Add technical indicators to a Go project using generics and streaming data workflows.
npx skills add https://github.com/cinar/indicator --skill add-indicator
Please help me install this Agent Skill. Skill: add-indicator Source: https://github.com/cinar/indicator/tree/main/.gemini/skills/add-indicator Command: npx skills add https://github.com/cinar/indicator --skill add-indicator
Introduces a standardized, end-to-end workflow for adding new technical indicators to the indicator project, ensuring consistency with streaming data patterns, Go generics, and a channel-based architecture.
Follow the workflow to implement a new indicator by selecting a category, coding with generics for a streaming pipeline, and adding tests.
High-intent search queries and answers about installing and using this skill.
To add a new technical indicator, you follow a workflow that uses Go generics for streaming data pipelines, selects the appropriate category like trend or momentum, and implements a channel-based factory constructor with Compute and IdlePeriod interfaces.
The standard structure for indicators like SMA or RSI involves a category-based organization, frontmatter documentation, a generics-based streaming input and output pipeline, and predictable testing standards to ensure consistent integration.
Yes, you can use Go generics to build a channel-based architecture for EMA calculations, utilizing factory constructors and a standardized streaming data workflow to process continuous inputs efficiently.
The best way to organize technical indicators is by mapping them to specific categories such as trend, momentum, volatility, volume, and valuation, ensuring a reusable scaffold and consistent coding standards across the project.
The Compute and IdlePeriod interface provides a predictable execution flow for streaming indicators, processing continuous channel-based inputs and managing warm-up periods before generating valid indicator outputs.
When creating a new momentum indicator, you should follow standardized testing practices that validate the channel-based streaming outputs against expected values, simplifying the integration of new indicators into the existing project architecture.
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