Results for “hypotheses”

Transform threat intelligence and attack patterns into testable hunting hypotheses for proactive threat detection.

Design rigorous A/B tests with clear hypotheses, controlled variants, appropriate metrics, and sample size calculations.

Designs an A/B test or experiment with clear hypothesis, variants, success metrics, sample size, and duration. Use when planning experiments to validate product changes or test hypotheses.

Build reproducible ML experiment plans with hypotheses, metrics, and ablations. Use when: (1) planning experiments, (2) comparing variants, (3) defining acceptance thresholds. NOT for: long-running experiment execution.

Evaluate an AI product idea across outcomes, hypotheses, risks, and positioning to decide whether it deserves investment or recommendation.

Design surveys that collect reliable, unbiased quantitative data to validate hypotheses and measure user attitudes at scale.

Generate a Lean Canvas with sections for problem, solution, metrics, cost structure, UVP, unfair advantage, channels, segments, and revenue for testing business hypotheses.

Formulate testable hypotheses from observations, design experiments, and generate predictions using a structured scientific method framework.

Provides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework. Use when conducting causal tracing, activation patching, interchange intervention training, or testing causal hypotheses about model behavior.

Audits and optimizes conversion points across the funnel, applying behavioral science to produce prioritized, testable hypotheses.

Provides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework. Use when conducting causal tracing, activation patching, interchange intervention training, or testing causal hypotheses about model behavior.

Interrogates research plans before evals or published claims, forcing explicit null hypotheses, confound checks, and data verification across four review waves.

Cross-runtime 2-stage pipeline for Claude Code, Codex/OMX, and Gemini/Antigravity/OMA: trace causal hypotheses, inject evidence into deep-interview style requirements crystallization, then hand off to the right runtime planner/executor.

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Design, prioritize, and evaluate product experiments with clear hypotheses and defensible decisions, including A/B testing, sample size estimation, and statistical interpretation.

Generate falsifiable trade strategy hypotheses from market data, trade logs, and journal snippets, with ranked hypothesis cards, experiment designs, kill criteria, and optional strategy.yaml export.

Defines a testable hypothesis with clear success metrics and validation approach. Use when forming assumptions to test, designing experiments, or aligning team on what success looks like.

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Guide cross-functional teams through creating Jeff Gothelf's Lean UX Canvas v2 to frame business problems, surface assumptions, and define experiments.

Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques for 1.5-3.6× speedup without quality loss.

Runs a hypothesis-driven debugging workflow that reproduces failures, forms discriminating tests, proves root cause via toggle, and delivers a fix, regression test, and postmortem.

research-synthesis

Automates hypothesis generation and testing on tabular datasets using LLMs, combining data-driven discovery with literature integration for scientific research.

Multi-phase dialectical stress-test for HIGH-STAKES decisions only (architecture choices, irreversible product calls, strategic bets). Heavy-cost skill — do NOT use for routine questions, brainstorming, or simple tradeoffs. User must explicitly invoke or describe a decision they call "high-stakes", "irreversible", or "needs stress-testing". Triggers: "stress test this decision", "dialectic on", "challenge this thesis", "should I really".

First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English.

Apply lean thinking to UX: hypothesis-driven design, collaborative sketching, and rapid experiments instead of heavy deliverables. Use when the user mentions "Lean UX", "design hypothesis", "UX experiment", "collaborative design", "outcome over output", "design studio method", "assumption mapping", or "lightweight research". Also trigger when reducing design documentation overhead, getting cross-functional teams to co-design, or running fast usability experiments. Covers hypothesis statements, MVPs for UX, and cross-functional collaboration. For Build-Measure-Learn, see lean-startup. For usability audits, see ux-heuristics.

Audits hidden assumptions in decisions by classifying them into four types, ranking by fragility and impact, and rebuilding conclusions from verified premises. Auto-detects Chinese or English.

Identify risky assumptions for a new product idea across 8 risk categories including go-to-market, strategy, and team.

First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English.

Surface every assumption embedded in a plan, strategy, or document, assess how critical and how validated each one is, and identify which ones to test first. Load when the user asks to map assumptions, surface hidden beliefs, find what must be true for this to work, run an assumption audit, or when deep-thinking diagnoses an assumption frame. Also triggers on "what are we assuming", "what must be true for this to work", or "find the untested beliefs". Based on David Bland and Alex Osterwalder's assumption mapping method from Testing Business Ideas.