Skill UI

A comprehensive guide to optimizing data enrichment workflows within Clay. Learn techniques to improve throughput, reduce API costs, and maximize data hit rates by correctly ordering enrichment columns, implementing conditional run rules, pre-cleaning raw leads, and managing auto-update settings. Use this when processing large datasets slowly or encountering high costs.

This skill provides advanced techniques to significantly reduce API costs when using Firecrawl for web scraping. It covers best practices such as setting crawl limits, utilizing batch scraping for known URLs, implementing local caching, and monitoring credit usage. Learn how to efficiently scrape large sites while managing budgets and minimizing unexpected billing spikes.

This expert tool orchestrates the complete onboarding experience for new team members in technical or remote-first organizations. It provides a structured roadmap covering pre-arrival preparation (access setup, documentation), Day 1 orientation, technical stack immersion (CI/CD, codebases), and cultural integration (buddy system). Use this when you need a comprehensive, step-by-step guide to rapidly transition new hires into productive, integrated team members.

A comprehensive guide to diagnosing and resolving common technical issues encountered with the Granola application. Covers fixes for audio capture failures, transcription quality degradation, calendar syncing problems, and integration errors with platforms like Slack and Notion. Includes platform-specific troubleshooting steps for macOS and Windows users.

This comprehensive guide details advanced automation patterns for Granola, covering three key integration channels: Zapier workflows (connecting Granola to over 8,000 apps), the robust Enterprise API (REST access), and native integrations (e.g., Slack, Notion). Use these patterns to automate meeting note processing, extract action items, and build multi-step follow-up pipelines across your entire tech stack.

This comprehensive guide outlines best practices for securing Granola meeting data. It covers critical compliance standards, including SOC 2 and GDPR, and details technical controls such as AES-256 encryption, TLS 1.3 transmission, and robust configuration of SSO and data retention policies for enterprise use.

This skill collects a comprehensive debug bundle from the Instantly workspace. It gathers critical metrics including campaign state, account health, warmup analytics, webhooks, and background job status. The resulting structured JSON report is ideal for creating detailed support tickets, performing technical troubleshooting, or conducting internal system audits.

A comprehensive guide to managing and reducing costs associated with Obsidian's paid services (Sync, Publish) and external API calls from community plugins. Learn how to audit vault size, implement secure exclusion patterns for sync, and utilize coding techniques like caching and rate limiting to control usage fees from external services (e.g., AI).

A comprehensive command-line utility designed to systematically audit the quality, relevance, and redundancy of all stored Claude skills and commands. It supports both quick scans (checking only recent changes) and full inventory assessments. The process evaluates skills against a detailed checklist, checking for content overlaps, technical staleness, and unique functional value to ensure a robust and up-to-date knowledge base.

Context Guardian preserves critical context before Claude Code’s automatic compression, collecting verified snapshots, detecting integrity issues, and briefing transitions so heavy-technical sessions keep every essential decision and dependency intact.

Captures coding session learnings by extracting corrections, successes, and anti-patterns with confidence scoring so teams can track, reinforce, and reuse technical knowledge across projects.

This utility compresses verbose natural language notes, memories, and documents (like markdown files) into a highly dense format. By eliminating filler words, redundant phrases, and grammatical fluff, it significantly reduces the token count for large inputs. Crucially, it strictly preserves all technical content, including code blocks, inline code, URLs, file paths, and commands, ensuring technical accuracy while boosting AI processing efficiency.