Results for “cognitive-biases”

Applies behavioral science and persuasion frameworks to marketing. Use when the user requests 'cognitive bias', 'loss aversion', 'social proof', 'scarcity', 'Cialdini', or 'persuasion framework'.

当观察到或经历某种极端转化现象，或试图主动促成根本性观念转变时

Simulates a multi-perspective deliberation council to explore complex questions, decisions, or creative challenges from diverse thinking archetypes.

Benchmarks LLM bias by scoring responses to automatically generated open-ended questions across sensitive attributes, producing a composite fitness score from 0 to 5.

Apply six decades of persuasion science—Cialdini's principles of reciprocity, commitment, social proof, authority, liking, scarcity, and unity—to product design, copy, and sales, ethically.

当个体在数字环境中困惑于自身角色（消费vs创造），或认为吸引注意力不道德时

Use to measure customer effort from behavioural signals instead of CES surveys, and to audit whether a composite effort score is honest. Trigger for "customer effort score", "behavioural CES", effort without survey, repeat contacts and channel switches, transfers and reopens, or "our CES doesn't match operational data".

Stores, retrieves, and curates persistent facts in a structured memory map, distinguishing between user facts and reference text.

Apply behavioral science and persuasion principles to make marketing more convincing.

Build a library of metacognitive prompts targeting planning, monitoring, or evaluation for a specific task. Use when developing students' thinking-about-thinking during independent work.

当需要将个人知识或经验转化为商业机会，或希望建立有意义的个人发展路径时

Evaluate scientific claims and evidence quality by assessing experimental design, identifying biases and confounders, and applying evidence grading frameworks like GRADE and Cochrane Risk of Bias.

Modify a classroom task with language scaffolds that preserve cognitive demand for EAL learners. Use when adapting existing tasks for students at different English proficiency levels.

当个人感到迷茫，不确定自己真正想要什么，或发现当前追求无法带来深层满足时，调用此模型来探索和澄清内在真实欲望

Evaluate a screen's information density by analyzing cognitive load, content prioritization, scanning patterns, and progressive disclosure, with actionable fixes for each dimension.

Evaluates social bias in question-answering models using the BBQ benchmark, measuring accuracy and a bias score across ambiguous and disambiguated contexts to reveal reliance on stereotypes.

Skeptical pushback on visual placement — both density and quality. Reads the annotated outline plus the visuals manifest and asks (a) whether the article hits the density target from editorial-principles-visuals.md, (b) whether each [VISUAL:...] earns its place, (c) whether sections without one would benefit. One revision pass on FAIL (BLOG_AGENT_VISUALS_REVISION_BUDGET, default 1).

Capture confidence ratings before and after a learning attempt to identify overconfidence and underconfidence patterns. Use when a student wants to understand how well they actually know something versus how well they think they know it.

Validates the Emotional Attitude Score (EAS) metric by measuring its consistency with human judgment on word-level sentiment polarity, using the AmbGIMT dataset and pairwise score comparisons.

Performance-coaching mode for repeated failures, passive behavior, completion-quality issues, or explicit try-harder requests. Uses structured troubleshooting and evidence-first delivery habits.

当感到迷茫、焦虑，不清楚人生目标或方向时，用于通过主动试错来探索和澄清个人真实欲望

当遇到令人情绪激动或陷入僵局的情境，需要跳出固有反应模式以做出更优决策时

Evaluation of BaSiC illumination correction caching - NOT RECOMMENDED for sparse markers. Trigger: optimizing BaSiC, caching illumination correction

Design metacognitive checkpoints that prevent AI-assisted learning from bypassing genuine understanding. Use when students use AI tools and may overestimate their own comprehension.

Creates digital banners for social, display, hero, and email formats, applying behavioral science principles to maximize attention and conversion.

Simulates Bill Gates' cognitive framework, strategic thinking, and decision-making processes for analysis and problem-solving.

Diagnose and fix retention problems using the Fogg Behavior Model (B=MAP), covering motivation, ability, prompts, and tiny habits.

Guide teams through MITRE's Problem Framing Canvas to clarify problem statements before jumping to solutions.

Run Mom Test–style customer-discovery interviews to validate or kill an unbuilt idea — generate a non-leading interview guide, conduct or coach the conversations, and synthesize signal vs compliments. Load when the user asks to do customer discovery, run problem interviews, validate an idea with users, run a Mom Test, design an interview guide, or says "talk to customers", "validate the problem", "interview users", "Mom Test this", "did real users want it", "synthesize my interviews", "I just talked to N people". Sub-skill of `venture-exploration`. Hard-bans "would you use this?", solution-pitching, friend/family-only ICP, and treating compliments as validation. Calls `secure-*` before synthesizing any pasted external transcripts.

Use to design or fix a customer support survey — CSAT, NPS, or CES — and to diagnose response bias. Trigger for "design a CSAT survey", "our CSAT doesn't match reality", "should we use NPS", survey response rate, non-response bias, survey timing or scale choice, comparing satisfaction across teams or channels, or interpreting a satisfaction trend.

旗木卡卡西（少年漫）认知与表达框架（压缩蒸馏）：迟到的老师、写轮眼悬念、慵懒靠谱 触发：火影忍者 等。虚构；非暴力教唆

Diverga Memory System v7.0 - Context-persistent research support with checkpoint auto-trigger and cross-session continuity. Triggers: memory, remember, context, recall, checkpoint, decision, persist, 기억, 맥락, 세션, 체크포인트

蔡澜（美食 / 生活家）认知与表达框架（压缩蒸馏）：享乐主义正当化、旅行搭子叙事 触发：食神专栏 等。非过量饮酒医疗建议

Sigmoid Loss for Language Image Pre-Training

Prevent Kubernetes hallucinations by diagnosing and fixing failure modes: insecure workload defaults, resource starvation, network exposure, privilege sprawl, fragile rollouts, and API drift. Use when generating, reviewing, refactoring, or migrating manifests, Helm charts, Kustomize overlays, cluster policies, and platform-specific Kubernetes work for EKS, GKE, AKS, OpenShift, GitOps controllers, or observability stacks.