AVS scoring: professional operator TVL vs community TVL

The problem: yield without a risk label

Restaking lets a staker reuse staked ETH (or a liquid staking token) to secure additional services. In EigenLayer’s vocabulary those services are Actively Validated Services, or AVSs. Operators run the software for an AVS, and stakers delegate to operators. In return the staker may earn extra rewards. In exchange, the staker accepts extra conditions under which part of the stake can be penalised, which is slashing.

The difficulty is that each AVS is a different piece of software with a different task, a different team and a different failure mode. A staker choosing between them has no standard rating. We believe AVSs are poised to become the linchpin of yields, which makes evaluating them essential. Our answer is a risk score for every AVS shown in the AutoLayer interface.

The two inputs

We define the score through two measurements. Both are about how much money is backing the AVS, and by whom.

Metric	What it measures	What it tells you
Professional Operators TVL	Amount staked on the AVS by institutional and professional operators. Examples include P2P, Ankr and Nethermind.	Whether firms that run infrastructure for a living have put capital behind the service.
Community TVL	Amount of retail money backing the AVS.	How much support the AVS has gathered from individual stakers.

The first metric gives a risk profile based on factual data from funds deposited by professionals, rather than subjective opinions. The second metric adds the retail side to complete the profile. Sudden changes in these scores also help users stay informed.

Why the split can carry information

Total value locked on an AVS is a single number. Splitting it in two says more.

Professional stake as a signal of diligence

Operators that run validators for a living can lose revenue and reputation if an AVS misbehaves. When such an operator commits stake to a new service, it implies someone with technical knowledge looked at it. That is not proof of safety. Professionals make mistakes, and they can also follow incentives that retail users do not see. But a service with no professional stake at all is at least a different category of risk from one with several established operators.

Community stake as a signal of reach, and of herd behaviour

Retail money can arrive fast, especially when rewards are advertised. Large community TVL with little professional TVL is a pattern worth noticing: it may mean strong marketing, or it may mean capital chasing yield without scrutiny. Read the other way, strong professional TVL with a small community TVL can mean an early service that professionals trust before the public has found it.

The ratio and the movement

The score is built on the two inputs, and movement matters as much as level. A score that drops because professional operators withdraw is more informative than a static label.

An example

Take two AVSs, A and B, each with the same total stake. For example, A has most of its stake from three well-known operators and a small amount from retail stakers. B has almost no professional stake and a large retail base drawn in by high advertised rewards.

On a TVL ranking alone, A and B look identical. With the two-metric view, they look different at a glance: B is backed mainly by people who may not have reviewed the service closely, and if rewards drop, that stake could leave quickly. A is not safe because of its operators, but the evidence behind it is of a different kind. That difference in evidence is what the score is meant to surface.

What a score cannot do

A stake-based score has clear limits, and users should hold them in mind:

1. It does not audit code. Nothing in the two metrics reads the AVS’s contracts or slashing conditions.
2. It measures money, not correctness. A heavily staked service can still be flawed.
3. Professional is a classification. The score relies on classifying operators as professional, so that list needs to be kept current.
4. It describes the past. Stake can leave as quickly as it arrived.

A transparent, data-based first filter is useful in a market where most decisions are made from a Twitter post. It is a starting point for your own research, not a guarantee.

How it fits with the rest of the V2 plan

AVS scoring is one of four V2 features, alongside LRT scoring, compounding and liquification, and slashing bounties. They address different layers of the same risk:

• AVS scoring looks at the services being secured. This article.
• LRT scoring looks at the liquid restaking token itself and how it is reused as collateral.
• Slashing bounties aim to halt an AVS before a slashing event reaches stakers.

Our Vault Manager lets each AutoLayer Operator validate its own set of AVSs, producing vaults characterized by varied yields and risk profiles. A score would give that choice a basis. Read more in slashing bounties and Virtual Operators.

How to use the idea today

You do not need a product to apply the logic. When comparing any AVS, a few questions follow directly from the two metrics:

1. Which operators are running it, and are they names you can verify as established firms?
2. How much of the stake comes from them versus the general public?
3. Has either number changed sharply in recent weeks, and why?
4. What exactly can be slashed, and under which conditions?

The fourth question is the one a stake score cannot answer for you. Read the AVS’s own documentation and the risk disclosure before committing funds. For the wider design of the product, see how AutoLayer works and the security page.

Takeaway

Splitting AVS stake into professional and community TVL turns one blunt number into two readable signals. The approach is simple, based on observable on-chain data, and clear about being a risk indicator rather than a verdict. For our wider background, read From Tortle Ninja to AutoLayer.