「bias」の検索結果

Design surveys that collect reliable, unbiased quantitative data to validate hypotheses and measure user attitudes at scale.

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。

Mid-conversation reflection skill that pauses execution and zooms out from detail-mode to honestly reassess direction, assumptions, and bias. Use when the user says 'reflect', 'take a step back', 'step back', 'zoom out', 'are we missing something', 'bigger picture', 'sanity check this', 'are we on track', 'are we overthinking this', 'forest for the trees', or any variation signaling intent to break out of detail-mode and reassess. Also trigger when the conversation has gone deep on implementation details without strategic check-in, or when the user shows signs of being stuck — that's often a signal the framing needs a reset, not more detail work. Intentionally low-intake: runs the 5-dimension analysis immediately when prior context is rich enough; asks one forcing clarifier only when invocation context is too thin to reassess from.

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Analyze liquid markets with a strict ICT/SMC framework that combines multi-timeframe bias, liquidity sweeps, displacement, close-confirmed MSS/BOS, FVG/OB/Breaker/OTE retracements, premium-discount location, session timing, invalidation, targets, and risk-to-reward. Use for ICT chart reviews, live-market analysis, OHLCV scans, or validating a user's trade thesis. Return an auditable setup status and conditional plan; do not use for generic indicator analysis, order execution, or claims of proven institutional intent.

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Use this skill when reasoning about generative AI, adversarial machine learning, neural network security, algorithmic fairness, or deep learning fundamentals. This skill channels the thinking of Ian Goodfellow, inventor of Generative Adversarial Networks (GANs). Trigger this skill when the user asks about model robustness, mitigating bias, evaluating AI guardrails, designing generative models, or defending against adversarial attacks. Apply his frameworks of minimax games, adversarial feature learning, and worst-case robustness analysis to shift the user's perspective from average-case optimization to adversarial resilience.

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Senior UI/UX Engineer. Architect digital interfaces overriding default LLM biases. Enforces metric-based rules, strict component architecture, hardware-accelerated CSS, and balanced design engineering. By Leon Lin & blueemi. Multi-runtime improved by shokunin.

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Write blogs, newsletters, Twitter threads, case studies, and marketing copy that convert. Covers copywriting frameworks (AIDA, PAS, BAB, FAB), headline formulas, SEO/GEO for 2026 (AI Overviews, SGE), newsletter deliverability, Twitter thread hooks, Cialdini psychology, and cognitive biases.

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。

Generate clean, playful low-density isometric voxel icon illustrations and explicitly requested four-frame physically credible loops built from large opaque face-lit acrylic blocks on an exact

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Search with feature attributions — return WHY each match scored where it did. Use when debugging recall quality, auditing for bias, or explaining results to a user.

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。

Design a media deconstruction protocol analysing persuasion, bias, or representation in media texts. Use when teaching students to critically read advertisements, news, or social media content.

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。

Slow down interpretation from observation to action. Use when students or adults need to examine assumptions in conflict, dialogue, or inquiry.

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。

Design a critical thinking task targeting specific skills like evaluating evidence, identifying bias, or analysing arguments. Use when embedding critical analysis into subject lessons.

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。

When the user wants to apply psychological principles, mental models, or behavioral science to marketing. Also use when the user mentions 'psychology,' 'mental models,' 'cognitive bias,' 'persuasion,' 'behavioral science,' 'why people buy,' 'decision-making,' 'consumer behavior,' 'anchoring,' 'social proof,' 'scarcity,' 'loss aversion,' 'framing,' or 'nudge.' Use this whenever someone wants to understand or leverage how people think and make decisions in a marketing context. For applying psychology to specific pages, see cro; for pricing tactics, see pricing; for copy framing, see copywriting.

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。

Audit an implementation against its plan with a hostile peer agent, debate findings to consensus, and drive fix waves until the auditor runs dry. Use when a done-claim needs verification the implementer cannot game ("hostile review", "adversarial audit", "is this PR faithful", "find the escape hatches") — the loop that beats the laziness bias with its own game theory.

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Expert guide for AI Safety, Governance, and Responsible AI in production — Constitutional AI enforcement, runtime guardrails (NeMo Guardrails 2.0, Llama Guard 3), bias auditing, hallucination detection, EU AI Act compliance, model cards, and content provenance / Panduan ahli untuk Keamanan AI, Tata Kelola, dan AI Bertanggung Jawab di produksi.

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Methods for assessing what people actually eat — 24-hour recall, food frequency questionnaires, diet diaries, biomarkers, duplicate-plate studies, and controlled feeding. Covers the relative strengths and biases of each instrument, how to choose among them for a given question, and how to interpret results in the presence of measurement error. Use when a user wants to estimate population-level intake, individual-level intake, or evaluate the plausibility of a dietary claim that depends on how intake was measured.

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Primary and secondary source analysis for historical reasoning. Covers source classification, sourcing (author/context/purpose), corroboration across multiple sources, contextualization within time and place, and bias detection. Use when evaluating historical evidence, assessing source reliability, or constructing evidence-based historical arguments.

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Systematic departures from rational choice theory and their implications for economic analysis and policy. Covers cognitive heuristics (anchoring, availability, representativeness), biases (loss aversion, status quo, overconfidence), prospect theory (reference dependence, probability weighting, diminishing sensitivity), nudge theory and choice architecture, and the integration of psychological findings into economic models. Use when analyzing decision-making under uncertainty, evaluating policy interventions that exploit behavioral patterns, or assessing where standard rational-agent models break down.

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Understanding how algorithmic systems shape what users see, know, and do -- from recommendation feeds to search ranking to credit scoring to hiring software. Covers the mechanics of recommendation systems, algorithmic bias and its sources, personalization's effects on information diets, opacity and accountability, AI limitations (hallucination, confident wrongness), and the human-in-the-loop question. Use when a learner needs to think critically about why particular content reached them.

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Supervised and unsupervised learning, bias-variance tradeoff, cross-validation, decision trees, ensemble methods, neural network fundamentals, and the practitioner's workflow from problem framing through deployment. Covers classification, regression, clustering, dimensionality reduction, regularization, hyperparameter tuning, and evaluation metrics. Use when building predictive models, selecting algorithms, or understanding the machine learning pipeline.

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。

Interrupt controller converting polled to proactive agent execution. Per-runtime strategies, configurable thresholds, Deacon heartbeat supervision. Fights LLM passivity bias.

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。

When the user wants to apply psychological principles, mental models, or behavioral science to marketing. Also use when the user mentions 'psychology,' 'mental models,' 'cognitive bias,' 'persuasion,' 'behavioral science,' 'why people buy,' 'decision-making,' or 'consumer behavior.' This skill provides 70+ mental models organized for marketing application.

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Designs survey instruments, covering question wording, scale construction and index reliability, flow and treatment placement, pretesting and soft launches, respondent burden with attention and speeding rules, sensitive questions and social-desirability bias, and treatment delivery. Use when the user writes or revises survey questions, chooses a scale, orders blocks, plans a pilot, or worries respondents will not answer honestly. Indirect measurement goes to list-experiment, multi-country instruments to cross-national-design, and live-survey operations to qualtrics-ops.

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Designs and diagnoses list experiments (the item count technique), covering whether indirect measurement is warranted, control-list construction against floor and ceiling effects, design variants, estimators and assumption tests in the list package, and power planning for the precision penalty. Use when the user measures a sensitive attitude or behavior and asks about list experiments, item count, unmatched count, or veiled questioning, or has an implausible prevalence estimate. Deciding whether sensitivity bias exists at all goes to survey-design.

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。

Catch insensitive, inconsiderate writing with alex. This CLI tool and Node.js library scans Markdown and plain text for gender-biased, polarizing, or exclusionary language and suggests inclusive alternatives.

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。