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Found 25 Skills
Interactive hypothesis-driven debugging with documented exploration, understanding evolution, and analysis-assisted correction.
Improve activation, retention, and engagement through hypothesis-driven growth experiments.
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.
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.
Guides proactive threat hunting for advanced SOC—hypothesis-driven hunt campaigns, advanced SIEM/query workflows, baseline and anomaly analysis, MITRE ATT&CK–aligned techniques, threat intel fusion, detection engineering feedback, and hunt reporting with IR handoff. Use for threat hunting, proactive hunt, hypothesis-driven detection, advanced SOC, hunt campaign, detection engineering, MITRE ATT&CK hunt, anomaly hunting—not routine SOC alert triage (soc-analyst), declared incident command (incident-responder), adversary simulation campaigns (red-team-specialist), disk forensics acquisition (digital-forensics-analyst), authorized pentest (penetration-tester), or binary RE lab work (reverse-engineer).
Run conversion rate optimization through hypothesis-driven testing including audit, hypothesis generation, test design, statistical analysis, and rollout decisions. Use this skill whenever the user wants to optimize conversion, run A/B tests, audit a funnel, generate test hypotheses, design experiments, or analyze test results. Triggers on conversion optimization, CRO, A/B test, split test, multivariate test, hypothesis, conversion funnel, funnel audit, experiment design, statistical significance, lift, optimization. Also triggers when the user has a conversion problem and isn't sure where to start, or when test results are ambiguous and need interpretation.
Guides management consulting-style work—engagement framing, hypothesis-driven problem structuring, issue trees, business cases, operating model and capability design, strategic options analysis, workshop facilitation, and executive recommendations (not legal advice). Use when diagnosing a business problem, structuring a strategy or transformation initiative, building a business case for leadership, designing target operating models, preparing steerCo or board recommendations, or advising on build-vs-buy and portfolio priorities—not for detailed requirements/BRDs (business-analyst), multi-team delivery tracking (technical-program-manager), contract negotiation (commercial-counsel), revenue accounting (senior-revenue-accountant), applied AI architecture (applied-ai-architect-commercial-enterprise), or system ADRs (senior-system-architecture). Canvas/TAM: business-model-researcher. Comms: communication-lead. M&A closing: transaction-manager. M&A principal/IC: transaction-principal.
Systematic debugging methodology — binary search isolation, hypothesis-driven debugging, reproducing issues, and root cause analysis. Use when debugging errors, unexpected behavior, or test failures.
Systematic debugging with hypothesis-driven investigation. Use when diagnosing bugs, errors, or unexpected behavior. Phases: Reproduce, Hypothesize, Investigate, Fix, Verify, Regression.
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.
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.