Scale Game
Scale Game is a testing methodology that validates system design by intentionally testing at extreme parameters, such as processing one item versus one billion items, or operating over milliseconds versus years. Use this skill when designing architecture, building new systems, or troubleshooting unexpected production failures to identify fundamental weaknesses and confirm what assumptions actually hold under stress.
git clone --depth 1 https://github.com/mrgoonie/claudekit-skills /tmp/scale-game && cp -r /tmp/scale-game/.claude/skills/problem-solving/scale-game ~/.claude/skills/scale-gameSKILL.md
# Scale Game ## Overview Test your approach at extreme scales to find what breaks and what surprisingly survives. **Core principle:** Extremes expose fundamental truths hidden at normal scales. ## Quick Reference | Scale Dimension | Test At Extremes | What It Reveals | |-----------------|------------------|-----------------| | Volume | 1 item vs 1B items | Algorithmic complexity limits | | Speed | Instant vs 1 year | Async requirements, caching needs | | Users | 1 user vs 1B users | Concurrency issues, resource limits | | Duration | Milliseconds vs years | Memory leaks, state growth | | Failure rate | Never fails vs always fails | Error handling adequacy | ## Process 1. **Pick dimension** - What could vary extremely? 2. **Test minimum** - What if this was 1000x smaller/faster/fewer? 3. **Test maximum** - What if this was 1000x bigger/slower/more? 4. **Note what breaks** - Where do limits appear? 5. **Note what survives** - What's fundamentally sound? ## Examples ### Example 1: Error Handling **Normal scale:** "Handle errors when they occur" works fine **At 1B scale:** Error volume overwhelms logging, crashes system **Reveals:** Need to make errors impossible (type systems) or expect them (chaos engineering) ### Example 2: Synchronous APIs **Normal scale:** Direct function calls work **At global scale:** Network latency makes synchronous calls unusable **Reveals:** Async/messaging becomes survival requirement, not optimization ### Example 3: In-Memory State **Normal duration:** Works for hours/days **At years:** Memory grows unbounded, eventual crash **Reveals:** Need persistence or periodic cleanup, can't rely on memory ## Red Flags You Need This - "It works in dev" (but will it work in production?) - No idea where limits are - "Should scale fine" (without testing) - Surprised by production behavior ## Remember - Extremes reveal fundamentals - What works at one scale fails at another - Test both directions (bigger AND smaller) - Use insights to validate architecture early
Manage MCP (Model Context Protocol) server integrations - discover tools/prompts/resources, analyze relevance for tasks, and execute MCP capabilities. Use when need to work with MCP servers, discover available MCP tools, filter MCP capabilities for specific tasks, execute MCP tools programmatically, or implement MCP client functionality. Keeps main context clean by handling MCP discovery in subagent context.
Stage all files and create a commit.
Stage, commit and push all code in the current branch
Create a pull request
Create a new agent skill
Utilize tools of Model Context Protocol (MCP) servers
Create aesthetically beautiful interfaces following proven design principles. Use when building UI/UX, analyzing designs from inspiration sites, generating design images with ai-multimodal, implementing visual hierarchy and color theory, adding micro-interactions, or creating design documentation. Includes workflows for capturing and analyzing inspiration screenshots with chrome-devtools and ai-multimodal, iterative design image generation until aesthetic standards are met, and comprehensive design system guidance covering BEAUTIFUL (aesthetic principles), RIGHT (functionality/accessibility), SATISFYING (micro-interactions), and PEAK (storytelling) stages. Integrates with chrome-devtools, ai-multimodal, media-processing, ui-styling, and web-frameworks skills.
Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.