「uncertainty」の検索結果

Search and read an existing repository OpenWiki when requested or needed to resolve a concrete architecture or dependency uncertainty, or initialize and update one through the resumable page-job lifecycle.

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Estimate market, segment, or opportunity size with transparent assumptions and uncertainty. Use for TAM/SAM/SOM, sizing scenarios, or comparing the scale of possible opportunities.

日本語の概要は準備中です。原文の説明を表示しています。

Search connected Notion pages and databases, verify the relevant sources, and produce concise answers that preserve links and uncertainty.

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Evaluate factual claims in AI-generated text and teach a lightweight verification habit. Use when a learner wants to fact-check an AI answer, identify uncertainty, choose appropriate independent sources, or practise critical AI literacy.

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Predicts ADMET properties using ADMETlab 3.0 API or DeepChem models. Estimates bioavailability, CYP inhibition, hERG liability, and 119 toxicity endpoints with uncertainty quantification. Filters for PAINS and other structural alerts. Use when filtering compounds for drug-likeness or prioritizing leads by predicted safety.

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Use when the user asks to "design an A/B test", "set up a creative/landing test", "run an incrementality test", or "is this result statistically and practically material?"; produces a hypothesis, variant matrix, sample-size/duration/power plan, and a documented effect/uncertainty read from own exported results. It applies only a precommitted owner-approved action rule; the statistical helper never chooses a business action. Not for producing variants — use ad-creative-builder; not for reading back one shipped change — use paid-measurement-loop. 广告AB测试设计/实验设计/显著性判定/增效测试

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user asks to "design an email A/B test", "set up a multivariate subject/CTA test", "run a send-time test", "build a hold-out group", or "is this email result statistically and practically material?"; produces a falsifiable hypothesis, one-variable-per-cell matrix, sample-size/MDE/duration/power plan, and an effect/uncertainty read from own ESP data. Applies only a precommitted owner-approved action rule; the helper never chooses a business action. Not for EQS/vetoes or writing the email. 邮件AB测试设计/多变量测试/发送时间测试/留出组/显著性判定

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Role profile — a reviewer who is fact-seeking, bedrock-seeking, honest, and rejecting. Demands that arguments rest on small facts that cannot be wrong (not on stacked assumptions dressed up as foundation), and that deliberately-unclear concepts be quarantined — never leaked into solid layers, never mixed with each other. Catches the failure mode where designs ship after partial grounding: most of the spec researched, the design feels finished, and the unresearched remainder holds the deal-breaker. Invoke with `/pedantic <target>` (e.g. `/pedantic review this design doc`, `/pedantic review the SDK design`, `/pedantic review this PR`). Probes cover logical structure (definitions, assumptions, boundaries, contradictions, unfalsifiability, vague quantifiers, counterexamples), epistemic honesty (researchable vs. discoverable-only-by-doing), bedrock integrity (assumed-bedrock, leaked uncertainty, mixed unclarity), and design pathology (YAGNI, wrong-layer abstraction, unclear responsibility, fragmentation, over-engineering). Use when you want a hard critique of a plan, design doc, technical doc, code change, or PR description — not a copy edit, not a vibe check.

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Applies decision science and cognitive bias research to marketing and product decisions — how people actually choose under uncertainty, and how framing, defaults, sequencing, and social context change behavior. Use this to diagnose why a well-argued offer is not converting, to structure choices and pricing presentation, to design an experience around how attention and memory work, or to pressure-test whether a persuasion tactic is legitimate or manipulative.

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Plans under genuine uncertainty — building scenarios, identifying which assumptions are load-bearing, setting early-warning indicators, and stress-testing a plan against futures rather than forecasting one. Use this when a decision depends on something unknowable, when a plan assumes conditions that may not hold, before a large irreversible commitment, or when a market, regulatory, or technology shift could invalidate the strategy.

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Answers a business question with data without fooling yourself — framing the question so an answer would change something, choosing the right comparison, checking the data before trusting it, recognizing the traps that produce confident wrong answers (aggregation reversals, survivorship, regression to the mean, multiple comparisons), and reporting uncertainty honestly. Use this to run an analysis, review one before acting on it, or work out why two people looking at the same data reached opposite conclusions.

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Owns where the business plays and how it wins over a multi-year horizon — portfolio choices, corporate development, strategic partnerships, and planning under uncertainty. Use this for a decision about which markets or businesses to be in, whether to build, buy, or partner, how to allocate capital across business lines, or when a long-horizon bet needs framing. Distinct from `chief-executive`, which arbitrates present-quarter conflicts.

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Under genuine uncertainty with no reliable forecast, inventory means, cap downside at affordable loss, act for commitments, and let goals emerge from controllable moves.

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When provisioning, setting a limit, or committing an estimate under uncertainty, size a buffer to residual error and the cost of breach—not to the optimistic edge.

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Select QA-approved canonical intent cells into a versioned AI-visibility tracking panel with partitions, variants, lanes, surfaces, locales, repetitions, separate exposure and priority weights, randomization, uncertainty, refresh rules, and campaign controls. Use after prompt QA or when revising an existing panel.

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Audits, preserves and recovers local Git state before cleanup: unpushed or wrong-branch commits, dirty worktrees, duplicate clones, stashes, dangling commits, squash-merge uncertainty. Use when the user fears lost work, wants to recover a commit, asks if a branch is actually merged, asks whether a branch, worktree or clone is safe to delete, or wants one clean main (误删分支 / 还有没有丢的东西). Not for GitHub PR operations.

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Write a PRD for an AI-powered feature, covering the things normal PRDs miss. Use when asked to spec an AI/LLM feature, write a PRD for a feature that uses a model, or plan an AI capability (assistant, summarizer, generator, classifier). Produces an AI feature PRD — problem & UX of uncertainty, model approach, eval criteria, guardrails, fallback behaviour, the data flywheel, and cost/latency budget.

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Use when requesting independent code review, after implementation slices, before merging high-risk work, or when verification exposes evidence, baseline, architecture, compatibility, or retirement uncertainty.

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Use when designing or auditing AISTATS experiments, simulations, baselines, statistical tests, uncertainty estimates, ablations, random seeds, hyperparameters, compute, dataset handling, and claim-to-evidence fit, with emphasis on experiments that validate theorems rather than chase leaderboards.

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Use when strengthening AISTATS reproducibility evidence, including the official reproducibility checklist, statistical assumptions, proofs, datasets, hyperparameters, random seeds, compute, uncertainty estimates, baselines, code/data release statements, and checklist-to-claim consistency audits.

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Use when positioning an AISTATS submission against AI, machine-learning, statistics, and uncertainty literature, including arXiv preprints, workshop versions, concurrent submissions, prior conference versions, PMLR archival status, and the two-community citation coverage that AISTATS reviewers expect.

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Use when writing the response to an Agricultural Systems (AgSy) revision decision. AgSy review is single anonymized with at least two expert systems reviewers and an editor decision, and revisions often turn on model evaluation, sensitivity/uncertainty, and trade-off framing. The response must convert each reviewer without alienating the editor. Structures the response letter; it does not fabricate new results.

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Use when evaluating the model and analyzing results for an Agricultural Systems (AgSy) manuscript so it survives expert systems review — independent model evaluation (observed vs. simulated, fit statistics), sensitivity and uncertainty analysis, and trade-off / scenario analysis across the system. Guides evaluation norms; it does not fabricate results or run the model.

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Use when building or revising exhibits for an AEJ: Economic Policy manuscript so they meet AEA house style and carry the policy message — no significance asterisks, a self-contained headline exhibit, and figures that show the policy effect with uncertainty. Designs exhibits; it does not run the estimation or write the surrounding prose.

日本語の概要は準備中です。原文の説明を表示しています。