「assumption」の検索結果

Use anki-card-creator for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries for study-card generation.

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Plans sample size estimation logic, power assumptions, feasibility checks, and fallback enrollment strategies for clinical and translational study protocols.

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Assesses whether a medical research topic is worth starting now by separating true novelty from pseudo-novelty, auditing real feasibility under stated resource constraints, and forcing a concrete start / narrow / redesign / stop decision. Always require explicit assumptions and never fabricate references, datasets, resource availability, precedent studies, or publication claims.

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。

Apply when writing stateful logic, or when code branches a lot or repeats a shape assumption across files.

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 ONLY when the user asks to check, validate, review or audit analysis code for scientific correctness, or when you are the reviewer agent running its checks. Never run it unprompted as part of ordinary analysis work. Covers physics, earth/geo, biology, chemistry, social science and bioprocess/fermentation. Runs a deterministic domain-correctness gate that catches code which runs but is scientifically wrong (unit/dimension mismatch, Euclidean distance on lat/lon without a CRS, 0-based/1-based coordinate and strand errors, impossible SMILES valence, uncorrected multiple comparisons, averaging a categorical code, unconstrained kinetic-parameter fits, kLa computed from raw DO instead of the driving force, arithmetic mean of raw CFU counts, ANOVA/Tukey with no assumption check, a curvature DOE design fit with a first-order model, an ML model fit across process scales with no correction). Surfaces structured findings; never claims the code is correct.

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。

Apply when writing stateful logic, or when code branches a lot or repeats a shape assumption across files.

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。

Interrogate the brief before a line is built — ask only the questions whose answers change the work, and put every unasked decision on the record as a stated assumption.

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Stress-test a finding against the choices you did not make. Enumerates the discrete forks a competent analyst could have taken (measure definition, sample filter, control set, clustering level, weighting, functional form), runs the specification grid, and reports the distribution rather than a point estimate — then attacks the identifying assumption with named, computable sensitivity statistics. Use when the user says "is this robust", "challenge this result", "specification curve", "multiverse", "how sensitive is this", "what if I'd used a different measure", "stress-test my estimate", or before a result becomes a headline claim. NOT a reviewer of prose or code — it challenges the CLAIM.

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Holistic pedagogical review of a lecture deck (`.qmd` or `.tex`). Checks narrative arc, prerequisite assumptions, worked examples, notation clarity, and deck-level pacing. Use when user says "pedagogy review", "does this teach well?", "is the flow right?", "will students follow?", "review the narrative", or before teaching a deck for the first time. Read-only; produces a report.

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Scaffold and run a reproducible Monte Carlo simulation study in R — a declared assumption regime, a parameterized DGP, an estimator grid, a seeded replication loop, and a summary of bias, RMSE, empirical SE, coverage, size/power with Monte Carlo standard errors. Use when the user says "run a Monte Carlo simulation", "simulation study", "check the bias/coverage of an estimator", "compare estimators in simulation", "size and power simulation", "Monte Carlo experiment", or wants to demonstrate an estimator's finite-sample properties. Produces a numbered R script in `scripts/R/` and saves per-replication raw results + a summary table to `output/`.

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When a request is vague, assumption-laden, or "obvious," ask the few load-bearing questions that expose hidden requirements before building or committing.

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When a claim, doc, test, metric, or assumption conflicts with observed behavior, stop theorizing from the map and verify the live code or data; let territory overrule.

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Challenge a confirmed task, decision, plan, or artifact through adaptive skeptical questions. Use when you need to expose assumptions before acting.

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Investigate and resolve Cloudflare configuration issues using API-driven evidence gathering. Use when troubleshooting ERR_TOO_MANY_REDIRECTS, SSL errors, DNS issues, or any Cloudflare-related problems. Focus on systematic investigation using Cloudflare API to examine actual configuration rather than making assumptions.

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Use when asked 做一份外贸报价单, FOB 和 CIF 怎么报价, 出口报价核算, 报价怎么算利润, 退税怎么算进报价, quotation sheet for an export order, or price an export quotation from China. Produces a cost build-up from the factory price through export tax rebate, domestic logistics, port charges, freight and insurance to each Incoterms 2020 term, a quotation sheet in English with a Chinese summary, quantity tiers, validity and exchange-rate assumptions, payment terms and the risk each term puts on the seller, with rates marked to confirm.

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Compare the total cost of car ownership across buy-new, buy-used, lease, and keep-your-current-car — depreciation, insurance, maintenance ramp, and fuel over a real horizon, not just the monthly payment. Use when asked should I lease or buy a car, is it cheaper to keep my old car, what does this car really cost per month, or new vs used total cost. Produces the ranked scenario totals, per-month true cost, the assumption ledger, and the not-modeled list.

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Help users de-risk new product bets by testing core assumptions, building low-fidelity prototypes, and gathering high-signal evidence before investing heavy engineering resources.

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Help users build a sustainable habit of regular customer interaction to ensure product development is driven by real-world needs rather than internal assumptions.

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。

Behavioral guardrails for Codex coding work based on common user complaints. Use when Codex is asked to implement, modify, debug, review, test, or operate on a codebase and should avoid unsafe scope expansion, stale edits, fake completion, brittle edits, shallow debugging, superficial patch-on fixes, one-off special-case code, short-term design choices that hurt maintainability, blindly following incorrect user assumptions, poor dependency choices, generic product or UI output, over-mocked tests, noisy approvals, verbose status reports, or leaking internal reasoning into project artifacts or user-facing UI.

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Use when revising an AISTATS paper for concise AI-statistics framing, theorem-and-experiment clarity, 8-page two-column compression, double-blind wording, reproducibility clarity, statistically careful claims, and assumption-labeling discipline that survives statistician reviewers.

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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 the question is what makes the result tight or what the data identify for an American Economic Journal: Microeconomics (AEJ: Micro) manuscript — covering both (a) structural/empirical-IO and experimental identification and (b) for pure theory, which assumptions drive the result and how robust the mechanism is. Stress-tests credibility; it does not build the model (see aejmic-theory-model).

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Use when extensions, edge cases, or applied robustness checks are missing for an American Economic Journal: Microeconomics (AEJ: Micro) manuscript — covering theory extensions (relaxed assumptions, alternative concepts, perturbations) and applied/experimental robustness. Decides which extensions earn their place; it does not prove the main result (see aejmic-theory-model).

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Use when an AEJ: Economic Policy manuscript needs a framework that maps reduced-form estimates into a welfare, cost-benefit, or distributional policy object — sufficient statistics, MVPF, optimal-policy, or a small applied model. Builds the estimate-to-welfare bridge and states its assumptions; it does not run the empirical estimation or write the 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。