agentleFS
Sign inStart free
agenkinagenkin/telemetrydeck-analytics/skills/groupby/SKILL.md

groupby

Skill2 starsChanged 7 months agoMITView on GitHub

Break down an event count or user count by any TelemetryDeck dimension (license, OS version, country, etc.).

The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 0 are distinctive, and 3 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈0

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Run a one-dimension breakdown.

    line 8
  2. Very specific:

    If the user mentions "share" / "mix" / "split", compute percentages client-side from the result and include them in the summary.

    line 21
  3. Very specific:

    Always state what the metric represents: --metric count is event count (not users), --metric users is unique clientUser cardinality.

    line 23

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Groupby

Run a one-dimension breakdown. $ARGUMENTS starts with the dimension name and then any flags:

tdq groupby $ARGUMENTS

Examples:

  • "License split for App_launched, last 30d" → tdq groupby license --event App_launched --interval last-30d
  • "OS major-version breakdown by users, this month" → tdq groupby majorSystemVersion --metric users --interval this-month
  • "License share vs. last period" → tdq groupby license --event App_launched --interval last-30d --compare prior-period
  • "Top countries last week" → tdq groupby countryCode --metric users --interval last-7d

If the user mentions "share" / "mix" / "split", compute percentages client-side from the result and include them in the summary. If they say "vs last month" / "trending", add --compare prior-period.

Always state what the metric represents: --metric count is event count (not users), --metric users is unique clientUser cardinality.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

Check your setup, free