By name: sequential-thinking description: "Tools for step-by-step reasoning"
Sequential Thinking Skill
Metadata
- Skill Name: sequential-thinking
- Trigger Tag:
#st - MCP Server: sequentialthinking (built-in reasoning enhancement)
- Category: Problem Solving
Note: Activated with
#st(short for sequential thinking). Use when a problem requires breaking down into explicit, verifiable reasoning steps.
Description
Engage structured step-by-step reasoning for complex problem-solving. Breaks down queries into logical steps with the ability to revise, branch, and verify thinking before arriving at a final answer. Reduces reasoning errors on multi-step or ambiguous problems.
Capabilities
- Multi-step problem decomposition with explicit thought chain
- Hypothesis generation and verification
- Ability to revise earlier reasoning steps mid-chain
- Branching for exploring alternative approaches
- Systematic debugging through structured analysis
- Architectural and design decision reasoning
Activation
Include #st tag in your prompt to activate this skill.
Usage Examples
Algorithm Design
#st Design an efficient algorithm for finding duplicates in a large dataset
Complex Debugging
#st Debug why the authentication flow fails intermittently under load
Architecture Planning
#st Plan microservices decomposition for an e-commerce platform
Root Cause Analysis
#st Analyze why memory usage grows over time in this Node.js service
Configuration
MCP server is configured in the .copilot/mcp-config.json.
Environment Variables
None required. This is a built-in reasoning tool with no external service dependency.
Connectivity Check
This skill uses a built-in reasoning tool with no external MCP service dependency. No connectivity check is required before use.
Best Practices
- Use for complex, multi-step problems where linear reasoning may miss edge cases
- Ideal for architectural decisions with multiple valid tradeoffs
- Helpful for debugging non-obvious or intermittent issues
- Good for algorithm design where correctness must be verified step-by-step
- Overkill for simple, well-defined questions
Limitations
- Increases response length and time compared to direct answers
- Not needed for straightforward lookup or simple code generation tasks
GitHub Copilot & LLM Optimization Context
- Environment Indicator: You are operating within the GitHub Copilot CLI context. Always leverage native GitHub Copilot capabilities when interacting with codebases.
- Model Optimization: This prompt is optimized specifically for Claude Opus 4.5.
- Leverage Claude Opus 4.5's deep comprehension and superior coding accuracy for complex architectural and logical tasks.
- Ensure responses are direct, code-focused, and minimize conversational filler to optimize for developer workflows.