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m-binimranm-binimran/creator-pack/skills/analytics-review/SKILL.md

analytics-review

Skill3 starsChanged 5 months agoMITView on GitHub

Interpret Instagram/Facebook Insights and turn them into next actions - what's working, what to do more of, what to cut. Use when reviewing performance from supplied metrics.

4 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 1 is distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

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

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

    Recommend: do more of what works (format/hook/topic), fix the drop-off point, stop what underperforms.

    line 19
  2. Very specific:

    No fabricated benchmarks or made-up metrics.

    line 27
  3. Distinctive:

    Don't over-read tiny samples.

    line 28

≈ “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

analytics-review

Numbers into decisions. Read the right metrics for the goal, then act.

Read the right metric per goal

  • Reach/growth: reach, follows from a post, shares, watch-through on Reels.
  • Resonance/value: saves and shares (strongest value signals on Meta), comments.
  • Retention: Reel average watch time / replays; carousel swipe-through; Story completion + tap-backs.
  • Action: profile visits, link taps, DMs.

Process

  1. Use the creator's real numbers (supplied or pulled via the Meta connector) - don't invent benchmarks.
  2. Find patterns: which hooks/formats/topics over-index on saves/shares/retention? Where do people drop?
  3. Recommend: do more of what works (format/hook/topic), fix the drop-off point, stop what underperforms.
  4. One experiment for next week (e.g. test a new hook style on Reels).

Output

  • A short read: top performers + why, the drop-off insight, 3 concrete next actions, 1 experiment. Cite the actual numbers; mark anything missing [verify].

Guardrails

  • No fabricated benchmarks or made-up metrics; work from real data (truth-telling).
  • Don't over-read tiny samples; note when data is thin.

Download now (ZIP)

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  • It worked
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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.

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