agentleFS
m-binimranm-binimran/marketing-pack/skills/marketing-analytics/SKILL.md

marketing-analytics

Skill1 starsChanged 5 months agoMITView on GitHub

Turn marketing metrics into insights and next actions - funnel, channel, campaign, and cohort analysis - using real data only. Use when reviewing performance or building a report.

5 other projects use a version of this.

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Most recently taken up by

The file

We split this skill into the rules it gives an agent

1 rule. 0 are common, 1 is distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 5 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Don't over-read small samples or short windows.

    line 28

“Common” is measured: kept by at least 6 in 10 of the 5 edited versions of this file we could read. “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

marketing-analytics

Numbers into decisions. Read the right metric for the goal; never invent a figure.

Read the right metric per goal

  • Acquisition: traffic by channel, CTR, CPC, CPL/CPA, ROAS.
  • Conversion: landing/page conversion rate, funnel drop-off, form completion.
  • Email: delivery, open, click, unsubscribe, conversion.
  • Retention/value: repeat rate, LTV, churn, cohort behaviour.

Process

  1. Use the real data (supplied or via a connector - GA4, ad platforms, ESP). Don't fabricate or guess benchmarks.
  2. Find the story: what's working (scale it), where the funnel leaks (fix it), what underperforms (cut it). Compare to the baseline/goal, not vibes.
  3. Attribute honestly - note attribution model limits; don't over-credit a channel.
  4. Recommend: 3 concrete next actions + one experiment (ab-test).

Output

  • A short report: top performers + why, the biggest leak, 3 actions, 1 experiment - every number real and sourced.

Guardrails

  • No fabricated metrics or benchmarks (truth-telling); mark missing data [verify].
  • Don't over-read small samples or short windows; state the limitation.

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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.