Skill UI

This guide outlines essential security best practices for integrating Firecrawl. It covers secure API key storage (using environment variables and .gitignore), verifying webhook signatures with HMAC-SHA256 for data integrity, implementing key rotation strategies across different environments (dev/staging/prod), and sanitizing scraped content to mitigate XSS and PII risks. Essential for building secure, production-grade systems.

Comprehensive guide for upgrading the Firecrawl SDK and migrating between major API versions (v0 to v1/v2). It details breaking changes across key functions like `crawlUrl`, `scrapeUrl`, and the new extract schema format. Use this when updating dependencies or transitioning from legacy API structures to the latest v2 standard.

This guide provides comprehensive setup for implementing CI/CD pipelines for Fireflies.ai integrations. It covers best practices for validating GraphQL queries, running mock-based unit tests (using Vitest), and executing live integration tests against the actual API. It integrates seamlessly with GitHub Actions, ensuring that code changes are automatically validated in a stable, automated testing environment before deployment. Essential for maintaining API stability and robustness.

This comprehensive guide serves as a quick reference for diagnosing and fixing common errors encountered when interacting with the Fireflies.ai GraphQL API. It covers specific error codes (e.g., authentication failure, rate limits, invalid parameters) and provides actionable fixes, diagnostic scripts, and schema introspection techniques, ensuring seamless API integration and rapid troubleshooting.

This guide details the comprehensive setup process for authenticating with the Fireflies.ai GraphQL API. It covers retrieving the API key, configuring environment variables, and verifying connectivity using code examples for Node.js, Python, and cURL. Essential for securely integrating Fireflies data into external applications and automating transcript access.

This guide details a complete local development workflow for integrating with Fireflies.ai GraphQL. It outlines setting up the project structure, recording real API responses as mock fixtures, creating a custom mock client, and writing robust unit tests. Use this methodology to ensure fast, reliable, and isolated development cycles without dependency on live APIs or worrying about rate limits.

This comprehensive guide details how to configure Fireflies.ai across multiple isolated environments (Development, Staging, and Production). It emphasizes best practices for managing environment-specific secrets, API keys, and webhook URLs. By implementing robust environment separation, you can prevent data leakage and ensure that changes in one environment do not impact the stability or data integrity of others, which is crucial for CI/CD pipelines.

A comprehensive guide detailing best practices for optimizing GraphQL queries against external APIs, demonstrated using Fireflies.ai. Learn advanced techniques such as requesting minimum required fields (field selection), implementing robust caching strategies (LRU/Redis), and managing rate limits through controlled batch processing and pagination. This ensures stable, fast, and scalable data retrieval.

A comprehensive guide and implementation for managing rate limits, exponential backoff, and request queuing when interacting with the Fireflies.ai GraphQL API. This skill ensures robust API calling by detecting 429 errors, handling per-plan quotas, and implementing reliable retry mechanisms.

This guide outlines essential security protocols for integrating Fireflies.ai. It covers best practices for managing sensitive API keys (e.g., environment variables, pre-commit hooks), implementing robust HMAC-SHA256 webhook signature verification in multiple languages (TypeScript, Python), and correctly configuring transcript privacy levels to ensure data confidentiality. Ideal for developers setting up production-ready integrations.

This guide teaches developers how to achieve type-safe and predictable error handling in TypeScript using functional programming concepts from fp-ts, specifically focusing on the Either and TaskEither patterns. Instead of relying on disruptive try/catch exceptions, this pattern treats errors as explicit data values, forcing the calling code to handle failure paths, which is crucial for building robust, maintainable, and functional APIs and services.

This structured workflow guides the systematic, low-risk modernization of legacy systems using the Strangler Fig pattern. It provides a phased approach, starting with comprehensive assessment (risk, dependency mapping), establishing robust test coverage (contract testing, data management), and implementing incremental migration infrastructure (API gateways, feature flags) to ensure continuous business operation during the transition.