chore: install openagent opencode

Signed-off-by: Dmytro Stanchiev <git@dmytros.dev>

.opencode/context/development/ai/mastra-ai/concepts/evaluations.md

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+<!-- Context: development/evaluations | Priority: critical | Version: 1.0 | Updated: 2026-02-15 -->
+
+# Concept: Mastra Evaluations
+
+**Purpose**: Quality assurance and scoring for LLM outputs.
+
+**Last Updated**: 2026-01-09
+
+---
+
+## Core Idea
+Evaluations in Mastra use Scorers to assess the quality, accuracy, and safety of LLM-generated content. They provide a quantitative way to measure performance and detect issues like hallucinations or factual errors.
+
+## Key Points
+- **Scorers**: Specialized functions that take LLM output (and optionally ground truth) and return a score (0-1).
+- **Integration**: Registered in the Mastra instance and can be triggered automatically during workflow execution.
+- **Metrics**: Common metrics include hallucination detection, fact validation, and relevance scoring.
+- **Audit Trail**: Scorer results are stored in the `mastra_scorers` table for long-term analysis and reporting.
+
+## Quick Example
+```typescript
+// Scorer definition
+export const hallucinationDetector = new Scorer({
+  id: 'hallucination-detector',
+  description: 'Detects hallucinations in LLM output',
+  execute: async ({ output, context }) => {
+    // Logic to detect hallucinations
+    return { score: 0.95, rationale: 'No hallucinations found' };
+  },
+});
+
+// Registration
+export const mastra = new Mastra({
+  scorers: { hallucinationDetector },
+});
+```
+
+**Reference**: `src/mastra/scorers/`, `src/mastra/evaluation/`
+**Related**:
+- concepts/core.md
+- concepts/workflows.md