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Found 22 Skills
Comprehensive debugging methodology for finding and fixing bugs (formerly debugging). This skill should be used when debugging code, investigating errors, troubleshooting issues, performing root cause analysis, or responding to incidents. Covers systematic reproduction, hypothesis-driven investigation, and root cause analysis techniques. Use when encountering exceptions, stack traces, crashes, segfaults, undefined behavior, or when bug reports need investigation.
Scientific method expert for systematic bug investigation and root cause analysis. Use when users report bugs, crashes, unexpected behavior, or debugging requests. Applies hypothesis-driven investigation, controlled experiments, and rigorous validation across any programming language or platform.
Use when hunting for threats in an environment, analyzing IOCs, or detecting behavioral anomalies in telemetry. Covers hypothesis-driven threat hunting, IOC sweep generation, z-score anomaly detection, and MITRE ATT&CK-mapped signal prioritization.
Improve activation, retention, and engagement through hypothesis-driven growth experiments.
Systematic debugging methodology — binary search isolation, hypothesis-driven debugging, reproducing issues, and root cause analysis. Use when debugging errors, unexpected behavior, or test failures.
Core consulting thinking frameworks and methodologies for structuring business problems, communicating findings, analyzing strategy, building financial models, and designing operations. Use when any agent or command needs to apply MECE decomposition, pyramid principle, hypothesis-driven analysis, issue trees, SCR communication, Porter's Five Forces, TAM/SAM/SOM market sizing, value chain analysis, NPV/IRR decision criteria, build/buy/partner evaluation, RACI matrices, or any standard consulting framework. This skill provides procedural guidance — not just framework names, but how to apply them correctly.
Help a CS or AI PhD student design hypothesis-driven experiments with baselines, variables, metrics, controls, logging, and stop conditions. Use this skill whenever the user is about to run experiments, compare models, plan an ablation, debug inconclusive results, prepare an experiment section, or wants to avoid changing too many things at once.
Systematic debugging with hypothesis-driven investigation. Use when diagnosing bugs, errors, or unexpected behavior. Phases: Reproduce, Hypothesize, Investigate, Fix, Verify, Regression.
This skill should be used when the user's request or requirement is ambiguous and needs iterative questioning to become actionable. Trigger on "clarify requirements", "refine requirements", "요구사항 명확히", "요구사항 정리", "뭘 원하는 건지", "make this clearer", "spec this out", "scope this", "/clarify". Turns vague inputs into concrete specs. For strategy blind spots use unknown; for content-vs-form reframing use metamedium.
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.