Boost is better modeled as incentive infrastructure than as a single quest product. The docs now distinguish One-Time Actions from Time-Based Incentives, both of which sit under the Boost umbrella.
The protocol’s reusable mechanism is modular campaign composition: action definition, qualification / validation, reward design, budget control, eligibility gating, and distribution / claim flows are separable components.
The V2 architecture shows that offchain validation and signature generation are core parts of the system, not incidental implementation details. This makes validator policy and signer operation important power centers.
The Time-Based Incentives docs show Boost extending from action bounties into retention and liquidity-shaping infrastructure, where reward flows depend on position size and time held rather than a single qualifying event.
The getting-started docs reveal an important live operational constraint: new actions are reviewed by the Boost team before use, so real deployment authority is narrower than a naive “permissionless incentives protocol” reading would suggest.
The rabbit hole consumer surface is part of the mechanism because discovery, participation, and claims route through rabbithole.gg for end users.
Specific source signals worth retaining
Platform overview: best concise framing of the two-product model and the role of rabbithole.gg.
V2 architecture page: strongest explanation of the proof path from external action to offchain validation to onchain verification and reward distribution.
Repository README: best view of the protocol’s modular pieces and how campaigns compose them.
One-Time Actions setup docs: useful evidence of current review / approval workflows and the human gate around action creation.
Time-Based Incentives docs: best explanation of Boost’s move from discrete actions toward continuous retention incentives.
Why this entry belongs in the corpus
It improves the corpus’s coverage of behavioral-incentive infrastructure and gives a modular comparison class for quests, liquidity mining, governance mining, and sponsored user-acquisition campaigns.
It is especially useful alongside Royco Protocol because both turn desired user behavior into explicit incentive objects, but through different market structures and control planes.
The reusable insight is that incentive systems are governed less by reward token choice than by who defines qualifying behavior, who validates completion, who gates eligibility, who funds the budget, and who owns user discovery and claims.