@noanswer/context-compose
Version:
Orchestrate complex AI interactions with Context Compose. A powerful CLI and server for building, validating, and managing context for large language models using the Model Context Protocol (MCP).
40 lines (34 loc) • 1.82 kB
YAML
version: 1
kind: mcp
name: sequential-thinking
description: Systematic sequential thinking process for complex problem solving
prompt: |
Apply a systematic, step-by-step thinking process to deconstruct
and solve complex problems. Define the problem, break it down into
manageable parts, execute each step methodically, and validate the
outcome to ensure a robust and well-documented solution.
enhanced-prompt: |
# 🧠 Sequential Thinking Process
## 1. Problem Definition
Start by clearly articulating the problem and its context.
- **Goal:** What is the desired outcome?
- **Constraints:** What are the limitations (e.g., time, tools)?
- **Assumptions:** What are you taking for granted?
- **Success Criteria:** How will you measure success?
## 2. Problem Decomposition
Break the main problem into smaller, sequential steps.
- **Identify Sub-tasks:** List all the individual tasks required.
- **Establish Order:** Arrange tasks in a logical, dependent sequence.
- **Prioritize:** Determine which tasks are most critical.
## 3. Step-by-Step Execution
For each step in your sequence, follow this structure:
- **Input:** What information or resources are needed?
- **Process:** What specific actions will you take?
- **Output:** What is the expected result of this step?
- **Validation:** How will you verify the output is correct?
## 4. Documentation and Review
Maintain a clear record of your process and decisions.
- **Log Decisions:** Note why certain choices were made over alternatives.
- **Record Outputs:** Document the results of each step.
- **Final Review:** Once all steps are complete, review the entire process against the original goal and success criteria.
**🎯 Result:** A systematic and validated solution with clear, reproducible steps.