Always-active skill that applies MiniMax M2.5 prompt engineering best practices. Automatically enriches prompts with additional context, asks for clarification when needed, and ensures clear instructions.
SkillScore87Maintained
Automatic
◆AI Engineer pack
Last verified 8/3/2026
What it changes
Passed 20 of 23 test cases with the skill, and 16 of 23 without it.
Without skill70%
With skill87%
Checks it passed that the model alone missed
The enriched prompt specifies an enterprise data visualization focus.
The system prompt provides a response template for order status updates.
The optimized rule includes the practical reason behind avoiding markdown.
and 3 more in the full report
5 fixed15 passed either way2 still failing1 broke
Pass-rate lift
+17 pp
Tokens
+18%
Agent turns
0%
Response time
-6%
Model gemini-3.6-flashMeasured 8/3/2026v120 of 23 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 5
Prompt
I want to add a rule to my prompt: 'Do not use markdown formatting or symbols'. Rewrite this rule to optimize it for model performance.
What was checked, without and with the skill
Checked
Without skill
With skill
The optimized rule includes the practical reason behind avoiding markdown.
Missed
Passed
The optimized rule explicitly specifies the intended plain-text output format.
Passed
Passed
case-01 · gemini-3.6-flash · 8/3/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md52 linessingle file
Prompt Engineering - MiniMax Best Practices
This skill is always active and applies MiniMax M2.5 prompt engineering techniques to every user interaction.
Core Principles
1. Be Clear and Specific with Instructions
Always state expected output format, content, and style explicitly
Avoid vague instructions; be explicit about what you need
Include specific requirements, constraints, and success criteria
2. Explain Your Intent
Tell the model "why" you're asking for something
When giving constraints, explain the reasoning behind them
Context helps the model provide more relevant answers
3. Focus on Examples and Details
Provide templates or examples of desired output
Show what to avoid along with what to include
Use concrete details rather than abstract descriptions
4. Request Additional Context When Needed
If a prompt is ambiguous, ask clarifying questions
Identify missing information that would improve the response
Don't assume - confirm understanding when uncertain
5. Add Context Proactively
When possible, enrich prompts with relevant background
Consider the user's goal, not just the literal request
Add helpful context that improves response quality
Always-Active Behavior
When processing ANY user message, automatically:
Analyze the request for clarity and completeness
Identify missing information or ambiguous parts
Enrich with relevant context when possible
Ask for clarification when critical details are missing