feat: implement micro learning generation service with cached LLM content delivery and UI components

src/lib/microLearningService.js

@@ -15,7 +15,6 @@ import {
   EMIT_CONCEPT_EXPLAINER_TOOL,
   EMIT_SCENARIO_QUIZ_TOOL,
   EMIT_FLASHCARD_SET_TOOL,
-  EMIT_REFLECTION_PROMPT_TOOL,
 } from './llmTools';
 import * as db from './db';
 
@@ -53,15 +52,6 @@ Mix three question types:
   - Relationships: "How does X relate to Y?"
 Keep answers concise — one or two sentences maximum.`,
   },
-  reflection_prompt: {
-    tool: EMIT_REFLECTION_PROMPT_TOOL,
-    tier: 'fast',
-    maxTokens: 1024,
-    instructions: `Generate a reflection prompt.
-The question must be open-ended and cannot be answered with a fact.
-It must require the employee to think about their own professional context — their team, their role, their past experience.
-The model answer should show depth and specificity (3–5 sentences). It is not a rubric — it is an example of thoughtful reflection.`,
-  },
 };
 
 const SYSTEM_PROMPT = `You are an expert learning content writer for Respellion, an internal IT company.
@@ -145,6 +135,21 @@ export async function deleteAllForTopic(topicId) {
   }
 }
 
+// ── Public helpers ────────────────────────────────────────────────────────────
+
+/**
+ * Check which micro learning types already exist for a given topic.
+ * Returns an object like { concept_explainer: true, scenario_quiz: false, ... }
+ */
+export async function getExistingTypes(topicId) {
+  const types = Object.keys(MICRO_LEARNING_TYPES);
+  const result = {};
+  for (const type of types) {
+    result[type] = !!(await findExisting(topicId, type));
+  }
+  return result;
+}
+
 // ── Internal helpers ──────────────────────────────────────────────────────────
 
 async function findExisting(topicId, type) {