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Found 333 Skills
Axum (Rust) web framework patterns for production APIs: routers/extractors, state, middleware, error handling, tracing, graceful shutdown, and testing
Arrow of Root Tracing - a vertical deep-dive thinking tool. Given an opinion, phenomenon or problem, it drills all the way down like an arrow to the irreducible essence. Use when user says '想透', '追本', '本质是什么', '为什么会这样', '深挖', '钻到底', 'think deep', 'drill down', or wants to trace any idea/phenomenon vertically to its irreducible root. Also trigger when user provides a statement and wants depth analysis, not breadth survey.
Use when building cloud-native apps. Keywords: kubernetes, k8s, docker, container, grpc, tonic, microservice, service mesh, observability, tracing, metrics, health check, cloud, deployment, 云原生, 微服务, 容器
Use when designing distributed systems, decomposing monoliths, or implementing microservices patterns. Invoke for service boundaries, DDD, saga patterns, event sourcing, service mesh, distributed tracing.
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.
AI-powered codebase security scanner that reasons about code like a security researcher — tracing data flows, understanding component interactions, and catching vulnerabilities that pattern-matching tools miss. Use this skill when asked to scan code for security vulnerabilities, find bugs, check for SQL injection, XSS, command injection, exposed API keys, hardcoded secrets, insecure dependencies, access control issues, or any request like "is my code secure?", "review for security issues", "audit this codebase", or "check for vulnerabilities". Covers injection flaws, authentication and access control bugs, secrets exposure, weak cryptography, insecure dependencies, and business logic issues across JavaScript, TypeScript, Python, Java, PHP, Go, Ruby, and Rust.
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.
Azure Monitor OpenTelemetry Exporter for Java. Export OpenTelemetry traces, metrics, and logs to Azure Monitor/Application Insights. Triggers: "AzureMonitorExporter java", "opentelemetry azure java", "application insights java otel", "azure monitor tracing java". Note: This package is DEPRECATED. Migrate to azure-monitor-opentelemetry-autoconfigure.
Enforces consistent structured logging with request correlation IDs, standardized log schema, middleware integration, and best practices. Use for "structured logging", "log standardization", "request tracing", or "log correlation".
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.
Comprehensive financial audit tool for balance sheets and income statements. Use when Claude needs to verify balance sheet equilibrium, validate income statement items against detail records, track account changes with opening/closing balance reconciliation, verify cross-statement relationships, or generate audit reports with account analysis and transaction 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.