Earned vs. Burned, Claude skill for measuring AI delivery value (not tokens)

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GitHub - harveer10x/earned-vs-burned-skill: A Claude skill for measuring AI delivery value. Replaces tokens, story points & velocity with one honest metric: outcomes earned vs effort burned. · GitHub

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Earned vs Burned — Claude Skill

An open source Claude skill for measuring what actually matters in AI-native delivery.

By Harveer Singh | Author, When Data Moves | Founder, Rizz Wireless

The Problem

Every team building with AI is measuring the wrong things.

Token usage. Lines of code. Story points. Velocity. Hours burned. These are burned metrics — they measure effort, not value. A model that hallucinates in 100 tokens is not better than one that solves the problem in 10,000. 500 lines of code that don't ship earn nothing. 40 story points closed on work that never reaches production delivers zero business value.

The only measure that matters: did we earn it?

The Framework

Value is only EARNED when a tangible, verifiable business outcome is achieved. Until that point, all effort is only BURNED .

This is the Earned vs Burned Framework — originally built at Deloitte in 2010 for a Sysco Foods data factory processing 1M+ SKUs (a human-AI hybrid workflow that predated the term). Now generalised for AI-native software delivery.

The Outcome Hierarchy

Level<br>Gate<br>Earned<br>What it means

Not Started<br>0.00<br>Backlog. Zero burn, zero earn.

In Progress<br>0.00<br>Effort accumulating. Still zero earned.

Dev Complete<br>0.25<br>Code written and unit-tested. Partial.

QA Passed<br>0.60<br>Tested and accepted. Meaningful but not in prod.

Deployed to Prod<br>0.85<br>Running in production. Close — but not confirmed.

Outcome Verified<br>1.00<br>KPI moved. User confirmed. Revenue impacted. The only full earn.

The Key Metrics

Metric<br>Formula<br>Target

Earn Rate %<br>L5 outcomes ÷ Total stories<br>70%+

Earned / Hours<br>Total Earned ÷ Total Hours<br>>0.10

Earned per AI Token<br>Total Earned ÷ Total Tokens<br>Trending up

Outcome Verification %<br>L5 verified ÷ L4+L5 deployed<br>Trending up

Earned per AI Token is the metric the industry doesn't have yet. It replaces token volume entirely.

What This Skill Does

Install this skill into Claude and it can:

Pull tasks from any project tool — Linear, Asana, GitHub Issues, Jira, Azure DevOps, or a pasted/CSV list

Score each task against the 5-level Outcome Hierarchy

Calculate all metrics — Earn Rate, E/H Ratio, Earned per Token, Outcome Verification %, Team Effectiveness

Generate an Earned Value Report — one page, three numbers, replaces your velocity report

Coach your team — ends every report with the question that changes delivery culture: what is the verifiable outcome that will confirm this work is done?

Works for FTE teams, outsourced/offshored delivery, AI-agent workflows, or any mix.

Installation

Option 1 — Install the .skill file (Claude Desktop / Cowork)

Download earned-vs-burned.skill from the Releases page

In Claude Desktop or Cowork: Settings → Capabilities → Install Skill

Drop the .skill file in

Option 2 — Manual install (Claude Code)

# Clone the repo<br>git clone https://github.com/[your-org]/earned-vs-burned-skill

# Point Claude at the skill directory<br># Add to your .claude/settings.json:<br># "skillPaths": ["./earned-vs-burned"]

Usage Examples

Once installed, just talk to Claude naturally:

"Score my Linear sprint against the Earned vs Burned framework"

"Here are my Jira tasks — how much value did we actually earn this sprint?"

"We burned 400 hours and 2M tokens this month. What did we earn?"

"I'm an outsourcing vendor — help me prove our value to the client using outcome metrics"

"Pull our GitHub issues and give me an Earned Value Report"

Repository Structure

earned-vs-burned/<br>├── SKILL.md # Core skill...

earned skill burned claude value earn

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