feat: drop reflection_prompt type and flag cached micro-learnings

Remove the reflection_prompt micro-learning format end-to-end: type
config, tool definition, container case, selector tile, and the
ReflectionPrompt component file. The format wasn't pulling its weight as
a learning surface.

Add a Beschikbaar badge to selector tiles whose topic already has a
published micro-learning of that type, so users know which formats open
instantly instead of triggering a fresh generation. Cached records are
fetched once per topic via the new getExistingTypesForTopic helper, and
re-fetched after a generation returns so newly-created formats light up
without a manual refresh.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

src/lib/llmTools.js

@@ -418,15 +418,3 @@ export const EMIT_FLASHCARD_SET_TOOL = {
   },
 };
 
-export const EMIT_REFLECTION_PROMPT_TOOL = {
-  name: 'emit_reflection_prompt',
-  description: 'Return an open-ended reflection question that asks the employee to connect the topic to their own professional experience, plus a model answer showing the expected depth and specificity.',
-  input_schema: {
-    type: 'object',
-    properties: {
-      prompt: { type: 'string', description: 'An open-ended question that cannot be answered with a fact. It must require the employee to think about their own context.' },
-      model_answer: { type: 'string', description: 'An example of a thoughtful, specific response (3–5 sentences). This is not a rubric — it illustrates depth.' },
-    },
-    required: ['prompt', 'model_answer'],
-  },
-};

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.
@@ -126,6 +116,23 @@ export async function regenerateMicroLearning(topicId, type) {
   return getOrGenerateMicroLearning(topicId, type);
 }
 
+/**
+ * Return the set of micro-learning types that already have a published
+ * record for the given topic. Used by the selector UI to flag formats
+ * that are ready to read instantly (no generation latency).
+ */
+export async function getExistingTypesForTopic(topicId) {
+  try {
+    const records = await pb.collection('micro_learnings').getFullList({
+      filter: `topic_id = "${topicId}" && status = "published"`,
+      fields: 'type',
+    });
+    return new Set(records.map((r) => r.type));
+  } catch {
+    return new Set();
+  }
+}
+
 /**
  * Delete all cached micro learnings for a topic (all types).
  */