skills Skill
Best practices for using the DataTrue MCP integration to create, configure, run,
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Why use this skill
skills is most useful when you want an agent workflow that is more structured than an ad-hoc prompt. Instead of restating the same expectations every time, a dedicated SKILL.md file gives the assistant a repeatable brief. In this case, the core value is clarity: the repo already frames the workflow around documentation skills tasks, and the skill source gives you a portable starting point you can evaluate, adapt, and reuse. The inferred platform for this skill is Generic Skills, which helps you judge whether it is likely to feel native in your current agent ecosystem or whether it is better treated as a general reference.
That matters because AI assistants are better when the operating context is explicit. A good skill turns hidden team expectations into visible instructions. It can name preferred tools, describe failure modes, define what “done” looks like, and reduce the amount of corrective prompting you need after the first draft. For developers exploring the wider SKILL.md ecosystem, this page helps answer the practical question: is this skill specific and maintained enough to be worth trying?
How to evaluate and use it
Start with the source repo and the preview below. The preview tells you whether the instructions are actionable or just aspirational. Strong skills usually describe triggers, recommended tools, steps, and known pitfalls. Weak skills tend to stay generic. This one lives in datatrue-analytics/datatrue-ai-skills, which gives you a concrete repo context, update history, and direct ownership trail.
Once you confirm the scope looks right, test it on a small task before making it part of a larger workflow. If it improves consistency, keep it. If it is too broad, outdated, or conflicts with your own process, treat it as a reference rather than a drop-in rule. That is the healthiest way to use directory-discovered skills: not as magic plugins, but as reusable operational knowledge that still deserves judgment.
SKILL.md preview
Previewing the source is one of the fastest ways to judge whether a skill is truly useful. This snippet comes from the public file in the linked repository.
---
name: datatrue-ai-skills
description: >
Best practices for using the DataTrue MCP integration to create, configure, run,
and analyse tag validation tests. Use this skill whenever the user wants to work
with DataTrue — including creating suites, tests, steps, tag validations, data
layer validations, running tests, or reviewing results. Also trigger when the user
mentions "tag auditing", "GA4 validation", "GTM dataLayer checks", "cookie consent
testing", "marketing tag QA", or any reference to the DataTrue platform. Even if
the user just says "check my tags" or "validate analytics", this skill should be
consulted first.
---
# DataTrue MCP Skill
This skill captures hard-won best practices for using the DataTrue MCP tools to
build, run, and interpret tag validation tests. Following these patterns will save
significant trial-and-error time.
## Sub-skills
Base URL: https://github.com/datatrue-analytics/datatrue-ai-skills/tree/main/skills
Before working with suites, fetch: {base}/suites/SKILL.md
## Quick Reference: Object Hierarchy
DataTrue uses a strict hierarchy. Always create objects in this order:
```
Account → Suite → Test → Steps → Validations (Tag + DataLayer)
```
1. **Account** — the top-level container. Use `ListAccounts` to find the account ID.
2. **Suite** — groups related tests. Requires `accountId`, `name`, and `sensitiveDataSetting`.
3. **Test** — a simulation, coverage, email, or mobile_app test inside a suite.
4. **Step** — an action inside a test (navigate, click, type, run script, etc.).
5. **Validations** — tag validations and data l
...