> For the complete documentation index, see [llms.txt](https://docs.tickertrends.io/tickertrends/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tickertrends.io/tickertrends/documentation-v2/2.-data-coverage-and-data-types/2.2-data-types/homebrew-installs-formula.md).

# Homebrew installs (Formula)

#### Overview

Homebrew Installs (Formula) tracks installation activity for command-line tools, developer libraries, and infrastructure software distributed through Homebrew’s Formula package ecosystem. This dataset helps measure adoption trends for developer tools and technical software by monitoring install activity across the Homebrew ecosystem.

#### Historical Length

Historical coverage varies depending on the package, with multi-month to multi-year history available for supported formulas.

#### Granularity

Data is available at daily granularity, allowing users to monitor adoption trends, developer tooling momentum, and infrastructure software usage over time.

#### Update Frequency

Updated regularly as new Homebrew package installation data becomes available.

#### Methodology

Homebrew Installs (Formula) measures installation activity for packages distributed through Homebrew’s Formula package management ecosystem.

Homebrew Formula is primarily used to install command-line tools, developer frameworks, programming languages, infrastructure tooling, and technical dependencies such as Python, Node.js, Git, FFmpeg, wget, Kubernetes CLI tools, and database clients.

Because installation activity often reflects real developer adoption and workflow behavior, this dataset can serve as a leading indicator for technology adoption, infrastructure tooling growth, programming ecosystem momentum, and enterprise software usage trends.

Unlike Homebrew Cask, which focuses on GUI desktop applications, Formula data captures backend tooling and developer infrastructure adoption.

#### Use Cases

* Tracking adoption trends for developer tools and infrastructure software
* Monitoring growth in programming language ecosystems and frameworks
* Comparing momentum between competing developer tools
* Identifying emerging infrastructure or AI tooling adoption
* Measuring technical ecosystem growth before broader enterprise adoption
* Evaluating developer workflow and software stack shifts over time


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# Agent Instructions
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