget tracker, preventing double-enrichment through deduplication, and using a lead scoring mechanism to ensure credits are only spent on high-potential leads. Ideal for large-scale data enrichment tasks to minimize API costs.

This skill assists in optimizing app listings on the App Store and Google Play. It covers keyword research, metadata optimization, and competitor analysis to improve search rankings and visibility. Users can utilize this for keyword scoring, title and subtitle optimization, and identifying keyword gaps before writing metadata. It ensures compliance with platform character limits and best practices for ASO.

This skill assists in optimizing app listings on the App Store and Google Play. It covers keyword research, metadata optimization, and competitor analysis to improve search rankings and visibility. Users can utilize this for keyword scoring, title and subtitle optimization, and identifying keyword gaps before writing metadata. It ensures compliance with platform character limits and best practices for ASO.

Audits and improves CLAUDE.md files across repositories by discovering every relevant file, scoring its sections, reporting quality issues, and preparing targeted additions before any write actions.

A comprehensive guide for designing production-grade lead enrichment pipelines in Clay. Covers three integration patterns (Webhook, API), detailed table schema design, advanced data waterfall logic, implementing complex ICP scoring formulas, and setting up robust CRM synchronization flows for go-to-market data operations.

CLIP enables general-purpose vision-language tasks through zero-shot image classification, similarity scoring, semantic search, and content moderation using pretrained contrastive embeddings without fine-tuning.

This advanced system transforms session interactions into reusable, atomic knowledge units called "Instincts." By hooking into tool usage and session activity, it observes patterns (like preferred coding styles, error fixes, or repeated workflows). These instincts are then clustered and "evolved" into structured Skills, Commands, or specialized Agents, capturing deep contextual knowledge with weighted confidence scoring to enhance development productivity.

A comprehensive toolkit for building production-grade AI agents focused on crypto business development. This skill guides users through the entire token listing lifecycle: autonomously discovering promising assets across multiple chains, applying a weighted 100-point scoring model, verifying safety via advanced wallet forensics, and managing outreach pipelines with necessary human oversight and LLM cascade patterns.

This architecture provides a scalable, production-ready framework for integrating AI candidate analysis with Juicebox. It manages automated data ingestion from job descriptions, ranks candidates using intelligent scoring, handles result caching, and facilitates seamless data export to major ATS platforms like Greenhouse and Lever. Designed for high-volume, complex hiring pipelines requiring data freshness, deduplication, and robust scaling.

A comprehensive workflow for implementing robust LLM evaluation using Langfuse. This skill guides users through key processes including scoring traces (numeric, categorical, boolean), collecting user feedback, managing versioned prompts, creating structured datasets, running automated experiments, and implementing LLM-as-a-Judge patterns for advanced quality assurance and system benchmarking.