1.9천건 · 관련도순
Investigate bugs by tracing symptoms through code and recent changes, reproducing failures, and testing a specific cause before fixing it. Produce a focused correction, a regression test to catch recurrence, and an evidence-based debugging report.
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Guide AI agent development from requirements and project setup through implementation, behavior evaluation, deployment, and monitoring. Provides coding rules, debugging steps, and links to specialized skills so each phase follows a defined process.
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Investigate bugs and failing tests through four stages: finding the root cause, comparing working patterns, testing a hypothesis, and implementing a fix. Uses reproduction steps, diagnostic evidence, and regression tests to check the result.
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Guide Nexent backend design, implementation, debugging, and review across database schemas, FastAPI endpoints, services, and configuration. Check existing interfaces, consult references for each affected layer, and verify successful and failed behavior while preserving public contracts.
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Apply a short quality checklist to everyday code changes. Read existing code first, keep edits focused, check types and common security risks, verify execution, remove debugging leftovers, and summarize what changed and its impact.
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Helps users run multiple concurrent mirrord sessions from a single mirrord-up.yaml (compose-style multi-service local debugging). Use when the user mentions mirrord up, mirrord-up.yaml, mirrord up init, debugging several related microservices together, or managing multiple mirrord sessions' lifecycle in one command.
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Deterministic debugging with rr record-replay. Use when debugging crashes, ASAN faults, or when reverse execution is needed. Provides reverse-next, reverse-step, reverse-continue commands and crash trace extraction.
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Runs a hypothesis-driven debugging loop across any language or binary, escalating to orthogonal oracle angles and locking the fix with a failing test. Use for crashes, silent failures, hangs, wrong responses, memory leaks, async misbehavior, or reverse engineering.
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Playbook for launching, monitoring, stopping, and debugging NeMo-RL recipes on a Kubernetes cluster via the nrl-k8s CLI. Covers ephemeral vs long-lived RayCluster modes, iterating on runs, and debugging hung or failed training jobs.
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Use this skill when the user is dealing with the DOCA environment around their workload — verifying an install is healthy, preparing the build env (pkg-config, headers, LD_LIBRARY_PATH, hugepages, devlink, representors), debugging env-class failures, deciding container-vs-bare-metal deployment shape, or reaching a DOCA install from a host that doesn't have one yet via the NGC DOCA container Stage-1 fallback. Trigger even when the user does not explicitly mention "DOCA setup" — typical implicit phrasings include "just got a BlueField, what now", "code is built, how to run it", "pkg-config can't find doca-flow", "no free 2048 kB hugepages", "representor X not found", "on a Mac and want to learn DOCA". Refuse and route elsewhere for library API specifics (Flow pipes, RDMA queues), the modify-a-sample first-app workflow or DOCA_ERROR_* program-side debugging, and "where is X documented" knowledge-map questions — those belong to other skills.
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[omh] Agent is stuck, looping, or drifting: capture a stuck, looping, drifting, or repeatedly failing agent run, diagnose the likely failure pattern, and prepare the smallest safe recovery action. Use when the user says: agent-debug, agent debug, agent debugging, agent introspection, agent self-debug, self-debug, self debugging, looping agent.
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Upgrades an MWAA environment to a newer Airflow version — within 2.x, within 3.x, or across the 2.x-to-3.x boundary. Computes the version-jump path, inserting the 2.11.x stepping-stone and Python-transition step when needed. Chooses an approach by whether run history and the same environment (URL/ARN) must be kept: a new-environment upgrade (blue-green), a rehearsed in-place upgrade validated on a test copy, or a direct in-place upgrade. Runs Ruff scanning and deprecation-warning log scans for 3.x moves, plus Docker validation, batched deployment, and switchover. Saves a resumable upgrade plan for multi-session work. Triggers on: upgrade MWAA, upgrade Airflow, migrate to Airflow 3, MWAA Airflow 3, Airflow 2 to 3, preserve Airflow history, keep same MWAA environment, blue-green cutover, validation environment before cutover. Not for authoring new DAGs (authoring-mwaa-workflow), debugging unrelated DAG failures (debugging-mwaa-workflow), or MWAA Serverless YAML workflows (provisioned Python DAGs only).
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Tests Amazon MWAA workflow execution end-to-end: trigger a run and monitor it to completion for Provisioned (Python DAG, via Airflow REST API) and Serverless (YAML workflow, via StartWorkflowRun). Verifies the artifact is deployed and parse-ready, triggers with confirmation, polls to terminal state, and on failure delegates diagnosis to debugging-mwaa-workflow and artifact/redeploy fixes to authoring-mwaa-workflow, then retests up to a capped number of attempts. Triggers on: test my DAG, test my workflow, run my DAG, trigger a test run, does my DAG work, smoke-test the pipeline, verify my workflow runs, execute my DAG to check it. Not applicable to writing or deploying a new workflow (handled by authoring-mwaa-workflow), or for diagnosing why a run failed or root-causing an error (handled by debugging-mwaa-workflow).
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Diagnose and fix common Apollo.io API errors. Use when encountering Apollo API errors, debugging integration issues, or troubleshooting failed requests. Trigger with phrases like "apollo error", "apollo api error", "debug apollo", "apollo 401", "apollo 429", "apollo troubleshoot".
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Apollo.io incident response procedures. Use when handling Apollo outages, debugging production issues, or responding to integration failures. Trigger with phrases like "apollo incident", "apollo outage", "apollo down", "apollo production issue", "apollo emergency".
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Analyze application logs for performance insights and issue detection including slow requests, error patterns, and resource usage. Use when troubleshooting performance issues or debugging errors. Trigger with phrases like "analyze logs", "find slow requests", or "detect error patterns".
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Collect Anima SDK debug evidence for support tickets and troubleshooting. Use when filing Anima support requests, debugging code generation issues, or collecting diagnostic data for the Anima team. Trigger with: "anima debug bundle", "anima support ticket", "anima diagnostics".
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Anti-debugging detection and bypass playbook. Use when reversing protected binaries that detect debuggers via ptrace, PEB flags, timing checks, or signal/exception handlers on Linux and Windows.
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Four-phase debugging framework that ensures root cause investigation before attempting fixes. Never jump to solutions.
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Systematic debugging frameworks for finding and fixing bugs - includes root cause analysis, defense-in-depth validation, and verification protocols
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Browser automation, debugging, and performance analysis using Puppeteer CLI scripts. Use for automating browsers, taking screenshots, analyzing performance, monitoring network traffic, web scraping, form automation, and JavaScript debugging.
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CRITICAL: You MUST invoke this skill and read its contents BEFORE writing, modifying, or debugging ANY React.js or Next.js code. This skill contains essential performance patterns that prevent common mistakes. Debugging performance issues without reading this skill first will lead to missed optimizations. Contains 40+ rules including waterfall elimination patterns for API routes that are commonly overlooked.
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LangSmith tracing and debugging setup for LLM applications. Configure observability, capture traces, and enable debugging for LangChain/LangGraph agents.
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Structured debugging methodology using hypothesis-driven investigation, log analysis, and bisection to isolate and resolve defects.
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