What is prove-it-better?

Claude Code skill: make anything better and prove it before it ships — blind two-order judging, hard checks, reversible rollout. From the method behind harborseo.ai.. It is open-source on GitHub.

Is prove-it-better free to use?

prove-it-better is open-source under the MIT license, so it is free to use.

What category does prove-it-better belong to?

prove-it-better is listed under content in the Claudeers registry of Claude-compatible tools.

Platforms	cli, api
Operating systems	—
AI compatibility	claude
License	MIT
Pricing	open-source
Language	Python

prove-it-better

A Claude Code skill for making anything better, and proving it before it ships.

Ask Claude to "make it better" and it usually rewrites things from a best-practice list, then tells you it's better. This skill makes it prove it instead. The current version is the opponent. A change only ships once it beats the current version on real cases, judged blind with the order swapped, with hard checks. It ships behind a switch you can flip back, and Claude confirms it live.

It works for AI features, agents, prompts and model swaps, and for emails, landing and pricing pages, processes and cloud bills.

Install

Run this in your terminal:

git clone https://github.com/IncomeStreamSurfer/prove-it-better.git ~/.claude/skills/prove-it-better

That's it. Claude Code finds the skill automatically. To install it for one project only, clone into that project's .claude/skills/prove-it-better instead. To update:

git -C ~/.claude/skills/prove-it-better pull

Use it

Just ask for an improvement. The skill triggers on requests like:

"Our AI product description feature is meh — make it better."
"Swap our summariser to a cheaper model without making it worse."
"Our pricing page isn't converting. Improve it."
"Our cloud bill is too high. Bring it down."
"Rewrite our onboarding emails so more people activate."

What Claude does

Phase	What happens
0. Access first	Checks the project, CLIs, connectors and secrets (names only), then asks for everything missing in one message: data access, a judge model key, a safe environment, and what's off-limits.
1. Research	Usage data, complaints, past attempts, the current implementation, 10–30 real cases, what "better" means, and baseline numbers. Shows you before building.
2. Plan	Splits the work into units that win or lose independently, and writes the ship gate before any results exist.
3. Build behind a switch	The challenger is selectable per unit, with an automatic fallback to the current version.
4. Prove it	Blind pairwise judging, every pair judged twice with the order swapped, by a judge from a different vendor than the contestants, plus hard checks. For costs, the numbers decide. For clicks and sign-ups, a live A/B test decides.
5. Iterate	Fixes the judge's repeated defects, confirms on a fresh sample, and keeps the current version when it wins.
6. Ship reversibly	Only with your go-ahead, behind the switch.
7. Verify live	Confirms the new version is what's actually running, then compares the live metric with the baseline.
8. Report	A scoreboard per unit, what didn't ship and why, how to roll back. Measured claims only.

Does the skill itself work?

It was tested the same way it tests things. An agent got three "make it better" requests (an AI feature, a pricing page and a cloud bill), three times each, with and without the skill:

Behaviour	Without the skill	With the skill
Sorts out access before starting	2 / 9	9 / 9
Researches before changing anything	4 / 9	9 / 9
Compares against the current version blind	0 / 9	✓ where a judge applies
Swaps the order to remove judge bias	0 / 9	✓ where a judge applies
Uses an independent judge	0 / 9	✓ where a judge applies
Verifies the result live	0 / 9	9 / 9
Willing to keep the current version if it wins	0 / 9	9 / 9

For the cloud bill, the skill correctly skipped the AI judge and let the hard numbers decide.

Where it came from

This is the method used to rebuild every AI agent inside Harbor, my AI SEO content generator. Each new agent ran against the old one on real customer sites and real production jobs, judged blind in both orders. Most won 10–0 or 11–0. A few first versions lost and were fixed before shipping, and the ones that never beat the old version weren't shipped at all.

If you want SEO articles researched from real Google results and your own site, try harborseo.ai.

Scripts

Python 3. The judge needs the SDK for whichever vendor judges (pip install anthropic or pip install openai) and that vendor's API key in your environment. The other two scripts use only the standard library.

Script	What it does
`scripts/judge.py`	Blind, two-order pairwise judge, current vs challenger, with Claude or OpenAI as the judge. JSONL in, verdicts out.
`scripts/scoreboard.py`	W–L–T overall and per criterion, the most repeated defects, and a suggested decision.
`scripts/checks.py`	Hard checks: do the links load, and do quotes appear verbatim in the source.

python scripts/judge.py cases.jsonl --out verdicts.jsonl --provider anthropic --criteria criteria.json --task "Write a product description from the product facts."
python scripts/scoreboard.py verdicts.jsonl
python scripts/checks.py links output.txt

Run any script with --help for the input format.

Files

SKILL.md                 the method Claude follows
references/judging.md    writing criteria, bias controls, sample sizes
references/adapting.md   using it for copy, marketing, code, costs and processes
templates/tracker.md     unit-by-unit tracker
templates/report.md      results report
scripts/                 judge, scoreboard, hard checks

License

MIT

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