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Found 1,403 Skills
Systematic debugging for ADK agents — trace reading, log analysis, common failure diagnosis, and the debug loop.
Guide developers through creating ChatGPT apps. Covers the full lifecycle: brainstorming ideas against UX guidelines, bootstrapping projects, implementing tools/widgets, debugging, running dev servers, deploying and connecting apps to ChatGPT. Use when a user wants to create or update a ChatGPT app / MCP server for ChatGPT, or use the Skybridge framework.
Master network protocol reverse engineering including packet analysis, protocol dissection, and custom protocol documentation. Use when analyzing network traffic, understanding proprietary protocols, or debugging network communication.
AWS Lambda serverless functions for event-driven compute. Use when creating functions, configuring triggers, debugging invocations, optimizing cold starts, setting up event source mappings, or managing layers.
Expert debugging workflows including print debugging (push_warning, push_error, assert), breakpoints (conditional breakpoints), Godot Debugger (stack trace, variables, remote debug), profiler (time profiler, memory monitor), error handling patterns, and performance optimization. Use for bug fixing, performance tuning, or development diagnostics. Trigger keywords: breakpoint, print_debug, push_error, assert, profiler, remote_debug, memory_leak, orphan_nodes, Performance.get_monitor.
Bubble.io plugin development rules, API reference, and coding standards. Use when working on any task in this repo: writing, reviewing, refactoring, or creating initialize.js, update.js, preview.js, header.html, element actions, client-side actions, server-side actions (SSA), Plugin API v4 async/await code, JSDoc, setup files, README, CHANGELOG, marketplace descriptions, or field tooltips. Also use for security audits, code review, debugging, and publishing plugins. Covers instance/properties/context objects, BubbleThing/BubbleList interfaces, data loading suspension, DOM/canvas rules, element vs shared headers, exposed states, event handling, ESLint standards, and Bubble hard limits.
Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.
CI/CD pipeline design, optimization, DevSecOps security scanning, and troubleshooting. Use for creating workflows, debugging pipeline failures, implementing SAST/DAST/SCA, optimizing build performance, implementing caching strategies, setting up deployments, securing pipelines with OIDC/secrets management, and troubleshooting common issues across GitHub Actions, GitLab CI, and other platforms.
Comprehensive Biome (biomejs.dev) integration for professional TypeScript/JavaScript development. Use for linting, formatting, code quality, and flawless Biome integration into codebases. Covers installation, configuration, migration from ESLint/Prettier, all linter rules, formatter options, CLI usage, editor integration, monorepo setup, and CI/CD integration. Use when working with Biome tooling, configuring biome.json, setting up linting/formatting, migrating projects, debugging Biome issues, or implementing production-ready Biome workflows.
Trace every user-facing button/touchpoint through its full state change sequence to find bugs where functions individually work but cancel each other out, produce wrong final state, or leave the UI in an inconsistent state. Use when: systematic debugging found no bugs but users report broken buttons, or after any major refactor touching shared state stores.
Add Privy authentication to a Solana Expo Android app on top of Mobile Wallet Adapter, using Sign-In-With-Solana. Use when installing @privy-io/expo, mounting PrivyProvider, logging a user in with useLoginWithSiws, linking a wallet to an existing Privy account, reading the Privy access token from a backend, or debugging a Privy plus MWA setup.
Python observability patterns including structured logging, metrics, and distributed tracing. Use when adding logging, implementing metrics collection, setting up tracing, or debugging production systems.