2d-games
Free2D game development principles. Sprites, tilemaps, physics, camera.
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Use when complex problems require systematic step-by-step reasoning with ability to revise thoughts, branch into alternative approaches, or dynamically adjust scope. Ideal for multi-stage analysis, design planning, problem decomposition, or tasks with initially unclear scope.
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Enables structured problem-solving through iterative reasoning with revision and branching capabilities.
Use mcp__reasoning__sequentialthinking when:
Don't use for: Simple queries, direct facts, or single-step tasks.
The MCP tool mcp__reasoning__sequentialthinking accepts these parameters:
thought (string): Current reasoning stepnextThoughtNeeded (boolean): Whether more reasoning is neededthoughtNumber (integer): Current step number (starts at 1)totalThoughts (integer): Estimated total steps neededisRevision (boolean): Indicates this revises previous thinkingrevisesThought (integer): Which thought number is being reconsideredbranchFromThought (integer): Thought number to branch frombranchId (string): Identifier for this reasoning branch1. Start with initial thought (thoughtNumber: 1)
2. For each step:
- Express current reasoning in `thought`
- Estimate remaining work via `totalThoughts` (adjust dynamically)
- Set `nextThoughtNeeded: true` to continue
3. When reaching conclusion, set `nextThoughtNeeded: false`
// First thought
{
thought: "Problem involves optimizing database queries. Need to identify bottlenecks first.",
thoughtNumber: 1,
totalThoughts: 5,
nextThoughtNeeded: true
}
// Second thought
{
thought: "Analyzing query patterns reveals N+1 problem in user fetches.",
thoughtNumber: 2,
totalThoughts: 6, // Adjusted scope
nextThoughtNeeded: true
}
// ... continue until done
For revision patterns, branching strategies, and complex workflows, see:
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2D game development principles. Sprites, tilemaps, physics, camera.
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3D game development principles. Rendering, shaders, physics, cameras.
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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.
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Use when implementing or managing persistent, hierarchical memory systems for AI agents. Covers cross-session state, fact supersession, and self-managed memory tools to enable long-term recall and adaptive agent behavior.
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Use when monitoring, tracing, or debugging agentic workflows in production. Keywords: observability, tracing, OpenTelemetry, Langfuse, latency, token cost, loop detection, telemetry.
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Use when building self-correcting retrieval systems for AI agents. Keywords: RAG, retrieval, Corrective RAG, Self-RAG, query decomposition, reranking, hallucination, grounding.
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