productivity▌

Structured prompt for generating Epic-level Product Requirements Documents with consistent output format. \n \n Guides product managers through a standardized PRD structure covering goals, user personas, journeys, functional and non-functional requirements, success metrics, and scope boundaries \n Outputs markdown-formatted PRDs saved to a consistent directory path ( /docs/ways-of-work/plan/{epic-name}/epic.md ) for use as single source of truth \n Includes built-in prompting for clarifying ques

Structured competitive analysis with feature matrices, pricing breakdowns, SWOT analysis, and positioning maps. \n \n Provides a 7-layer analysis framework covering product, pricing, positioning, traction, reviews, content, and team across competitors \n Includes templates and commands for feature matrices, pricing comparisons, SWOT analysis, and 2x2 positioning maps with visual generation \n Integrates web search (Tavily, Exa), browser automation for UX screenshots, and Python for positioning m

Add human warmth, emotion, and natural speech patterns to AI voice output. \n \n Includes five speaking presets (good night, good morning, comfort, celebration, just chatting) that automatically tune pace, warmth, and emotional tone \n Sprinkle non-lexical fillers (hmm, aww, haha, sighs) at natural pauses to create conversational authenticity; supports up to 4 fillers per short message \n Clone specific character voices by providing reference audio from YouTube clips or personal recordings, forw

Add educational comments to code files to transform them into effective learning resources. \n \n Adapts explanation depth and tone to three configurable knowledge levels: beginner, intermediate, and advanced \n Automatically requests a file if none is provided, with numbered list matching for quick selection \n Expands files by up to 125% using educational comments only (hard limit: 400 new lines; 300 for files over 1,000 lines) \n Preserves file encoding, indentation style, syntax correctness,

Autonomously research new technologies from the web and generate reusable agent skills. \n \n Discovers authoritative documentation via web search, prioritizing official docs, GitHub repositories, and blogs \n Extracts installation, core concepts, API references, and code examples from 3–5 high-quality sources \n Generates a self-contained skill with YAML frontmatter, markdown instructions, and optional bundled resources (scripts, references, assets) \n Saves skills to workspace-specific or glob

Here's a common workflow for using Replicate's API to run a model:

This is a Level 7 (self-improving) skill. It has two distinct sections:

Writes an explicit entry to auto-memory when something is important enough that you don't want to rely on Claude noticing it automatically.

Persistent, domain-organized memory system that captures lessons learned across VS Code projects. \n \n Stores reusable knowledge in two scopes: global (all projects) or workspace-specific, automatically organizing learnings by domain \n Uses simple syntax ( /remember [>domain [scope]] lesson ) to transform debugging sessions and hard-won discoveries into searchable memory instructions \n Automatically discovers existing memory domains and intelligently categorizes new learnings, creating domain

Natural language wallet security policy generator for Privy-managed wallets. \n \n Converts plain-language security requirements into Privy policy rule JSON, supporting transfer limits, address allowlists, method restrictions, chain constraints, and time windows \n Covers both EVM (Ethereum, Base, Arbitrum, Polygon, etc.) and Solana chains with dedicated condition types for each ecosystem \n Sends proposed policies to the frontend via wallet_propose_policy tool for user review and signature appr