statsmodels
davila7Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.
hypogenic
davila7Automated 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.
statistical-analysis
davila7Statistical analysis toolkit. Hypothesis tests (t-test, ANOVA, chi-square), regression, correlation, Bayesian stats, power analysis, assumption checks, APA reporting, for academic research.
ab-test-setup
davila7When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," or "hypothesis." For tracking implementation, see analytics-tracking.
hypothesis-generation
davila7Generate testable hypotheses. Formulate from observations, design experiments, explore competing explanations, develop predictions, propose mechanisms, for scientific inquiry across domains.
ab-test-setup
sickn33Structured guide for setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.
hypogenic
K-Dense-AIAutomated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.
scientific-brainstorming
K-Dense-AICreative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.
hypothesis-generation
K-Dense-AIStructured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.
ab-test-setup
coreyhaines31When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," or "hypothesis." For tracking implementation, see analytics-tracking.
statistical-analysis
anthropicsApply statistical methods including descriptive stats, trend analysis, outlier detection, and hypothesis testing. Use when analyzing distributions, testing for significance, detecting anomalies, computing correlations, or interpreting statistical results.
hunt-focus-definition
OTRFDefine a focused hunt hypothesis by synthesizing completed system internals and adversary tradecraft research. Use this skill after research has been completed to narrow a high-level hunt topic into a single, concrete attack pattern with clear investigative intent. This skill produces a structured, testable hypothesis and should be used before selecting data sources, defining environment scope, or developing analytics.
hunt-data-source-identification
OTRFIdentify relevant security data sources that could capture the behavior defined in a structured hunt hypothesis. Use this skill after the hunt focus has been defined to translate investigative intent into candidate telemetry sources using existing platform catalogs. This skill supports hunt planning by reasoning over available schemas and metadata before analytics development or query execution.
hunt-research-system-and-tradecraft
OTRFResearch system internals and adversary tradecraft to ground a threat hunt in real system behavior and realistic abuse patterns. Use this skill at the start of hunt planning, when you are given a high-level hunt topic but lack a clear understanding of how the system normally operates or how adversaries are known to abuse it. This skill informs early hunt direction by producing candidate abuse patterns, key assumptions, and cited sources, and should be used before defining a concrete hunt hypothesis or selecting data sources.
fix-bug
JamieMasonDebug and fix bugs in Syncpack using scientific debugging methodology. Use when a test is failing, unexpected behaviour occurs, or investigating issues. Covers hypothesis-driven debugging and TDD-based fixes.
debug-like-expert
glittercowboyDeep 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.
debug
udecodeUse when encountering any bug, test failure, or unexpected behavior, before proposing fixes - four-phase framework (root cause investigation, pattern analysis, hypothesis testing, implementation) that ensures understanding before attempting solutions
data-stats-analysis
StarlitnightlyPerform statistical tests, hypothesis testing, correlation analysis, and multiple testing corrections using scipy and statsmodels. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).
pol-probe-advisor
deanpetersSelect the right Proof of Life (PoL) probe based on hypothesis, risk, and resources. Use this to match the validation method to the real learning goal, not tooling comfort.
proto-persona
deanpetersCreate an initial, assumption-based persona profile that synthesizes available user research, market data, and stakeholder knowledge into a working hypothesis about your target user. Use this to align
Epic Hypothesis
deanpetersFrame epics as testable hypotheses using an if/then structure that articulates the action or solution, the target beneficiary, the expected outcome, and how you'll validate success. Use this to manage
discovery-process
deanpetersGuide product managers through a complete discovery cycle—from initial problem hypothesis to validated solution—by orchestrating problem framing, customer interviews, synthesis, and experimentation.
fpf:query
NeoLabHQSearch the FPF knowledge base and display hypothesis details with assurance information
fpf:propose-hypotheses
NeoLabHQExecute complete FPF cycle from hypothesis generation to decision
Hypothesis Testing
rohitg00Scientific approach to debugging with hypothesis formation and testing
hypothesis-generation
jimmc414Generate testable hypotheses. Formulate from observations, design experiments, explore competing explanations, develop predictions, propose mechanisms, for scientific inquiry across domains.
hypothesis-generator
a5c-aiAutomated hypothesis generation using abductive reasoning and knowledge graph traversal
quantitative-methods
a5c-aiDesign and execute statistical analyses including regression modeling, hypothesis testing, power analysis, and robustness checks using R, Stata, SPSS, or Python
hypothesis-tracker
a5c-aiHypothesis management skill for tracking business hypotheses through testing and validation
ab-test-setup
aitytechWhen the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," or "hypothesis." For tracking implementation, see analytics-tracking.
observability-first-debugging
NTCodingSystematic debugging methodology that eliminates guessing and speculation. Add instrumentation to gather specific data that fully explains the problem. Evidence before hypothesis. Observation before solution. Triggers on: debugging, error investigation, 'why is this failing', unexpected behavior, test failures, non-zero exit codes, stack traces.
systematic-debugging
MadAppGangUse when debugging failures, errors, or unexpected behavior. Covers root cause investigation, data flow tracing, hypothesis-driven debugging, and fix verification to prevent trial-and-error approaches.
debug-loop
proffesor-for-testingHypothesis-driven autonomous debugging with real command validation
debugging
elianivaUse when encountering any bug, test failure, or unexpected behavior, before proposing fixes - four-phase framework (root cause investigation, pattern analysis, hypothesis testing, implementation) that ensures understanding before attempting solutions
research-ideation
meleantonioGenerate research questions from economic phenomena
neovim-debugging
aiskillstoreDebug Neovim/LazyVim configuration issues. Use when: user reports Neovim errors, keymaps not working, plugins failing, or config problems. Provides systematic diagnosis through hypothesis testing, not just checklists. Think like a detective narrowing down possibilities.
hypothesis-tree
flpbaladaStructure complex questions into testable hypotheses. Use when validating product ideas, debugging problems, planning experiments, or breaking down ambiguous challenges into actionable research.
systematic-debugging
panaversitySystematic methodology for debugging bugs, test failures, and unexpected behavior. Use when encountering any technical issue before proposing fixes. Covers root cause investigation, pattern analysis, hypothesis testing, and fix implementation. Use ESPECIALLY when under time pressure, just one quick fix seems obvious, or you've already tried multiple fixes. NOT for exploratory code reading.
ai-co-scientist
sundial-orgTransform 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".
systematic-debugging
ed3daiUse when encountering any bug, test failure, or unexpected behavior, before proposing fixes - four-phase framework (root cause investigation, pattern analysis, hypothesis testing, implementation) that ensures understanding before attempting solutions
ring:systematic-debugging
LerianStudioFour-phase debugging framework - root cause investigation, pattern analysis, hypothesis testing, implementation. Ensures understanding before attempting fixes.
sequential-thinking
irahardiantoPerforms dynamic, reflective problem-solving through iterative thought chains. Use for complex planning requiring revision, branching, backtracking, or hypothesis verification. Ideal for multi-step analysis where context maintenance is required or the full scope isn't initially clear.
cc-debugging
ryanthedevGuide systematic debugging using scientific method: STABILIZE -> HYPOTHESIZE -> EXPERIMENT -> FIX -> TEST -> SEARCH. Two modes: CHECKER audits debugging approach (outputs status table with violations/warnings), APPLIER guides when stuck (outputs stabilization strategy, hypothesis formation, fix verification). Use when encountering ANY bug, error, test failure, crash, wrong output, flaky behavior, race condition, regression, timeout, hang, or code behavior differing from intent. Triggers on: debug, fix, broken, failing, investigate, figure out why, not working, it doesn't work, something's wrong.
hypothesis-library
gtmagentsCurated repository of experiment hypotheses, assumptions, and historical learnings.
property-based-testing
aj-geddesDesign property-based tests that verify code properties hold for all inputs using automatic test case generation. Use for property-based, QuickCheck, hypothesis testing, generative testing, and invariant verification.
Exploratory Data Analysis
aj-geddesDiscover patterns, distributions, and relationships in data through visualization, summary statistics, and hypothesis generation for exploratory data analysis, data profiling, and initial insights
Statistical Hypothesis Testing
aj-geddesConduct statistical tests including t-tests, chi-square, ANOVA, and p-value analysis for statistical significance, hypothesis validation, and A/B testing
hunt-threat
dandyeConduct proactive, hypothesis-driven threat hunting. Use when performing advanced hunting based on threat intelligence, TTPs, or anomalies. For Tier 3 analysts or dedicated threat hunters. Supports iterative search, pivoting, and comprehensive documentation.