multiAI Summary Pending

KPI Tracker Skill

Track, analyze, and report on Key Performance Indicators for any business.

3,556 stars

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/afrexai-kpi-tracker/SKILL.md --create-dirs "https://raw.githubusercontent.com/openclaw/skills/main/skills/1kalin/afrexai-kpi-tracker/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/afrexai-kpi-tracker/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How KPI Tracker Skill Compares

Feature / AgentKPI Tracker SkillStandard Approach
Platform SupportmultiLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Track, analyze, and report on Key Performance Indicators for any business.

Which AI agents support this skill?

This skill is compatible with multi.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# KPI Tracker Skill

Track, analyze, and report on Key Performance Indicators for any business.

## What It Does

When activated, this skill helps you:
- Define and categorize KPIs (revenue, ops, marketing, customer success)
- Set targets and thresholds (green/yellow/red)
- Generate weekly/monthly KPI reports in markdown
- Flag KPIs that are off-track with root cause prompts
- Store historical data in a simple JSON file for trend analysis

## Usage

Tell your agent: "Track these KPIs" or "Give me a KPI report" or "Which metrics are off track?"

### Setup

Create `kpi-config.json` in your workspace:

```json
{
  "kpis": [
    {
      "name": "Monthly Recurring Revenue",
      "category": "revenue",
      "unit": "$",
      "target": 50000,
      "redBelow": 35000,
      "yellowBelow": 45000
    },
    {
      "name": "Customer Churn Rate",
      "category": "customer",
      "unit": "%",
      "target": 3,
      "redAbove": 7,
      "yellowAbove": 5
    }
  ]
}
```

### Recording Data

Say: "Record MRR at $42,000 for this week"

The agent stores entries in `kpi-data.json`:
```json
{
  "entries": [
    { "kpi": "Monthly Recurring Revenue", "value": 42000, "date": "2026-02-13", "note": "Post-launch week" }
  ]
}
```

### Reports

Say: "KPI report" and the agent generates a formatted status board:

```
šŸ“Š KPI Report — Week of Feb 10, 2026

🟢 Monthly Recurring Revenue: $48,200 (target: $50,000) — 96.4%
šŸ”“ Customer Churn Rate: 8.1% (target: 3%) — needs attention
🟔 Lead Conversion Rate: 11% (target: 15%) — trending up from 9%

āš ļø Action needed on 1 red, 1 yellow KPI
```

### Trend Analysis

Say: "Show MRR trend" — the agent reads historical entries and summarizes direction, velocity, and whether you'll hit target at current pace.

## How the Agent Should Behave

1. Read `kpi-config.json` for KPI definitions
2. Read/write `kpi-data.json` for historical values
3. When asked for a report: calculate status for each KPI, format with color indicators
4. When a KPI is red: proactively suggest investigation areas
5. When recording: validate the value makes sense (e.g., churn can't be negative)

## File Locations

- Config: `kpi-config.json` (workspace root or custom path)
- Data: `kpi-data.json` (same directory as config)
- Reports: generated on-demand, optionally saved to `reports/kpi-YYYY-MM-DD.md`

## Pro Tip

Pair this with a cron job to generate weekly KPI reports automatically. For deeper business intelligence and pre-built industry KPI templates, check out [AfrexAI Context Packs](https://afrexai-cto.github.io/context-packs/) — drop-in configurations that include KPI frameworks for 10+ industries.