320 skills · sorted by relevance
Build a systematic threat hunt hypothesis framework that transforms threat intelligence, attack patterns, and environmental data into testable hunting hypotheses.
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Build a systematic threat hunt hypothesis framework that transforms threat intelligence, attack patterns, and environmental data into testable hunting hypotheses.
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Prepare a research grant presentation that organizes the hypothesis, approach, milestones, and risks for reviewers. Its ivory background and black typography create a formal, text-focused HTML deck, also suited to research summaries and detailed reports.
Explore gene, drug, disease, and phenotype associations in a downloaded PrimeKG CSV. Retrieve direct relationships, disease summaries, and paths through one intermediate node while preserving relationship types and data provenance for reproducible research and hypothesis generation.
Plan an A/B test to compare product changes, with a clear hypothesis, variants, sample estimates, and tracking checks. Define success metrics and stopping rules before launch, then document results and decisions.
Turn threat intelligence and detection gaps into testable investigation hypotheses. Use endpoint monitoring and security log queries to examine evidence, validate findings, connect related activity, and document risks and recommended actions in a consistent hunt report.
Looks up a person's identity, role, and relationship before asking the user for clarification. Searches stored knowledge and connected records, then the web, updates incomplete pages, and asks a specific question with a hypothesis when evidence remains inconclusive.
Plan A/B tests that compare page variations, from headlines to pricing layouts. Define a hypothesis, choose success metrics, calculate sample size and duration, and prepare launch checks and results reports to support decisions.
Coordinate AI research from literature review and hypothesis testing to findings and paper drafts. The skill alternates focused experiments with broader reflection, maintains project records, and directs specialized skills to carry out research tasks.
Investigate bugs through symptom gathering and hypothesis testing, with saved investigation state across context resets. Resume earlier sessions, check their status, or request a root cause report without applying a fix.
Investigate production incidents and bugs using logs, metrics, and code evidence. The skill guides hypothesis testing, root cause analysis, fix validation, and reusable investigation notes, with access to configured observability tools such as Axiom.
Plan and document a small, temporary experiment to test one risky product assumption before development. Choose a suitable prototype approach and define participants, pass and fail criteria, timing, ownership, and a disposal plan.
Guide a product team from a problem hypothesis through customer interviews, research synthesis, and solution experiments. Use six structured phases to gather evidence, compare options, and decide whether to build, change direction, or stop.
Interpret statistical models in R and Python using predictions, comparisons, and marginal effects, which describe how outcomes change with predictors. Get guidance on choosing quantities, evaluating scenarios, summarizing groups, measuring uncertainty, and testing hypotheses.
实践认识论：把方案、假说或判断放回实践中检验，按「实践 → 认识 → 再实践」螺旋迭代，并在开始前写明循环终止条件。当你提出了假说要验证、一次尝试失败要复盘再来、发现自己只想不做或只做不想时触发；一次性输出、答案可直接查阅、还没形成假说（先调查）时不触发。 English: Practice-cognition loop. Test a hypothesis or plan in practice, learn from the result, and iterate in a rising spiral with an explicit stop condition. Trigger when a plan needs validation, an attempt failed and must be retried, or you notice thinking without doing (or doing without thinking); skip for one-shot outputs, lookup questions, or before any hypothesis exists.
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Property-based testing with fast-check (TypeScript/JavaScript) and Hypothesis (Python). Generate test cases automatically, find edge cases, and test mathematical properties. Use when user mentions property-based testing, fast-check, Hypothesis, generating test data, QuickCheck-style testing, or finding edge cases automatically.
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Generate falsifiable trade strategy hypotheses from market data, trade logs, and journal snippets. Use when you have a structured input bundle and want ranked hypothesis cards with experiment designs, kill criteria, and optional strategy.yaml export compatible with edge-finder-candidate/v1.
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Create or assess an evidence-grounded, falsifiable outcome hypothesis: a testable prediction of what measurable business result will change, for whom, by when, and how leading and lagging indicators will prove or disprove it. Use when framing measurable outcomes, turning an MVP, POC, feature, or technical initiative into a beneficiary result, defining targets and indicators, or judging whether evidence is strong enough to invest. Also applies to business outcome hypotheses, value hypotheses, and outcome statements.
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A discipline for running paid media that does not light money on fire. Hypothesis writing for paid spend, channel selection, budget allocation, audience targeting, bid strategy, campaign types, what NOT to spend on, attribution reality, and the failure modes that produce expensive lessons. Triggers on paid media strategy, ad budget allocation, channel selection, paid media plan, audit my Google Ads, audit my Meta Ads, scale paid media, kill underperforming campaign, paid media hypothesis, ad spend strategy, attribution reality, performance marketing strategy. Also triggers when a team is asking how to scale paid media, or whether to add a new channel, or how to reallocate spend across channels.
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Systematic scientific hypothesis formulation and evaluation
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Design and validate bounded product experiments with falsifiable hypotheses, precommitted thresholds, guardrail metrics, ethical eligibility, and stop rules; use for product hypothesis validation / product experiment design. (triggers: `product hypothesis validation`, `product experiment design`)
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Evidence-before-action diagnosis of failing ML experiments. Probes the system before guessing causes, process list, dmesg, GPU stats, log scrollback, checkpoint state, then states a hypothesis as a hypothesis and runs a smoke before claiming a root cause. Use when the user asks why a run is failing, diverging, OOMing, hanging, slow, producing weird metrics, has crashed, or asks to debug, diagnose, troubleshoot, or investigate a training issue.
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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.
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Build AI agents that automate scientific research — hypothesis generation, experiment design, data analysis, and paper writing using agentic tree search. Use when: automating research workflows, generating and testing hypotheses, building AI-powered research assistants.
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