「assumptions」の検索結果

Open-ended scientific ideation partner. Use for research gaps, mechanism exploration, interdisciplinary connections, assumptions, possible research directions, and lightweight literature matrix or A+B paper-combination idea mapping. For structured testable hypotheses and validation plans, use hypothesis-generation instead.

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LQF Machine Learning Expert Guide - Routed skill for ML/Statistical Modeling with Critical Discussion Mode. Triggers on: machine learning, modeling, prediction, training, classification, regression, clustering, deep learning, neural network, model evaluation, feature engineering, hyperparameter tuning, overfitting, underfitting, baseline, ablation study, critique my approach, review my model, is this a good idea, should I use, what's wrong with, evaluate my solution, challenge my assumptions, discuss my approach Engages in critical discussion with minimum 3 rounds of iterative refinement. Challenges both user proposals and own suggestions with fact-based critique. Demands evidence and baselines before accepting solutions.

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Design experiments and quasi-experiments before analysis. Use when choosing study design, treatment/control structure, outcomes, assumptions, validation plans after scientific experiment failure, or which of DiD, ITS, synthetic control, or regression discontinuity fits the research question. For fitting models or estimating effects on existing data, use performing-causal-analysis instead.

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Extract report views, ratings, key assumptions and risks with sources and dates. Use when the user asks to research report breakdown with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.

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Migrate an Attio integration through a contract-led inventory, OpenAPI and documentation diff, shadow validation, canary rollout, reconciliation, and tested rollback. Use when endpoint assumptions, client versions, schemas, or integration architecture must change. Trigger with "upgrade Attio integration", "Attio migration", or "Attio API contract change".

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Design MVPs, validated learning experiments, and pivot-or-persevere decisions using Build-Measure-Learn. Use when the user mentions "MVP scope", "validated learning", "pivot or persevere", "vanity metrics", "test assumptions", "innovation accounting", "build-measure-learn", "minimum viable experiment", "should we pivot", "test a business idea cheaply", or "build the smallest version first". Also trigger when deciding what to include in a first version, measuring startup progress, or evaluating whether to change direction on a product bet. Covers innovation accounting and actionable metrics. For 5-day prototype testing, see design-sprint. For customer motivation analysis, see jobs-to-be-done.

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Flip core assumptions to reveal hidden constraints and alternative approaches - "what if the opposite were true?"

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Produces a cost or effort estimate someone can defend — decomposing the work, choosing between analogous, parametric and bottom-up methods, documenting the basis and its assumptions, expressing confidence as a range, and sizing contingency and management reserve separately. Use this to build an estimate, challenge one you have been handed, work out why estimates keep coming in low, or decide how much reserve a portfolio actually needs.

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Designs and operates the corporate network — segmentation, remote access, wireless, DNS and addressing, and diagnosing network problems. Use this to segment a network, set up or fix remote access, diagnose intermittent connectivity, plan addressing or DNS, or assess whether the network's trust assumptions still hold.

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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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Use lab budget forecaster for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.

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Use facs gating viz style for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.

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Use gene structure mapper for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.

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Use semantic consistency auditor for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries.

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Use study limitations drafter for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries.

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Use poster layout planner for other workflows that need structured execution, explicit assumptions, and clear output boundaries.

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Use rebuttal letter strategist for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries.

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Use figure reference checker for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries.

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Use medication adherence message gen for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries.

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Use market access value for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries.

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Use grant budget justification for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries.

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Use biotech-pitch-deck-narrative for academic writing workflows that need structured investor-facing storytelling, explicit assumptions, and clear output boundaries.

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Use medical cv resume builder for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries.

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Use conference poster pitch for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries.

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