Embed pre-conditions and audit templates into analytics metric development.
npx skills add https://github.com/vitalwarley/hyprdots --skill implement-vitalwarley
Please help me install this Agent Skill. Skill: implement Source: https://github.com/vitalwarley/hyprdots/tree/main/claude-global/skills/implement Command: npx skills add https://github.com/vitalwarley/hyprdots --skill implement-vitalwarley
This Skill guides building analytics or metrics with pre-conditions, ensuring auditability from the start rather than retrofitting an audit later.
Implement a new metric or pipeline following the audit-first protocol and generate an audit document alongside the code.
High-intent search queries and answers about installing and using this skill.
To build reproducible analytics pipelines with audit trails, embed pre-conditions, process steps, and post-conditions directly into metric development. This approach generates an audit-ready template output stored in report and results directories during implementation.
An audit-first approach for metric development creates audit artifacts proactively during implementation. It verifies explicit pre-conditions before coding to prevent wasted effort and retrofitted audits, ensuring reproducible scripts and documented assumptions from the start.
Verify pre-conditions before coding exploratory analyses by checking assumptions explicitly at the start of the workflow. This prevents wasted effort and ensures the resulting pipeline scripts remain reproducible and audit-ready throughout the process.
The best way to document analytics workflows for auditing is generating audit skeletons and templates during implementation. Store these audit-ready outputs under report and results directories alongside the reproducible pipeline scripts and metric code.
Retrofitting audit documentation into existing pipeline scripts is possible but discouraged. An audit-first approach prevents this by generating audit skeletons and verifying pre-conditions during metric development, ensuring auditability from the start rather than after.
Audit-first metric implementation works for exploratory data analysis by embedding pre-conditions verification and reproducible scripts into the analysis workflow. It produces an audit artifact alongside the exploratory results to ensure traceability.
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