Create a detailed specification for a dashboard, covering its purpose, panels, data sources, refresh schedules, and interactions. Use a reusable structure to document what each panel shows and how users and external systems interact with it.
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A reusable template for defining detailed domain specifications for dashboard projects, including panel requirements, data sources, and interfaces.
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This skill provides a structured approach to creating detailed domain specifications for dashboard projects. It ensures clarity and completeness by covering panel requirements, data sources, and interfaces.
Define Dashboard Purpose:
List Panels:
Specify Data Sources:
Define Interfaces:
Include Examples:
- title: "Daily Active Users"
purpose: "Display the number of active users per day"
data_sources:
- type: "API"
endpoint: "https://api.example.com/users/daily"
authentication: "Bearer token"
data_format: "JSON"
refresh_rate: "hourly"
- name: "User Activity API"
type: "REST API"
endpoint: "https://api.example.com/users"
authentication: "OAuth 2.0"
data_format: "JSON"
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Prepare audio stems, the separate parts of a track, for mixing by matching their durations with silence, loops, or crossfades. Verify each generated and processed file before moving to the next production step.
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Produce audio in separate stems, meaning individual instrument tracks, and verify each output as you go. Align track lengths with silence, loops, or crossfades, then apply effects, mix a master, and package the files.
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Build serverless endpoints that relay external API requests for a dashboard while keeping API keys on the server. Includes Vercel examples for input checks, caching, cross-origin access, and consistent JSON responses.
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Produce a master track and separate audio parts through a structured Python workflow. Apply filters and reverb, export WAV files with consistent settings, package them in a ZIP archive, and check that the deliverables meet the requested specifications.
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Keep document creation moving when web searches and page retrieval repeatedly fail. The skill preserves the original task, uses the model’s existing knowledge, and clearly identifies information that could not be checked against external sources.
中文简介正在准备中。当前显示英语简介。
When Python execution repeatedly fails in execute_code_sandbox, use a file-based alternative: save the script, run it through the shell, and capture results or errors. The guidance covers paths, imports, timeouts, structured output, and optional cleanup.
中文简介正在准备中。当前显示英语简介。