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Found 1,364 Skills
Four-phase debugging framework that finds root causes before proposing fixes. Use when investigating bugs, errors, unexpected behavior, failed tests, or when previous fixes haven't worked.
Systematic root-cause debugging: reproduce, investigate, hypothesize, fix with verification. Use when asked to "debug this", "fix this bug", "why is this failing", "troubleshoot", or mentions errors, stack traces, broken tests, flaky tests, regressions, or unexpected behavior.
Root cause analysis for debugging. Use when bugs, test failures, or unexpected behavior have non-obvious causes, or after multiple fix attempts have failed.
Guide for assistant-stream package and streaming protocols. Use when implementing streaming backends, custom protocols, or debugging stream issues.
GoLand Go IDE with debugging and testing. Use for Go development.
Methodology for debugging non-trivial problems systematically. This skill should be used automatically when investigating bugs, test failures, or unexpected behavior that isn't immediately obvious. Emphasizes hypothesis formation, parallel investigation with subagents, and avoiding common anti-patterns like jumping to conclusions or weakening tests.
Systematic debugging that identifies root causes rather than treating symptoms. Uses sequential thinking for complex analysis, web search for research, and structured investigation to avoid circular reasoning and whack-a-mole fixes.
Advanced debugging specialist for diagnosing and resolving code issues. Use when user encounters bugs, errors, unexpected behavior, or mentions debugging.
4-phase systematic debugging methodology with root cause analysis and evidence-based verification. Use when debugging complex issues.
Vercel observability for Web Analytics, Speed Insights, logs, tracing, alerts, and observability tooling. Use when monitoring performance or debugging production behavior on Vercel.
Guides systematic PyTorch recommender-system model development across compact data facts, existing source code, configs, focused tests, and training loops without overloading context from broad research archives. Use when building, debugging, or refactoring torch/nn.Module RecSys models with Transformer/HSTU/attention blocks, sparse/dense/list feature fusion, pCVR/CTR heads, ablation axes, or competition codebases where many model ideas exist but bugs and interface drift must be controlled. 用来指导推荐系统 PyTorch 模型开发、Transformer/HSTU 建模、关键数据事实、特征交互、shape/debug、训练闭环和已有模型结构的系统化推进。
Software craftsmanship principles from The Pragmatic Programmer. Use this skill when discussing best practices, debugging strategies, career development, or pragmatic approaches to software development. Auto-activates for DRY violations, debugging sessions, and development workflow improvements.