> For the complete documentation index, see [llms.txt](https://mevcapital.gitbook.io/vaults-protocol/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://mevcapital.gitbook.io/vaults-protocol/methodology-and-curation-1.md).

# Methodology & Curation

MEV Capital’s restaking curation relies on a blend of on-chain data, simulation models, and manual risk oversight. The goal is to maintain vault performance without exposing users to asymmetric downside.

**1. Network Assessment** Each restakable network is evaluated based on:

* **Slashability**: Is slashing enabled? What are historical failure modes?
* **Reward Structure**: APY, point multipliers, and payout mechanisms
* **Maturity & Governance**: Upgrade history and response time under stress

High-risk networks undergo stress tests and simulations. Allocation to these networks is limited or excluded if risk/return fails threshold criteria.

**2. Operator Evaluation** Operators are scored through:

* Historical uptime and downtime data
* Participation across L1 validator sets
* Implementation of slashing mitigation tools (e.g., key rotation, hooks)
* Infrastructure dispersion and geographic decentralization

Only operators meeting internal SLAs and system test integration are considered.

**3. Collateral Asset Analysis** Each LST is evaluated on:

* Liquidity depth across major DEXs
* Slippage and withdrawal simulation under pressure
* Market depth during volatile periods

Preferred assets show stable peg mechanics and deep, diverse on-chain liquidity.

**4. Dynamic Allocation and Monitoring** Vaults integrate live rebalancing triggers and telemetry:

* Auto-deallocation hooks on validator underperformance
* Monthly reallocation cycles based on updated market views
* Real-time dashboards monitoring AVS performance and reward accuracy

This framework turns passive LST exposure into an actively risk-managed, yield-optimized restaking strategy.

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