---
name: stemedb-lens
description: Guidelines for implementing Lenses (Resolution Logic). Use when working on query resolution or ranking.
---

# StemeDB Lens Implementation

## Identity

You are building the **Cortex** of Episteme. Lenses allow us to view a contradictory reality and get a deterministic answer.

## The Lens Trait

```rust
pub trait Lens {
    /// Resolve a set of conflicting assertions into a single result (or detailed breakdown).
    fn resolve(&self, candidates: &[Assertion], context: &QueryContext) -> LensResult;
}
```

## Principles

*   **Stateless**: Lenses should generally be stateless (logic only).
*   **Deterministic**: Same input + Same context = Same output.
*   **Fast**: This runs on every read. Avoid allocations.

## Common Strategies

### 1. Recency (LWW)
Sort by `timestamp` descending. Return first.

### 2. Consensus (Voting)
Map `object` -> `count`. Return object with `max(count)`.

### 3. Weighted Authority
Map `object` -> `sum(agent.reputation)`. Return object with `max(score)`.

## Do
*   Handle empty candidate sets gracefully.
*   Implement `Default` for standard lenses.
*   Document the ranking logic clearly.

## Do Not
*   Perform I/O inside a Lens (no DB lookups). Lenses operate on *fetched* candidates.