Loading...
Loading...
Found 131 Skills
Dynamic, reflective problem-solving through structured sequential thoughts with support for branching, revision, and adaptive depth. Use this skill when: (1) Breaking down complex problems into steps, (2) Planning and design with room for revision, (3) Analysis that might need course correction, (4) Problems where the full scope is not clear initially, (5) Multi-step solutions requiring maintained context, (6) Situations where irrelevant information must be filtered out, (7) Any task benefiting from hypothesis generation, verification, and iterative refinement. Triggers: think through, step by step, break this down, sequential thinking, reason through, analyze step by step, think carefully, or when a problem clearly benefits from structured multi-step reasoning.
Use this skill when performing exploratory data analysis, statistical testing, data visualization, or building predictive models. Triggers on EDA, pandas, matplotlib, seaborn, hypothesis testing, A/B test analysis, correlation, regression, feature engineering, and any task requiring data analysis or statistical inference.
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.
Systematic debugging with hypothesis-driven investigation. Use when diagnosing bugs, errors, or unexpected behavior. Phases: Reproduce, Hypothesize, Investigate, Fix, Verify, Regression.
Interactive hypothesis-driven debugging with documented exploration, understanding evolution, and analysis-assisted correction.
Four-mantra debugging discipline — reproduce, trace the fail path, falsify the hypothesis, cross-reference every breadcrumb. Recite the mantra block verbatim at the start of any debugging session, then apply the four steps in order before proposing any fix. Trigger on /debug-mantra and proactively whenever debugging starts — user reports a bug, says something is broken/throwing/failing, asks to debug/diagnose/investigate an issue, or pastes a stack trace or error log.
Automated hypothesis generation and testing using large language models. Use this skill when generating scientific hypotheses from datasets, combining literature insights with empirical data, testing hypotheses against observational data, or conducting systematic hypothesis exploration for research discovery in domains like deception detection, AI content detection, mental health analysis, or other empirical research tasks.
Transform Claude Code into an AI Scientist that orchestrates research workflows using tree-based hypothesis exploration. Triggers on "research project", "scientific experiment", "run experiments", "AI scientist", "tree search experimentation", "systematic study".
Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.
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.
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.
When the user wants to design, prioritize, or analyze growth experiments -- including A/B tests, hypothesis frameworks, ICE/RICE scoring, or growth sprints. Also use when the user says "A/B test," "experiment design," "growth sprint," "experiment prioritization," or "statistical significance." For analytics setup, see product-analytics. For growth modeling, see growth-modeling.