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Found 126 Skills
Deep analysis debugging mode for complex issues. Activates methodical investigation protocol with evidence gathering, hypothesis testing, and rigorous verification. Use when standard troubleshooting fails or when issues require systematic root cause analysis.
Use when the user needs ML pipelines, statistical analysis, data preprocessing, feature engineering, model selection, experiment tracking, or data visualization. Triggers: dataset exploration, model training, feature engineering, hyperparameter tuning, experiment tracking setup, statistical hypothesis testing, visualization creation.
A/B test design and experiment planning for paid advertising. Structured hypothesis framework, statistical significance calculator, test duration estimator, sample size calculator, and platform-specific experiment setup guides (Meta Experiments, Google Experiments, LinkedIn A/B). Use when user says A/B test, split test, experiment design, test hypothesis, statistical significance, sample size, or test duration.
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).
Autonomous NeMo-RL research agent workflow for directed hypothesis testing and open-ended discovery. Guides agents through the full experiment lifecycle: understanding recipes and environments, wiring RL or NeMo-gym runs, launching reproducible baselines and iterations, analyzing results, preserving human oversight, and using git plus TSV logs as the research ledger. Do NOT use for: bug fixes, code review, documentation, refactoring, dependency updates, or single-file changes.
Design and generate property-based tests (PBT) for changed files in the current git branch. Extracts specifications, designs properties (invariants, round-trip, idempotence, metamorphic, monotonicity, reference model), builds generator strategies, implements tests, and self-scores against a rubric (24/30+ required). Supports fast-check (TS/JS), hypothesis (Python), and proptest (Rust). Use when: (1) "write property tests for my changes", (2) "add PBT", (3) "property-based test", (4) after implementing pure functions, validators, parsers, or formatters to verify invariants.
factory_boy test data generation specialist. Covers Factory, DjangoModelFactory, SQLAlchemyModelFactory, all field declarations (Faker, LazyAttribute, Sequence, SubFactory, RelatedFactory, post_generation, Trait, Maybe, Dict, List), batch creation, pytest integration, and Celery task testing patterns. USE WHEN: user mentions "factory_boy", "test factory", "DjangoModelFactory", "SQLAlchemyModelFactory", asks about "test data generation", "factory traits", "SubFactory", "factory fixtures". DO NOT USE FOR: pytest internals - use `pytest`; Django setup - use `pytest-django`; Hypothesis property testing - use `pytest` with Hypothesis
Apply statistical methods to financial data including descriptive statistics, covariance estimation, regression, hypothesis testing, and resampling. Use when the user asks about return distributions, correlation between assets, building a covariance matrix, running a CAPM regression, testing whether alpha is significant, checking if returns are normal, or estimating confidence intervals. Also trigger when users mention 'volatility', 'how correlated are these', 'fat tails', 'skewness', 'R-squared', 'beta of a fund', 'bootstrap a Sharpe ratio', 'shrinkage estimator', 'Ledoit-Wolf', or ask why their optimizer produces unstable weights.
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.
Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.
Assumption mapping and product hypothesis testing frameworks for validating product ideas.