elias-vahlpahl/sk-73daaaa5f0a740e7b6f352f5fe925f95 icon
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Published on 8/1/2025
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Models
Context
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DeepSeek-V3

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DeepSeek-R1

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Prompts

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Exploratory Data Analysis
Initial data exploration and key insights
Create an exploratory data analysis workflow that includes:

Data Overview:
- Basic statistics (mean, median, std, quartiles)
- Missing values and data types
- Unique value distributions

Visualizations:
- Numerical: histograms, box plots
- Categorical: bar charts, frequency plots
- Relationships: correlation matrices
- Temporal patterns (if applicable)

Quality Assessment:
- Outlier detection
- Data inconsistencies
- Value range validation

Insights & Documentation:
- Key findings summary
- Data quality issues
- Variable relationships
- Next steps recommendations
- Reproducible Jupyter notebook

The user has provided the following information:
My prompt
Sequential Thinking Activation
<!-- Sequential Thinking Workflow -->
<assistant>
    <toolbox>
        <mcp_server name="sequential-thinking"
                        role="workflow_controller"
                        execution="sequential-thinking"
                        description="Initiate the sequential-thinking MCP server">
            <tool name="STEP" value="1">
                <description>Gather context by reading the relevant file(s).</description>
                <arguments>
                    <argument name="instructions" value="Seek proper context in the codebase to understand what is required. If you are unsure, ask the user." type="string" required="true"/>
                    <argument name="should_read_entire_file" type="boolean" default="true" required="false"/>
                </arguments>
                <result type="string" description="Context gathered from the file(s). Output can be passed to subsequent steps."/>
            </tool>
            <tool name="STEP" value="2">
                <description>Generate code changes based on the gathered context (from STEP 1).</description>
                <arguments>
                    <argument name="instructions" value="Generate the proper changes/corrections based on context from STEP 1." type="string" required="true"/>
                    <argument name="code_edit" type="object" required="true" description="Output: The proposed code modifications."/>
                </arguments>
                <result type="object" description="The generated code changes (code_edit object). Output can be passed to subsequent steps."/>
            </tool>
            <tool name="STEP" value="3">
                <description>Review the generated changes (from STEP 2) and suggest improvements.</description>
                <arguments>
                    <argument name="instructions" type="string" value="Review the changes applied in STEP 2 for gaps, correctness, and adherence to guidelines. Suggest improvements or identify any additional steps needed." required="true"/>
                </arguments>
                <result type="string" description="Review feedback, suggested improvements, or confirmation of completion. Final output of the workflow."/>
            </tool>
        </mcp_server>
    </toolbox>
</assistant>

Context

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@diff
Reference all of the changes you've made to your current branch
@codebase
Reference the most relevant snippets from your codebase
@url
Reference the markdown converted contents of a given URL
@folder
Uses the same retrieval mechanism as @Codebase, but only on a single folder
@terminal
Reference the last command you ran in your IDE's terminal and its output
@code
Reference specific functions or classes from throughout your project
@file
Reference any file in your current workspace

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MCP Servers

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Memory

npx -y @modelcontextprotocol/server-memory

Exa

npx -y exa-mcp-server

Playwright

npx -y @executeautomation/playwright-mcp-server

Browser MCP

npx -y @browsermcp/mcp@latest

GitHub

npx -y @modelcontextprotocol/server-github

Filesystem

npx -y @modelcontextprotocol/server-filesystem ${{ secrets.elias-vahlpahl/sk-73daaaa5f0a740e7b6f352f5fe925f95/anthropic/filesystem-mcp/PATH }}