productivity▌

Store per-project plugin configuration and state in .claude/plugin-name.local.md files with YAML frontmatter. \n \n Settings files use YAML frontmatter for structured configuration and markdown body for additional context or prompts \n Hooks, commands, and agents can read settings to customize behavior without requiring code changes \n Includes bash parsing patterns for extracting individual fields (strings, booleans, numerics) and markdown body content \n Common patterns include toggling hook a

Frameworks for building and scaling product operations functions across growing teams. \n \n Bridges product and operations by creating systems that enable PMs to focus on strategy rather than operational overhead like release management, enablement, and cross-functional coordination \n Standardizes processes, tooling, and insights across product teams while preserving PM decision-making authority; product ops informs, not decides \n Addresses common scaling challenges: surfacing user research a

Guide users through transitioning from pure product-led growth to sales-assisted expansion using product usage signals. \n \n Helps define Product Qualified Leads (PQLs) and Product Qualified Accounts (PQAs) based on actual product engagement rather than marketing metrics \n Covers the complete handoff workflow: identifying usage signals that trigger sales outreach, designing smooth transitions from self-serve to sales-assisted, and aligning product and sales team incentives \n Emphasizes person

Patterns for building shared UI across Android, iOS, Desktop, and Web using Compose Multiplatform and Jetpack Compose. Covers state management, navigation, theming, and performance.

Answers: "Is this token held by quality wallets or retail noise?"

Language models exhibit predictable degradation patterns as context length increases. Understanding these patterns is essential for diagnosing failures and designing resilient systems. Context degradation is not a binary state but a continuum of performance degradation that manifests in several distinct ways.

Web search with AI-optimized results, relevance scoring, and advanced filtering via Tavily API. \n \n Supports three topic types (general, news, finance) and two search depths (basic, advanced) with optional AI-generated answer summaries \n Includes domain filtering to include or exclude specific sites, time-range filtering (day, week, month, year), and configurable result counts \n Returns structured results with relevance scores, publication dates, and optional raw page content; supports both

Framework-driven pricing design and optimization using insights from 46 product leaders. \n \n Guides users through understanding business models, assessing value delivery, identifying constraints, and designing iterative pricing strategies \n Emphasizes willingness-to-pay research as the foundation for pricing decisions and product roadmap prioritization \n Covers key transitions: freemium vs. paid models, self-serve to sales-led (around $10K–$15K ARR), and sampling premium features in free tie

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Streaming protocol implementation guide for custom AI backends and assistant-ui integration. \n \n Provides createAssistantStreamResponse for building custom streaming responses with text, tool calls, and structured event handling \n Supports two integration paths: Vercel AI SDK (via toUIMessageStreamResponse ) or native assistant-stream for non-SDK backends \n Includes useLocalRuntime pattern for client-side streaming with ChatModelRunResult chunks and content part yielding \n Covers stream eve