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Debugging workflows for Python (pdb, debugpy), Go (delve), Rust (lldb), and Node.js, including container debugging (kubectl debug, ephemeral containers) and production-safe debugging techniques with distributed tracing and correlation IDs. Use when setting breakpoints, debugging containers/pods, remote debugging, or production debugging.
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Systematic debugging methodology covering root cause analysis, log-based debugging, production debugging, memory leak diagnosis, deadlock detection, performance anomaly investigation, and reproducible bug reproduction. Distilled from real experience at Google, DeepMind, ByteDance, and Huawei. USE WHEN: investigating production incidents, debugging hard-to-reproduce bugs, analyzing crash dumps, finding race conditions, diagnosing memory leaks, debugging performance regressions, or any situation requiring systematic root cause analysis. Triggers on "debugging", "root cause", "bug", "crash", "segfault", "deadlock", "race condition", "memory leak".
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Identifies and bypasses anti-debugging and anti-analysis checks in malware: PEB flags, debugger-detection APIs, timing checks, and exception tricks, then neutralizes them to continue analysis. Activates for requests to identify anti-debugging, bypass anti-debug checks, or analyze evasion that blocks a debugger.
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Guides debugging through reproduction, comparison with working examples, hypothesis testing, and a focused fix. It starts with a repeatable check for the exact symptom and uses regression tests to verify the result.
Connect to an existing Chrome browser or launch one with an explicit debugging port for sharing across agents and tools. It also covers scanning local CDP ports, the connection endpoints used to control a browser.
[omh] Native program crashes or corrupts memory: prepare hypothesis-driven debugging of native binaries and instruct the executor to drive a DAP debugger instead of printf. Use when the user says: native-debugging, native debugging, native binary, segfault, segmentation fault, core dump, stack corruption, memory corruption.
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[omh] Application code misbehaves -- a wrong value, a flaky test, a lost update: reproduce it first, form competing hypotheses, discriminate them with the cheapest observation, and only then fix the demonstrated root cause. Use when the user says: app-debugging, app debugging, application debugging, root cause, root-cause, find the root cause, root cause analysis, flaky test.
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You are a debugging expert specializing in setting up comprehensive debugging environments, distributed tracing, and diagnostic tools. Configure debugging workflows, implement tracing solutions, and establish troubleshooting practices for development and production environments.
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Comprehensive debugging specialist for errors, test failures, log analysis, and system problems. Use when encountering issues, analyzing error logs, investigating system anomalies, debugging production issues, analyzing stack traces, or identifying root causes. Combines general debugging workflows with error pattern detection and log analysis.
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Use when tutoring a learner through MATLAB debugging, error interpretation, failed tests, incorrect outputs, array-shape problems, indexing mistakes, function argument issues, or code repair practice. Use for guided debugging sessions, debugging drills, teach-the-agent critique, and evidence-based MATLAB troubleshooting.
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Systematic debugging methodology - scientific method, hypothesis testing, and root cause analysis that works across all technologiesUse when "bug, debugging, not working, broken, investigate, root cause, why is this happening, figure out, troubleshoot, doesn't work, unexpected behavior, debugging, root-cause, hypothesis, scientific-method, troubleshooting, bug-hunting, investigation, problem-solving" mentioned.
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Turn lessons from complex tasks, debugging, failures, or rollbacks into short learning entries. Capture the root cause, a reusable action, and a recognizable signal, then merge examples that share a cause with existing notes.
Investigate failing tests, broken builds, and unexpected behavior through a structured debugging process. Reproduce the failure, narrow down its source, fix the root cause, and add a regression test that catches the same problem.
Investigate crashes, hangs, incorrect responses, and other failures by testing hypotheses against observed runtime behavior. Confirm the cause, verify the fix with a reproducing test, check the real workflow, and remove temporary debugging artifacts.
Investigate complex bugs by assigning competing explanations to parallel agents. Compare code evidence, contradictory findings, and confidence levels to identify likely root causes, then check that a fix resolves the original failure and related edge cases.
Configure browser startup with a visible or hidden window, and optionally open a local debugging port for another tool to connect. The skill explains launch parameters, compatibility behavior, and when a browser restart is required.
Investigate difficult bugs by adding temporary logs to the actual execution path and reproducing the reported behavior. Analyze JSONL logs, which store one structured event per line, then fix the cause, verify regression tests, and remove debugging artifacts.
Investigate difficult bugs through explicit hypotheses, targeted logs, and repeated checks against actual data. Keep an investigation journal to track findings, examine environment differences and leftover state, and confirm the root cause before applying a fix.
Turns recurring workflows and proven debugging solutions into reusable skill packages. It creates SKILL.md, a readable overview, and optional supporting examples from a user description or saved project memory, with checks for completeness and portability.
Draws points, lines, arrows, boxes, and text over a running CARLA world to visually check positions, paths, and objects. Sets colors and display lifetimes for temporary debugging overlays, without creating persistent actors or output files.
Guides PyTorch model training and fine-tuning, from choosing architectures to configuring optimizers and learning rates. Provides troubleshooting steps for unstable loss and memory shortages, plus methods for improving GPU throughput and comparing experiments.
Find and load the appropriate skill for an agent working inside OpenRig. This index maps situations such as recovery, handover, debugging, and planning to relevant guidance, with commands that retrieve content matching the installed CLI.
Troubleshoot Golang programs systematically - find and fix the root cause. Use when encountering bugs, crashes, deadlocks, races, or unexpected behavior in Go code. Covers debugging methodology, common Go pitfalls, test-driven debugging, pprof setup and capture, Delve, race detection, GODEBUG tracing, and production debugging. Start here for any 'something is wrong' situation. Not for interpreting profiles or benchmarking (→ See `samber/cc-skills-golang@golang-benchmark` skill), applying optimization patterns (→ See `samber/cc-skills-golang@golang-performance` skill), or designing new code (→ See `samber/cc-skills-golang@golang-safety` skill for defensive coding, `samber/cc-skills-golang@golang-concurrency` skill for concurrency design).
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Expert DevOps troubleshooter specializing in rapid incident response, advanced debugging, and modern observability. Masters log analysis, distributed tracing, Kubernetes debugging, performance optimization, and root cause analysis. Handles production outages, system reliability, and preventive monitoring. Use PROACTIVELY for debugging, incident response, or system troubleshooting.
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