agentleFS
Sign inStart free
posteverywhereposteverywhere/cursor-plugin/skills/review-post-performance/SKILL.md

review-post-performance

Skill0 starsChanged 28 days agoMITView on GitHub

Review how social media posts performed with PostEverywhere analytics. Use when the user asks how their posts did, what performed best, which platform works, or wants a weekly or monthly social media report.

2 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

4 rules. About 0 are common, 2 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    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:

    Pick the period: Default to the last 30 days.

    line 12
  2. Very specific:

    Find the posts: Call list_posts_advanced with status: "published" and published_after for the same period, sorted by published_at.

    line 13
  3. Distinctive:

    Write the report: Keep it short:

    line 15
  4. Distinctive:

    Don't compare the user's numbers with other people's or invent benchmarks.

    line 24

≈ “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

Review post performance

Give the user a short, honest read of how their posts performed, and one or two concrete next steps.

Steps

  1. Pick the period. Default to the last 30 days. Call get_analytics_summary with period (week, month, or all), or with from and to dates.
  2. Find the posts. Call list_posts_advanced with status: "published" and published_after for the same period, sorted by published_at.
  3. Look closer where it matters. For the best and worst few posts, call get_post_results to see each platform's outcome and link.
  4. Write the report. Keep it short:
    • Totals for the period: posts published and engagement, from the summary.
    • The best posts and what they have in common (format, topic, platform, time of day).
    • Anything that failed to publish, and whether it needs fixing (see fix-failed-posts).
    • One or two suggestions the user can act on this week.

Be accurate

  • Some platforms don't share every number (for example, some don't report views for all account types). Say "not available" rather than zero.
  • Don't compare the user's numbers with other people's or invent benchmarks.
  • A small number of posts can't show a trend. Say so when the sample is small.

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