Loading...
Loading...
Found 337 Skills
Apply scientific debugging methodology through conversational investigation. Use when investigating bugs, forming hypotheses, tracing error causes, performing root cause analysis, or systematically diagnosing issues. Includes progressive disclosure patterns, observable actions principle, and user-controlled dialogue flow.
AI-powered JavaScript reverse engineering tool. Senior JavaScript reverse engineering expert assistant. Actions: collect, search, deobfuscate, understand, summarize, detect-crypto, browser, debugger, breakpoint, debug-step, debug-eval, debug-vars, script, hook, stealth, dom, page. Capabilities: obfuscated code analysis, VM cracking, Webpack unpacking, AST transformation, Puppeteer/CDP automation, anti-detection, fingerprint spoofing, encryption identification, parameter extraction, algorithm restoration, Canvas/WebGL fingerprinting, WebDriver hiding, CDP debugging, breakpoint analysis, dynamic tracing, Hook injection, DOM inspection, page control.
Use when designing distributed systems, decomposing monoliths, or implementing microservices patterns. Invoke for service boundaries, DDD, saga patterns, event sourcing, service mesh, distributed tracing.
LLM observability platform for tracing, evaluation, prompt management, and cost tracking. Use when setting up Langfuse, monitoring LLM costs, tracking token usage, or implementing prompt versioning.
Full Sentry SDK setup for NestJS. Use when asked to "add Sentry to NestJS", "install @sentry/nestjs", "setup Sentry in NestJS", or configure error monitoring, tracing, profiling, logging, metrics, crons, or AI monitoring for NestJS applications. Supports Express and Fastify adapters, GraphQL, microservices, WebSockets, and background jobs.
Investigate Bedrock AgentCore runtime sessions via CloudWatch Logs Insights — resolve session/trace IDs, query OTEL spans, filter noise, build timelines. Use when debugging AgentCore agent sessions, tracing tool calls, or analyzing latency.
DeepEval evaluation workflow for AI agents and LLM applications. TRIGGER when the user wants to evaluate or improve an AI agent, tool-using workflow, multi-turn chatbot, RAG pipeline, or LLM app; add evals; generate datasets or goldens; use deepeval generate; use deepeval test run; add tracing or @observe; send results to Confident AI; monitor production; run online evals; inspect traces; or iterate on prompts, tools, retrieval, or agent behavior from eval failures. AI agents are the primary use case. Covers Python SDK, pytest eval suites, CLI generation, tracing, Confident AI reporting, and agent-driven improvement loops. DO NOT TRIGGER for unrelated generic pytest, non-AI test setup, or non-DeepEval observability work unless the user asks to compare or migrate to DeepEval.
Salesforce Data Cloud Act phase. Use this skill when the user manages activations, activation targets, data actions, or downstream delivery of Data Cloud audiences and data. TRIGGER when: user manages activations, activation targets, data actions, or downstream delivery of Data Cloud audiences and data. DO NOT TRIGGER when: the task is segment creation (use segmenting-datacloud), data retrieval/search work (use retrieving-datacloud), or STDM/session tracing (use observing-agentforce).
Bytecode interpreter and JIT compiler skill for implementing language runtimes in C/C++. Use when designing bytecode dispatch loops (switch, computed goto, threaded code), implementing stack-based or register-based VMs, adding a simple JIT using mmap/mprotect, or understanding performance trade-offs in interpreter design. Activates on queries about bytecode VMs, dispatch loops, computed goto, JIT compilation basics, tracing JITs, or implementing a scripting language runtime.
AI-powered systematic codebase analysis. Combines mechanical structure extraction with Claude's semantic understanding to produce documentation that captures not just WHAT code does, but WHY it exists and HOW it fits into the system. Includes pattern recognition, red flag detection, flow tracing, and quality assessment. Use for codebase analysis, documentation generation, architecture understanding, or code review.
Full Sentry SDK setup for Apple platforms (iOS, macOS, tvOS, watchOS, visionOS). Use when asked to "add Sentry to iOS", "add Sentry to Swift", "install sentry-cocoa", or configure error monitoring, tracing, profiling, session replay, or logging for Apple applications. Supports SwiftUI and UIKit.
Use this skill when designing backend systems, databases, APIs, or services. Triggers on schema design, database migrations, indexing strategies, distributed systems architecture, microservices, caching, message queues, observability setup, logging, metrics, tracing, SLO/SLI definition, performance optimization, query tuning, security hardening, authentication, authorization, API design (REST, GraphQL, gRPC), rate limiting, pagination, and failure handling patterns. Acts as a senior backend engineering advisor for mid-level engineers leveling up.