本文へ移動
cccskills
無料GitHub で公開

sequential-thinking

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.

インストール方法を見る

含まれるファイル(4)

  • SKILL.md3.1 KB
  • README.md3.0 KB
  • references/advanced.md3.2 KB
  • references/examples.md7.6 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Sequential Thinking

Enables structured problem-solving through iterative reasoning with revision and branching capabilities.

Core Capabilities

  • Iterative reasoning: Break complex problems into sequential thought steps
  • Dynamic scope: Adjust total thought count as understanding evolves
  • Revision tracking: Reconsider and modify previous conclusions
  • Branch exploration: Explore alternative reasoning paths from any point
  • Maintained context: Keep track of reasoning chain throughout analysis

When to Use

Use mcp__reasoning__sequentialthinking when:

  • Problem requires multiple interconnected reasoning steps
  • Initial scope or approach is uncertain
  • Need to filter through complexity to find core issues
  • May need to backtrack or revise earlier conclusions
  • Want to explore alternative solution paths

Don't use for: Simple queries, direct facts, or single-step tasks.

Basic Usage

The MCP tool mcp__reasoning__sequentialthinking accepts these parameters:

Required Parameters

  • thought (string): Current reasoning step
  • nextThoughtNeeded (boolean): Whether more reasoning is needed
  • thoughtNumber (integer): Current step number (starts at 1)
  • totalThoughts (integer): Estimated total steps needed

Optional Parameters

  • isRevision (boolean): Indicates this revises previous thinking
  • revisesThought (integer): Which thought number is being reconsidered
  • branchFromThought (integer): Thought number to branch from
  • branchId (string): Identifier for this reasoning branch

Workflow Pattern

1. 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`

Simple Example

// 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

Advanced Features

For revision patterns, branching strategies, and complex workflows, see:

Tips

  • Start with rough estimate for totalThoughts, refine as you progress
  • Use revision when assumptions prove incorrect
  • Branch when multiple approaches seem viable
  • Express uncertainty explicitly in thoughts
  • Adjust scope freely - accuracy matters less than progress visibility

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

2d-games

無料

2D game development principles. Sprites, tilemaps, physics, camera.

日本語の概要は準備中です。原文の説明を表示しています。

VoDaiLocz/kilo-kit-mcp272026年9月13日 更新

3d-games

無料

3D game development principles. Rendering, shaders, physics, cameras.

日本語の概要は準備中です。原文の説明を表示しています。

VoDaiLocz/kilo-kit-mcp272026年9月13日 更新

aesthetic

無料

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.

日本語の概要は準備中です。原文の説明を表示しています。

VoDaiLocz/kilo-kit-mcp272026年9月13日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

VoDaiLocz/kilo-kit-mcp272026年9月13日 更新

Use when monitoring, tracing, or debugging agentic workflows in production. Keywords: observability, tracing, OpenTelemetry, Langfuse, latency, token cost, loop detection, telemetry.

日本語の概要は準備中です。原文の説明を表示しています。

VoDaiLocz/kilo-kit-mcp272026年9月13日 更新

Use when building self-correcting retrieval systems for AI agents. Keywords: RAG, retrieval, Corrective RAG, Self-RAG, query decomposition, reranking, hallucination, grounding.

日本語の概要は準備中です。原文の説明を表示しています。

VoDaiLocz/kilo-kit-mcp272026年9月13日 更新

VoDaiLocz のスキルをすべて見る

このスキルの問題を報告する