Skill UI

A comprehensive mechanism for managing data privacy, retention, and compliance when integrating with Gamma APIs. This skill guides users on handling Personally Identifiable Information (PII), sanitizing user content and logs, securely archiving exported files (e.g., via S3), and implementing precise data retention policies to meet standards like GDPR and CCPA. Essential for building compliant, production-ready applications.

Automates the conversion of meeting notes from Granola into structured development artifacts. This pipeline uses Zapier and GitHub Actions to automatically extract action items and decisions, creating corresponding issues in GitHub, tasks in Linear, and summarizing updates in Slack. It ensures that meeting outcomes are immediately tracked and integrated into the development workflow.

This comprehensive workflow automates the entire post-meeting cycle. It enhances raw transcripts by structuring notes, extracting action items, and summarizing key decisions using advanced AI models. Users can then seamlessly share these structured notes to integrations like Slack, Notion, and CRMs (HubSpot), while also using the Chat feature to draft follow-up emails and automatically update contact records in the built-in CRM.

This skill outlines the complete workflow for capturing and structuring meeting notes using Granola. It captures audio, transcribes the content, and intelligently merges live user notes with the full transcript. The output is structured into key sections like Summaries, Decisions, and Action Items. Users can also interact with Granola Chat for post-meeting Q&A, making it an essential tool for professional meeting documentation and follow-up.

This comprehensive runbook guides incident response for Groq API failures. It covers triage steps, severity classification (P1-P4), troubleshooting decision trees (e.g., handling 429 errors or 5xx outages), and implementing immediate mitigations like model fallback or key rotation. Use this when responding to Groq outages to restore service stability.

Guides developers in crafting and refining fuzzing harnesses that parse random fuzzer input, validate data, and exercise targeted API paths to improve coverage and find reproducible bugs.

This skill automatically gathers and structures comprehensive context for Apple product development. It analyzes existing project files (like README, Info.plist, and code) and prompts the user for details across product overview, target platforms, technology stack, design system, and accessibility requirements. The output is a structured context document used by other specialized skills to ensure consistent, informed design and development decisions.

Provides calibration-free weight quantization for LLMs down to 1-bit precision with multiple optimized backends, ideal for fast deployments with vLLM/HuggingFace and LoRA/PEFT fine-tuning without collecting calibration data.

HuggingFace Accelerate adds distributed support to any PyTorch script with just four lines, providing a unified API for DDP, DeepSpeed, FSDP, and Megatron, plus automatic device placement, mixed precision, and simple launch/config commands for fast prototyping.

This skill provides structured patterns for combining vector similarity search with traditional keyword-based search. It is essential for building advanced Retrieval-Augmented Generation (RAG) systems, sophisticated search engines, or any retrieval task requiring high recall. Use this when pure semantic understanding is insufficient, and precise matching of specific terms (like names or codes) is necessary.

A comprehensive runbook for rapid incident response concerning Ideogram API outages, authentication failures, and performance degradation. It provides detailed triage scripts, decision flows based on HTTP status codes (401, 429, 5xx), immediate mitigation actions (e.g., enabling fallback mode), and structured templates for communication and postmortems.

Master the creation of autonomous AI agents using LangChain. This guide covers defining tools with schemas (Zod), utilizing `createToolCallingAgent` and `AgentExecutor` for multi-step decision-making, managing conversation memory, and implementing streaming output. Ideal for building complex, tool-augmented workflows.