a776744889
- Rust workspace with stemedb-core crate - Full .claude/ configuration (agents, skills, commands, guides) - ai-lookup/ for token-efficient fact storage - Quality gates: clippy, fmt, jscpd duplication detection - Pre-commit hook with 5-phase quality checks - CLAUDE.md router and CODING_GUIDELINES.md standards Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
45 lines
1.2 KiB
Markdown
45 lines
1.2 KiB
Markdown
---
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name: stemedb-lens
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description: Guidelines for implementing Lenses (Resolution Logic). Use when working on query resolution or ranking.
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---
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# StemeDB Lens Implementation
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## Identity
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You are building the **Cortex** of Episteme. Lenses allow us to view a contradictory reality and get a deterministic answer.
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## The Lens Trait
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```rust
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pub trait Lens {
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/// Resolve a set of conflicting assertions into a single result (or detailed breakdown).
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fn resolve(&self, candidates: &[Assertion], context: &QueryContext) -> LensResult;
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}
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```
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## Principles
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* **Stateless**: Lenses should generally be stateless (logic only).
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* **Deterministic**: Same input + Same context = Same output.
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* **Fast**: This runs on every read. Avoid allocations.
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## Common Strategies
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### 1. Recency (LWW)
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Sort by `timestamp` descending. Return first.
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### 2. Consensus (Voting)
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Map `object` -> `count`. Return object with `max(count)`.
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### 3. Weighted Authority
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Map `object` -> `sum(agent.reputation)`. Return object with `max(score)`.
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## Do
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* Handle empty candidate sets gracefully.
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* Implement `Default` for standard lenses.
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* Document the ranking logic clearly.
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## Do Not
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* Perform I/O inside a Lens (no DB lookups). Lenses operate on *fetched* candidates.
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