'hypothesis' 검색 결과

Investigate errors, crashes, and unexpected behavior by reproducing failures, tracing execution, and comparing logs. Tests one explanation at a time, then provides a verified fix, supporting evidence, and tests or safeguards to prevent recurrence.

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• Investigate error messages and execution traces
• Find the cause of an application crash
• Compare logs to locate a failure

Create distinct paid advertising concepts, headlines, copy, and production briefs from validated brand information and campaign goals. Each concept connects its message to evidence, audience needs, platform requirements, and a hypothesis to test.

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• Develop campaign concepts with distinct strategic angles
• Draft ad headlines and descriptions
• Prepare creative briefs and production plans

Design and evaluate paid-ad experiments with clear hypotheses, comparison groups, sample requirements, and stopping rules. Check data quality before estimating effects, then deliver structured results and a plain-language decision that reflects uncertainty and the population tested.

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• Planning an A/B test for paid advertising
• Estimating sample requirements and test duration
• Setting success thresholds and stopping rules in advance

Investigate bugs, failing tests, and unexpected behavior through reproducible steps, logs, and comparisons with working code. Follow four stages to identify the root cause, test a hypothesis, apply a focused fix, and verify the result.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating failing tests
• Tracing production bugs to their source
• Finding where a build or integration breaks

Investigate bugs, failed tests, and unexpected behavior through reproducible steps, evidence gathering, and comparison with working code. Test one hypothesis at a time, then implement a focused fix and verify it with tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating failed tests or builds
• Tracing production bugs across components
• Reassessing a problem after repeated failed fixes

Hunt for signs of compromise in web and application logs without an initial alert. Establish normal activity, record investigative hypotheses, check source code, and reproduce issues locally before producing incident records and a report.

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• Hunting for compromise without an initial alert
• Tracing related activity across application logs
• Distinguishing attack attempts from successful exploitation

Turn business goals and customer problems into a product roadmap. Define initiatives, rank priorities, check dependencies and team capacity, and organize releases with clear reasons stakeholders can review and discuss.

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• Planning a quarterly or six-month product roadmap
• Prioritizing competing initiatives across teams
• Explaining roadmap choices to executives

Build an initial customer profile from existing research, market data, and team knowledge. Organize behaviors, frustrations, goals, and buying influences into a shared working hypothesis, with assumptions clearly marked for later validation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating a working customer profile before research
• Aligning the team's assumptions about users
• Identifying assumptions to test in interviews

Evaluate an AI product or feature idea by connecting customer problems, business outcomes, testable assumptions, risks, and differentiation. Produce a structured recommendation with lightweight experiments, success measures, and next steps for an investment decision.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deciding whether to invest in an AI feature
• Comparing AI solution options by outcomes and risk
• Preparing a proposal for executive or budget approval

Use three adaptive questions to identify where product growth is stuck. The skill recommends a path through new customer segments, geographies, distribution channels, or products, then suggests a low-cost first experiment and a common trap to watch for.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Identify where product growth is getting stuck
• Choose between new customer segments and new regions
• Compare distribution expansion with a new product

Work through eight canvas sections to clarify a business problem, target users, desired outcomes, and possible solutions. Turn assumptions into testable hypotheses and choose the smallest experiment to investigate the most important uncertainty before development.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Framing the problem behind a new product idea
• Aligning a cross-functional team on assumptions
• Choosing which hypothesis to test first

Investigate bugs and failing tests through four stages: finding the root cause, comparing working patterns, testing a hypothesis, and implementing a fix. Uses reproduction steps, diagnostic evidence, and regression tests to check the result.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Tracing the cause of a failing test
• Following runtime errors back to invalid data
• Finding the change that broke working code

Review technical claims and deliverables by checking evidence and testing alternative explanations. Confirm that references and API endpoints exist, then report verified, unverified, or contradicted claims alongside evidence and an overall review verdict.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check claims in technical documents
• Verify URLs, package names, and API endpoints
• Review another agent's deliverables

Analyze A/B test results to assess whether observed differences are likely due to chance. Check test duration and sample size, compare audience segments, distinguish statistical findings from practical value, and generate hypotheses for follow-up tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Evaluating results from a headline A/B test
• Estimating sample sizes before starting a test
• Comparing results by device or traffic source

Develop a research idea through a structured conversation, then turn the answers into a written research specification. Clarify the question, testable hypothesis, data needs, methods for assessing cause and effect, expected findings, and feasibility.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning an early research idea into a concrete plan
• Exploring a new research direction from scratch
• Identifying data needs and threats to causal interpretation

Coordinate research from question development through literature searches, analysis, and publication with specialized agents. Supports quantitative, qualitative, and mixed methods studies, plus systematic reviews, with human approval at key decisions about research direction and methods.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Refining research questions and choosing a theoretical framework
• Planning a systematic review and approving screening criteria
• Designing a qualitative study using interviews

Helps build a research framework by comparing theories, mapping relationships between variables, and deriving testable hypotheses. Explores less conventional options, examines weaknesses and competing explanations, and produces diagrams with anticipated reviewer objections.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing theories for a research question
• Deriving hypotheses from relationships between variables
• Examining weaknesses and anticipated reviewer objections

Coordinate AI research from literature review to paper drafting. The skill alternates focused experiments with broader reviews of findings, tracks hypotheses and decisions, and uses specialist skills to carry out research tasks and prepare progress reports.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Starting a research project from a question
• Testing and comparing multiple hypotheses
• Reassessing direction when experiments stall

Test one risky assumption through a prototype with an agreed deadline, hypothesis, and success signal. Record observations and the source revision, then return a keep, discard, or inconclusive verdict with the status of each artifact.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Testing one uncertain assumption before implementation
• Setting a deadline and success signal for a prototype
• Deciding whether to keep or discard prototype results

Guided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or survey data, check statistical assumptions, compute required sample sizes, or write up results - even if they never name a specific test. Covers t-tests, ANOVA, chi-square, correlation, regression, non-parametric and Bayesian methods. For low-level model APIs, see the statsmodels and pymc skills.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Creative 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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Hypothesis-driven, tool-grounded security review of coverage gaps

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Orchestrates multi-agent forensic investigations on public GitHub repositories, coordinating parallel evidence collection, hypothesis formation, verification, and report generation.

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Runs a hypothesis-driven debugging loop across any language or binary, escalating to orthogonal oracle angles and locking the fix with a failing test. Use for crashes, silent failures, hangs, wrong responses, memory leaks, async misbehavior, or reverse engineering.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.