Results for “behavioral-patterns”

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

Analyzing session replays, extracting persona-based behavioral patterns, and storytelling UX issues. Reads the 'why' from real user operation logs. Works with Field/Echo for persona validation.

Design feedback patterns for user actions, including confirmations, status updates, and notifications.

Fundamental patterns for effective agentic behavior. Teaches decomposition, tool orchestration, error recovery, context management, quality self-assessment, and knowing when to stop. Model-agnostic principles that make any agent more effective regardless of domain. Activate on: "how should I structure this agent", "agentic workflow", "agent patterns", "multi-step task", "tool orchestration", "/agentic-patterns", "decompose this", "agent best practices", "chain of actions", "when should the agent stop", "agent loop design". NOT for: creating agent infrastructure (use agent-creator), building DAGs (use windags-architect), specific tool implementation.

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

Pattern-Based Diagnosis

GOF State Pattern

JS State Pattern

Composition Patterns

Audits decisions and situations for operating psychological biases using Munger's 25 tendencies framework, producing a structured analysis with countermeasures.

Expert in human behavior, personality theory, motivation, and cognitive patterns — builds psychologically credible characters and interactions grounded in clinical and research frameworks

Diagnose and fix retention problems using behavior design (B=MAP). Use when the user mentions "users drop off", "activation rate", "onboarding friction", "retention metrics", "why users dont complete", "churn analysis", "user activation", or "aha moment". Also trigger when analyzing cohort retention curves, designing activation milestones, reducing time-to-value for new users, or investigating why users stop after their first session. Covers the Ability Chain, prompt design, and tiny behaviors that compound. For habit loops and variable rewards, see hooked-ux. For intrinsic motivation, see drive-motivation.

Analyzes GitHub Copilot CLI conversation history to extract behavioral patterns and writes a personalization block into the user's Copilot instructions file.

Interprets Culture Index survey data, behavioral profiles, and personality assessments from JSON or PDF. Supports individual profile interpretation, team composition analysis, burnout detection, hiring profiles, manager coaching, interview transcript analysis, and conflict mediation.

Provides idiomatic React 18/19 patterns for hooks, server/client components, Suspense, error boundaries, forms, data fetching, state management, and accessibility-first composition.

Provides copy-paste animation patterns for React and Next.js, including buttons, modals, toasts, stagger lists, page transitions, scroll reveals, and layout animations, all built on motion-foundations tokens.

Apple HIG: Interaction Patterns

Apple Human Interface Guidelines interaction and UX patterns.

Designs intuitive gesture-based interactions for touch and pointer devices, covering core gestures, discoverability, feedback, thresholds, conflict resolution, and accessibility.

Master error handling patterns across languages including exceptions, Result types, error propagation, and graceful degradation to build resilient applications. Use when implementing error handling, designing APIs, or improving application reliability.

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

Provides Pythonic idioms, PEP 8 standards, type hints, and best practices for building robust, efficient, and maintainable Python applications.

AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.

Identifies recurring patterns across three or more domains to extract universal principles and apply them to new contexts.

AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.

Apply named refactoring transformations to improve code structure without changing behavior, guided by code smells and safe transformation sequences.

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

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.

A library of reusable, production-tested prompt engineering patterns for building AI-powered features. Use when designing system prompts for apps, building AI pipelines, selecting the right prompting technique for a use case, or reviewing prompts for common failure modes. Complements the prompt-engineering skill (which covers the optimization framework); this skill covers the pattern library itself.

Design habit-forming product loops using the Hook Model (Trigger, Action, Variable Reward, Investment) to analyze and improve user engagement, retention, and re-engagement strategies.