agentleFS
gokulsvisiongokulsvision/Crewm8-Social-Media-Manager-Skill-Graph/skills/analytics/performance-report/SKILL.md

performance-report

Skill17 starsChanged 7 months agoMITView on GitHub

Generate weekly and monthly performance summary reports — top/bottom posts, content type analysis, pillar performance, follower growth, conversion tracking, and actionable recommendations.

9 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

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

  • 0

    Common

    Most of the 8 edited versions we read keep these.

  • 2

    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:

    Update always-on learnings doc

    line 79
  2. Distinctive:

    Always-on learnings doc is updated

    line 141

“Common” is measured: kept by at least 6 in 10 of the 8 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

Performance Report

Generate structured weekly and monthly performance reports that turn raw metrics into actionable insights. The report answers three questions: what happened, why it happened, and what we should do differently.

Purpose

A performance report that nobody reads is worse than no report at all. This skill produces reports designed for one specific reader — the founder — and answers exactly what they need to know in the order they need to know it.

When to Use

  • Weekly: Monday morning review of previous week
  • Monthly: First week of month — trends across 4 weeks
  • Campaign post-mortem: within 48 hours of campaign end
  • Founder asks "how's social performing?"

Inputs Required

  • Metrics data for the period (from metrics-track)
  • A/B test results (from ab-test-analyze)
  • Content calendar (planned vs actual)
  • Any external events that might have influenced performance

Quick Reference

Report TypeFrequencyKey SectionsAudience
Weekly SnapshotEvery MondayTop/bottom posts, metrics summary, 1 insight, 1 actionFounder + team
Monthly Deep-DiveFirst week of monthTrends, format analysis, growth trajectory, forecastFounder + strategy
Campaign Post-MortemAfter campaignGoals vs actuals, lessons, format winnersFounder + team

Procedure

  1. Gather data: metrics, A/B test results, competitor benchmarks, content calendar.
  2. Build metrics summary:
    • Total posts per platform
    • Total impressions
    • Average ER per platform
    • Follower growth (net + growth rate)
    • Link clicks / conversions
    • Compare to previous period
  3. Identify top 3 and bottom 3 posts:
    • What made them work/fail (hook, format, timing, topic)?
    • Include the defining metric
  4. Analyze by content type:
    • Threads vs short posts vs carousels vs videos — which format wins?
    • Has the winning format changed?
  5. Analyze by content pillar:
    • Which pillars drive engagement vs conversions?
    • Is the pillar mix matching strategy?
  6. Track conversion funnel: Impressions → engagement → profile visits → link clicks → signups.
  7. Extract 3-5 key learnings:
    • What worked (do more)
    • What didn't (stop doing)
    • What to test next
    • Update always-on learnings doc
  8. Provide 2-3 actionable recommendations:
    • Specific and testable (not "post more")
    • "Increase thread cadence from 2/week to 3/week targeting Tue/Thu mornings"

Output Format

# [Weekly / Monthly] Report: [Period]

## Executive Summary
[3-4 sentences — founder reads this in 30 seconds]

## Metrics at a Glance
| Metric | This Period | Last Period | % Change |
|--------|-----------|------------|----------|
| Total posts | [N] | [N] | [+/-N]% |
| Total impressions | [N] | [N] | [+/-N]% |
| Avg ER (X) | [N]% | [N]% | [+/-N]% |
| Avg ER (LinkedIn) | [N]% | [N]% | [+/-N]% |
| Follower growth | [+N] | [+N] | [+/-N]% |
| Link clicks | [N] | [N] | [+/-N]% |

## Top 3 Posts
### 1. [Title]
**Platform:** [X / LinkedIn / IG / TikTok] | **Format:** [Type]
**Impressions:** [N] | **ER:** [N]% | **Why it worked:** [Analysis]
### 2-3...

## Bottom 3 Posts
### 1. [Title]
**Impressions:** [N] | **ER:** [N]% | **Why it underperformed:** [Diagnosis]

## Format Analysis
| Format | Posts | Avg ER% | vs Overall |
|--------|-------|---------|-----------|
| Thread | [N] | [N]% | [+/-N]% |
...

## Pillar Performance
| Pillar | Posts | Avg ER% | Top Post |
|--------|-------|---------|----------|
...

## Key Learnings
1. [Specific learning with supporting data]
2-3...

## Recommendations
1. [Specific, actionable recommendation]
2-3...

Done Criteria

The skill is complete when:

  1. Executive summary is written (founder can read in 30 seconds)
  2. Top and bottom 3 posts are identified with analysis
  3. Format and pillar analysis are included
  4. 3-5 key learnings are extracted
  5. 2-3 actionable recommendations are provided
  6. Always-on learnings doc is updated

Pitfalls

  • Data dump without insight (metrics without interpretation = noise)
  • Comparing platforms naively (X ER != LinkedIn ER)
  • Highlighting problems without solutions
  • Cherry-picking favorable comparisons
  • Report so long nobody reads it

Verification

Can the founder read the executive summary and know whether it was a good week? Are there at least 2 actionable next steps? Does this report make the next content calendar smarter than the last one?

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.