Add UI/logical temporal ordering feature

Query validation

README.md

@@ -1,19 +1,28 @@
-# kongruity app
+# kongruity
 
-kongruity employs Large Language Model ("LLM") semantic grouping functionality to cluster large volumes of "to dos" or issue tags in development (or other) settings, according to thematic or topical similarity.
+kongruity pulls in the unstructured artifacts of the creative-engineering process -- to-dos, action items, agile tickets, Jira comment threads, retrospective notes -- and synthesizes them into semantically coherent, prioritized clusters ready for implementation planning.
 
-(To learn more about this topic, see, e.g., [Kozlowski A., Boutyline A., Semantic Structure in Large Language Model Embeddings Aug. 2025, arXiv:2508.10003v1:04 Aug 2025](https://arxiv.org/html/2508.10003v1)).
+In kongruity world, these artifacts are "sticky notes." A board full of them looks chaotic. With one click, an LLM analyzes their meaning and groups them into thematic clusters, each with a descriptive header. 
 
-In the world of kongruity, these "to dos" are called "sticky notes."  kongruity's React/Vite UI views a board of seemingly chaotic "sticky notes".  But with one click, they are transformed into manageable, actionable groups, each with a header that explains the group semantic interrelation.
+An independent embedding-based evaluation scores clustering quality, so the output is data-backed. From there, teams can cimply drag-and-rank clusters by implementation priority, turning a wall of noise into an actionable workflow.
 
-The backend is an Express API that serves "sticky note" data and proxies semantic grouping requests to Antrhopic Claude.  
+## How it works
 
-Developers may feel free to install other LLM SDKs and alter the syntax at backend/services/clustering.service.js to experiment with any LLM model/platform they prefer.
+1. **Ingest** — Sticky notes are loaded and displayed on a board.
+2. **Cluster** — An LLM reads every note and groups them by semantic similarity (not keywords).
+3. **Evaluate** — In parallel, a separate embedding model (Voyage AI) generates vector representations of each note. A silhouette-based cohesion score measures how well-separated and internally consistent clusters are. The score is displayed alongside the results.
+4. **Validate** — Structural checks confirm every note is assigned to exactly one cluster, no clusters are empty, and labels are present.
+5. **Prioritize** — Clusters appear ranked and are drag-reorderable. Teams set implementation priority by dragging clusters into position.
+
+## Dev implementation note
+
+Developers may swap in other LLM SDKs/APIs and alter prompt syntax in `backend/services/clustering.service.js` to experiment with any model or platform.
 
 ## Prerequisites
 
 - Node.js (v18 or later recommended)
-- An [Anthropic API key](https://console.anthropic.com/)
+- An [Anthropic API key](https://console.anthropic.com/) (or other LLM platform, for clustering)
+- A [Voyage AI API key](https://dash.voyageai.com/) (for embedding-based evaluation)
 
 ## Setup
 
@@ -26,13 +35,16 @@ cd kongruity
 
 ### 2. Create an environment file
 
-The backend expects a `.env` file containing an Anthropic API key in the root `backend/` directory. This file is git-ignored and must be created manually:
+The backend expects a `.env` file in the `backend/` directory. This file is git-ignored and must be created manually:
 
 ```bash
-echo 'ANTHROPIC_API_KEY=<your Anthropic API key>' > backend/.env
+cat > backend/.env << 'EOF'
+ANTHROPIC_API_KEY=<your Anthropic API key>
+VOYAGEAI_API_KEY=<your Voyage AI API key>
+EOF
 ```
 
-Replace `<your Anthropic API key>` with your actual key.
+Replace placeholder values with your actual keys.
 
 ### 3. Install dependencies
 
@@ -48,7 +60,7 @@ npm install
 
 ## Run the app
 
-### Start the backend - Production Mode
+### Start the backend — Production mode
 
 From the `backend/` directory:
 
@@ -58,15 +70,15 @@ npm run start
 
 The API server starts on **http://localhost:3001** (configurable via the `PORT` environment variable).
 
-### Start the backend - Development mode
+### Start the backend — Development mode
 
-To start using Nodemon for "hot reloads," if developing your own features:
+To start using Nodemon for hot reloads while developing:
 
 ```bash
 npm run dev
 ```
 
-### Build the frontend - Production mode
+### Build the frontend — Production mode
 
 From the `frontend/` directory:
 
@@ -74,13 +86,14 @@ From the `frontend/` directory:
 npm run build
 ```
 
-### Start the frontend - Development mode
+### Start the frontend — Development mode
 
 From the `frontend/` directory:
 
 ```bash
 npm run dev
 ```
+
 The Vite dev server starts on **http://localhost:5173** by default. Open that URL in a browser.
 
 ## Running tests
@@ -92,7 +105,8 @@ From the `backend/` directory:
 ```bash
 npm test
 ```
-Backend tests use Supertest for HTTP assertions.
+
+Backend tests use Vitest with Supertest for HTTP assertions.
 
 ### Frontend tests
 
@@ -102,7 +116,7 @@ From the `frontend/` directory:
 npm test
 ```
 
-This runs `vitest run` with jsdom. For watch mode, during development:
+This runs Vitest with jsdom. For watch mode during development:
 
 ```bash
 npm run test:watch

backend/app.js

@@ -6,7 +6,6 @@ const app = express();
 
 app.use(cors());
 app.use(express.json());
-
 app.use('/v1/notes', router);
 
 app.use((req, res) => {

backend/package-lock.json

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+                "express": "^4.21.2",
+                "voyageai": "^0.1.0"
             },
             "devDependencies": {
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backend/package.json

@@ -13,7 +13,8 @@
         "@anthropic-ai/sdk": "^0.74.0",
         "cors": "^2.8.5",
         "dotenv": "^16.4.7",
-        "express": "^4.21.2"
+        "express": "^4.21.2",
+        "voyageai": "^0.1.0"
     },
     "devDependencies": {
         "nodemon": "^3.1.9",

backend/routes/notes.routes.js

@@ -3,7 +3,7 @@ import { readFile } from 'fs/promises';
 import { clusterNotes } from '../services/clustering.service.js';
 
 const router = Router();
-const DATA_PATH = '../data/notes.json';
+const DATA_PATH = new URL('../data/notes.json', import.meta.url);
 
 const loadNotes = async () => {
   const raw = await readFile(DATA_PATH, 'utf-8');
@@ -23,11 +23,11 @@ router.get('/', async (req, res) => {
 router.post('/cluster', async (req, res) => {
   try {
     const notes = await loadNotes();
-    const clusters = await clusterNotes(notes);
+    const result = await clusterNotes(notes);
-    res.json(clusters);
+    res.json(result);
   } catch (err) {
     console.error(`Clustering failed: ${err}`);
-    res.status(500).json({ error: `Clustering failed: ${err}` });
+    res.status(500).json({ error: 'Clustering failed' });
   }
 });
 

backend/services/clustering.service.js

@@ -1,4 +1,6 @@
 import Anthropic from "@anthropic-ai/sdk";
+import { embedNotes } from "./embedding.service.js";
+import { validateStructure, computeCohesionScore } from "./validation.service.js";
 
 const client = new Anthropic({
   apiKey: process.env.ANTHROPIC_API_KEY,
@@ -31,7 +33,7 @@ Here are the notes:
 ${notesJson}`;
 };
 
-export const clusterNotes = async (notes) => {
+const requestClusters = async (notes) => {
   const response = await client.messages.create({
     model: "claude-sonnet-4-20250514",
     max_tokens: 4096,
@@ -52,3 +54,20 @@ export const clusterNotes = async (notes) => {
     throw new Error('LLM API returned non-JSON response');
   }
 };
+
+export const clusterNotes = async (notes) => {
+  const [clusters, embeddingMap] = await Promise.all([
+    requestClusters(notes),
+    embedNotes(notes),
+  ]);
+
+  const noteIds = notes.map((n) => n.id);
+  const { valid, reasons } = validateStructure(clusters, noteIds);
+  if (!valid) {
+    throw new Error(`Cluster validation failed: ${reasons.join('; ')}`);
+  }
+
+  const score = computeCohesionScore(clusters, embeddingMap);
+
+  return { clusters, score: Math.round(score * 100) / 100 };
+};

backend/services/embedding.service.js

@@ -0,0 +1,25 @@
+import { VoyageAIClient } from "voyageai";
+
+const client = new VoyageAIClient({
+  apiKey: process.env.VOYAGEAI_API_KEY,
+});
+
+/**
+ * @param {Array<{id: string, text: string}>} notes
+ * @returns {Promise<Map<string, number[]>>} noteId → embedding vector
+ */
+export const embedNotes = async (notes) => {
+  const texts = notes.map((n) => n.text);
+
+  const response = await client.embed({
+    input: texts,
+    model: "voyage-3-lite",
+  });
+
+  const embeddingMap = new Map();
+  response.data.forEach((item, i) => {
+    embeddingMap.set(notes[i].id, item.embedding);
+  });
+
+  return embeddingMap;
+};

backend/services/validation.service.js

@@ -0,0 +1,129 @@
+/**
+ * Structural validation: confirms the LLM output is well-formed
+ * before it reaches the frontend.
+ *
+ * @param {Array<{label: string, noteIds: string[]}>} clusters
+ * @param {string[]} inputNoteIds - the original note IDs that were sent to the LLM
+ * @returns {{valid: boolean, reasons: string[]}}
+ */
+export const validateStructure = (clusters, inputNoteIds) => {
+  const reasons = [];
+
+  if (!Array.isArray(clusters) || clusters.length === 0) {
+    return { valid: false, reasons: ['Response is not a non-empty array'] };
+  }
+
+  const assignedIds = [];
+  for (const cluster of clusters) {
+    if (!cluster.label || typeof cluster.label !== 'string') {
+      reasons.push(`Cluster missing a valid label`);
+    }
+    if (!Array.isArray(cluster.noteIds) || cluster.noteIds.length === 0) {
+      reasons.push(`Cluster "${cluster.label ?? '(unlabeled)'}" has no noteIds`);
+    }
+    assignedIds.push(...(cluster.noteIds ?? []));
+  }
+
+  const inputSet = new Set(inputNoteIds);
+  const assignedSet = new Set(assignedIds);
+
+  if (assignedIds.length !== assignedSet.size) {
+    reasons.push('One or more notes appear in multiple clusters');
+  }
+
+  const missing = inputNoteIds.filter((id) => !assignedSet.has(id));
+  if (missing.length > 0) {
+    reasons.push(`Notes missing from clusters: ${missing.join(', ')}`);
+  }
+
+  const extra = assignedIds.filter((id) => !inputSet.has(id));
+  if (extra.length > 0) {
+    reasons.push(`Unknown noteIds in clusters: ${[...new Set(extra)].join(', ')}`);
+  }
+
+  if (clusters.length > inputNoteIds.length) {
+    reasons.push(`More clusters (${clusters.length}) than notes (${inputNoteIds.length})`);
+  }
+
+  return { valid: reasons.length === 0, reasons };
+};
+
+const cosineSimilarity = (a, b) => {
+  let dot = 0;
+  let magA = 0;
+  let magB = 0;
+  for (let i = 0; i < a.length; i++) {
+    dot += a[i] * b[i];
+    magA += a[i] * a[i];
+    magB += b[i] * b[i];
+  }
+  const denom = Math.sqrt(magA) * Math.sqrt(magB);
+  return denom === 0 ? 0 : dot / denom;
+};
+
+/**
+ * Computes a silhouette-style cohesion score for the clustering.
+ *
+ * For each note, measures how much more similar it is to its own cluster
+ * versus the nearest neighboring cluster. Returns a score in [-1, 1]
+ * where higher is better.
+ *
+ * @param {Array<{label: string, noteIds: string[]}>} clusters
+ * @param {Map<string, number[]>} embeddingMap - noteId → vector
+ * @returns {number} average silhouette score
+ */
+export const computeCohesionScore = (clusters, embeddingMap) => {
+  if (clusters.length <= 1) return 1.0;
+
+  const scores = [];
+
+  for (let ci = 0; ci < clusters.length; ci++) {
+    const clusterIds = clusters[ci].noteIds;
+    if (clusterIds.length <= 1) {
+      scores.push(0);
+      continue;
+    }
+
+    for (const noteId of clusterIds) {
+      const vec = embeddingMap.get(noteId);
+      if (!vec) continue;
+
+      // a(i): avg distance to other notes in same cluster
+      let intraSum = 0;
+      let intraCount = 0;
+      for (const otherId of clusterIds) {
+        if (otherId === noteId) continue;
+        const otherVec = embeddingMap.get(otherId);
+        if (!otherVec) continue;
+        intraSum += 1 - cosineSimilarity(vec, otherVec);
+        intraCount++;
+      }
+      const a = intraCount > 0 ? intraSum / intraCount : 0;
+
+      // b(i): min avg distance to notes in any other cluster
+      let b = Infinity;
+      for (let oi = 0; oi < clusters.length; oi++) {
+        if (oi === ci) continue;
+        const otherClusterIds = clusters[oi].noteIds;
+        let interSum = 0;
+        let interCount = 0;
+        for (const otherId of otherClusterIds) {
+          const otherVec = embeddingMap.get(otherId);
+          if (!otherVec) continue;
+          interSum += 1 - cosineSimilarity(vec, otherVec);
+          interCount++;
+        }
+        if (interCount > 0) {
+          b = Math.min(b, interSum / interCount);
+        }
+      }
+      if (b === Infinity) b = 0;
+
+      const max = Math.max(a, b);
+      scores.push(max === 0 ? 0 : (b - a) / max);
+    }
+  }
+
+  if (scores.length === 0) return 0;
+  return scores.reduce((sum, s) => sum + s, 0) / scores.length;
+};

backend/tests/Clustering.service.test.js

@@ -1,7 +1,11 @@
 import { describe, it, expect, vi, beforeEach } from 'vitest';
 
-const { createMock } = vi.hoisted(() => {
+const { createMock, mockEmbeddings } = vi.hoisted(() => {
-  return { createMock: vi.fn() };
+  const embeddings = new Map([
+    ['note_001', [1.0, 0.0, 0.0]],
+    ['note_002', [0.0, 1.0, 0.0]],
+  ]);
+  return { createMock: vi.fn(), mockEmbeddings: embeddings };
 });
 
 vi.mock('@anthropic-ai/sdk', () => {
@@ -14,6 +18,10 @@ vi.mock('@anthropic-ai/sdk', () => {
   };
 });
 
+vi.mock('../services/embedding.service.js', () => ({
+  embedNotes: vi.fn().mockResolvedValue(mockEmbeddings),
+}));
+
 import { clusterNotes } from '../services/clustering.service.js';
 
 const MOCK_NOTES = [
@@ -60,14 +68,17 @@ describe('clusterNotes service', () => {
     expect(prompt).toContain('Export fails');
   });
 
-  it('should parse and return the clustered JSON from the API response', async () => {
+  it('should return clusters and a cohesion score', async () => {
     createMock.mockResolvedValue({
       content: [{ type: 'text', text: JSON.stringify(MOCK_CLUSTERS) }],
     });
 
     const result = await clusterNotes(MOCK_NOTES);
 
-    expect(result).toEqual(MOCK_CLUSTERS);
+    expect(result.clusters).toEqual(MOCK_CLUSTERS);
+    expect(typeof result.score).toBe('number');
+    expect(result.score).toBeGreaterThanOrEqual(-1);
+    expect(result.score).toBeLessThanOrEqual(1);
   });
 
   it('should throw error when the API returns non-JSON', async () => {
@@ -89,4 +100,15 @@ describe('clusterNotes service', () => {
 
     await expect(clusterNotes(MOCK_NOTES)).rejects.toThrow('401 Unauthorized');
   });
+
+  it('should throw a validation error when a note is missing from clusters', async () => {
+    const incompleteClusters = [
+      { label: 'Auth Issues', noteIds: ['note_001'] },
+    ];
+    createMock.mockResolvedValue({
+      content: [{ type: 'text', text: JSON.stringify(incompleteClusters) }],
+    });
+
+    await expect(clusterNotes(MOCK_NOTES)).rejects.toThrow('Cluster validation failed');
+  });
 });

backend/tests/Notes.test.js

@@ -115,7 +115,7 @@ describe('POST /v1/notes/cluster', () => {
 
   it('should return 500 when clusterNotes (API call) fails', async () => {
     readFile.mockResolvedValue(JSON.stringify(MOCK_NOTES));
-    clusterNotes.mockRejectedValue(new Error('Anthropic API error'));
+    clusterNotes.mockRejectedValue(new Error('LLM API error'));
 
     const res = await request(app).post('/v1/notes/cluster');
 

frontend/.env.development

@@ -0,0 +1,2 @@
+VITE_APP_URL=localhost:3000
+VITE_API_BASE=http://localhost:3001

frontend/.env.production

@@ -0,0 +1,2 @@
+VITE_APP_URL=https://example.com
+VITE_API_BASE=https://www.example.com:4000

frontend/src/api/api.ts

@@ -1,7 +1,7 @@
 import { useQuery, useMutation } from '@tanstack/react-query';
-import type { Sticky, Cluster } from '../types/types';
+import type { Sticky, ClusterResponse } from '../types/types';
 
-const API_BASE = 'http://localhost:3001/v1/notes';
+const API_BASE = `${import.meta.env.VITE_API_BASE}/v1/notes`;
 
 const fetchStickies = async (): Promise<Sticky[]> => {
   const response = await fetch(API_BASE);
@@ -11,7 +11,7 @@ const fetchStickies = async (): Promise<Sticky[]> => {
   return response.json();
 };
 
-const fetchClusters = async (): Promise<Cluster[]> => {
+const fetchClusters = async (): Promise<ClusterResponse> => {
   const response = await fetch(`${API_BASE}/cluster`, {
     method: 'POST',
   });
@@ -29,7 +29,7 @@ export const useGetStickies = () => {
 };
 
 export const useClusterStickies = () => {
-  return useMutation<Cluster[]>({
+  return useMutation<ClusterResponse>({
     mutationFn: fetchClusters,
   });
 };

frontend/src/components/stickies.tsx

@@ -1,16 +1,39 @@
+import { useState, useEffect, useRef } from 'react';
+import type { DragEvent } from 'react';
 import { useGetStickies, useClusterStickies } from '../api/api';
-import type { Sticky as StickyType } from '../types/types';
+import type { Sticky as StickyType, RankedCluster } from '../types/types';
 import Sticky from './sticky';
 import Button from './button';
 import '../styles/stickies.css';
 
+const scoreLabel = (score: number): string => {
+  if (score >= 0.7) return 'Strong';
+  if (score >= 0.4) return 'Moderate';
+  if (score >= 0.1) return 'Weak';
+  return 'Poor';
+};
+
 const Stickies = () => {
   const { data: stickies, isLoading, error } = useGetStickies();
-  const { mutate: cluster, data: clusters, isPending } = useClusterStickies();
+  const { mutate: cluster, data: clusterResponse, isPending } = useClusterStickies();
+
+  const [rankedClusters, setRankedClusters] = useState<RankedCluster[]>([]);
+  const dragIndex = useRef<number | null>(null);
+  const [dragOverIndex, setDragOverIndex] = useState<number | null>(null);
+
+  useEffect(() => {
+    if (clusterResponse?.clusters) {
+      setRankedClusters(
+        clusterResponse.clusters.map((c, i) => ({ ...c, rank: i + 1 }))
+      );
+    }
+  }, [clusterResponse]);
 
   if (isLoading) return <div>Loading...</div>;
   if (error) return <div>Error: {error.message}</div>;
 
+  const score = clusterResponse?.score;
+
   const handleCluster = () => {
     cluster();
   };
@@ -24,20 +47,75 @@ const Stickies = () => {
   const stickyMap = buildStickyMap();
 
   const renderStickies = (items: StickyType[]) =>
-    items.map((sticky) => <Sticky key={sticky.id} sticky={sticky} />);
+    items?.map((sticky) => <Sticky key={sticky.id} sticky={sticky} />);
+
+  const handleDragStart = (index: number) => {
+    dragIndex.current = index;
+  };
+
+  const handleDragOver = (e: DragEvent, index: number) => {
+    e.preventDefault();
+    setDragOverIndex(index);
+  };
+
+  const handleDragLeave = () => {
+    setDragOverIndex(null);
+  };
+
+  const handleDrop = (targetIndex: number) => {
+    const sourceIndex = dragIndex.current;
+    if (sourceIndex === null || sourceIndex === targetIndex) {
+      dragIndex.current = null;
+      setDragOverIndex(null);
+      return;
+    }
+
+    const reordered = [...rankedClusters];
+    const [moved] = reordered.splice(sourceIndex, 1);
+    reordered.splice(targetIndex, 0, moved);
+
+    setRankedClusters(reordered.map((c, i) => ({ ...c, rank: i + 1 })));
+    dragIndex.current = null;
+    setDragOverIndex(null);
+  };
+
+  const handleDragEnd = () => {
+    dragIndex.current = null;
+    setDragOverIndex(null);
+  };
 
   return (
     <div className="stickies-container">
       <Button onClick={handleCluster} isLoading={isPending} label="Group Stickies By Topic" />
-      {clusters ? (
+      {rankedClusters.length > 0 ? (
         <div className="clusters-container">
-          {clusters.map((group) => (
+          {score != null && (
-            <div key={group.label} className="cluster-group">
+            <div className="cohesion-score">
+              Cluster cohesion: <strong>{score.toFixed(2)}</strong> — {scoreLabel(score)}
+            </div>
+          )}
+          {rankedClusters.map((group, index) => (
+            <div
+              key={group.label}
+              className={`cluster-group cluster-draggable${dragOverIndex === index ? ' cluster-drag-over' : ''}`}
+              draggable
+              onDragStart={() => handleDragStart(index)}
+              onDragOver={(e) => handleDragOver(e, index)}
+              onDragLeave={handleDragLeave}
+              onDrop={() => handleDrop(index)}
+              onDragEnd={handleDragEnd}
+            >
+              <div className="cluster-header">
+                <span className="cluster-rank" aria-label={`Priority ${group.rank}`}>
+                  {group.rank}
+                </span>
                 <h3 className="cluster-label">{group.label}</h3>
+                <span className="cluster-drag-handle" aria-hidden="true">⠿</span>
+              </div>
               <div className="stickies-grid">
                 {renderStickies(
-                  group.noteIds
+                  group?.noteIds
-                    .map((id) => stickyMap.get(id))
+                    .map((id) => stickyMap?.get(id))
                     .filter((s): s is StickyType => !!s)
                 )}
               </div>

frontend/src/main.tsx

@@ -14,5 +14,5 @@ createRoot(document.getElementById('root')!).render(
         <App />
       </BrowserRouter>
     </QueryClientProvider>
-  </StrictMode>,
+  </StrictMode>
 )

frontend/src/pages/Home.tsx

@@ -4,14 +4,10 @@ import '../styles/home.css'
 
 const Home = () => {
   return (
-      <div>
     <div>
       <Navbar />
-         </div>
-         <div>
       <Stickies />
     </div>
-      </div>
   );
 };
 

frontend/src/styles/home.css

@@ -1,25 +1,25 @@
 .main-head-box {
   border-radius: 8px;
   border: 1px solid #6dd6f4;
-    background-color: rgb(92, 0 91);
+  background-color: rgb(92, 0, 91);
   display: flex;
-  }
+}
 
-  .main-head-subbox-right {
+.main-head-subbox-right {
   display: flex;
   justify-content: flex-end;
   margin-right: 34px;
   width: 50%;
-  }
+}
 
-  .main-head-subbox-left {
+.main-head-subbox-left {
   display: flex;
   justify-content: flex-start;
   margin-left: 34px;
   width: 50%;
-  }
+}
 
-  .main-head {
+.main-head {
   font-size: 5rem;
   color: #6dd6f4;
   margin: 6px 0px 24px 22px;
@@ -27,9 +27,9 @@
   font-weight: 500;
   letter-spacing: 4px;
   text-decoration: underline;
-  }
+}
 
-  .material-symbols-outlined {
+.material-symbols-outlined {
   margin: 13px 74px 0px 0px;
   font-size: 82px;
   color: #6dd6f4;
@@ -37,5 +37,37 @@
     'FILL' 0,
     'wght' 300,
     'GRAD' 0,
-    'opsz' 24
+    'opsz' 24;
+}
+
+@media screen and (max-width: 478px) {
+  .main-head-subbox-right {
+    display: none;
   }
+
+  .main-head-subbox-left {
+    display: flex;
+    justify-content: center;
+    margin-left: auto;
+    margin-right: auto;
+    width: 100%;
+  }
+
+  .main-head {
+    font-size: 4rem;
+    color: #6dd6f4;
+    font-family: "Sulphur Point", sans-serif;
+    display: flex;
+    justify-content: center;
+    align-items: center;
+    margin-left: auto;
+    margin-right: auto;
+    font-weight: 400;
+    letter-spacing: 2px;
+    text-decoration: underline;
+  }
+
+  .material-symbols-outlined {
+    display: none !important;
+  }
+}

frontend/src/styles/stickies.css

@@ -3,6 +3,7 @@
   flex-wrap: wrap;
   gap: 16px;
   justify-content: center;
+  margin-top: 18px;
   padding: 24px;
 }
 
@@ -23,8 +24,69 @@
   padding: 16px;
 }
 
+.cluster-draggable {
+  cursor: grab;
+  transition: box-shadow 0.2s ease, border-color 0.2s ease, transform 0.15s ease;
+}
+
+.cluster-draggable:active {
+  cursor: grabbing;
+}
+
+.cluster-drag-over {
+  border-color: #ffb7ce;
+  box-shadow: 0 0 12px rgba(255, 183, 206, 0.4);
+  transform: scale(1.01);
+}
+
+.cluster-header {
+  display: flex;
+  align-items: center;
+  gap: 12px;
+  margin-bottom: 16px;
+}
+
+.cluster-rank {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  width: 32px;
+  height: 32px;
+  border-radius: 50%;
+  background: #6dd6f4;
+  color: #1a1a2e;
+  font-weight: 700;
+  font-size: 0.95em;
+  flex-shrink: 0;
+}
+
 .cluster-label {
-  margin: 0 0 16px 0;
+  margin: 0;
   font-size: 1.2em;
   font-weight: 600;
+  flex: 1;
+}
+
+.cluster-drag-handle {
+  font-size: 1.4em;
+  color: #6dd6f4;
+  opacity: 0.4;
+  user-select: none;
+  transition: opacity 0.2s ease;
+  flex-shrink: 0;
+}
+
+.cluster-draggable:hover .cluster-drag-handle {
+  opacity: 0.8;
+}
+
+.cohesion-score {
+  text-align: center;
+  font-size: 0.95em;
+  color: #e0e0e0;
+  padding: 8px 16px;
+  background: rgba(109, 214, 244, 0.1);
+  border-radius: 6px;
+  width: fit-content;
+  margin: 0 auto;
 }

frontend/src/tests/stickies.test.tsx

@@ -9,10 +9,13 @@ const MOCK_STICKIES = [
   { id: 'note_002', text: 'Export takes too long', x: 798, y: 211, author: 'user_2', color: 'green' },
 ];
 
-const MOCK_CLUSTERS = [
+const MOCK_CLUSTER_RESPONSE = {
+  clusters: [
     { label: 'Auth Issues', noteIds: ['note_001'] },
     { label: 'Export Issues', noteIds: ['note_002'] },
-];
+  ],
+  score: 0.74,
+};
 
 let fetchMock: ReturnType<typeof vi.fn>;
 
@@ -70,10 +73,9 @@ describe('Stickies', () => {
   });
 
   it('should show cluster labels after clustering succeeds', async () => {
-    // First call = GET notes, second call = POST cluster
     fetchMock
       .mockReturnValueOnce(mockFetchOk(MOCK_STICKIES))
-      .mockReturnValueOnce(mockFetchOk(MOCK_CLUSTERS));
+      .mockReturnValueOnce(mockFetchOk(MOCK_CLUSTER_RESPONSE));
 
     const { Wrapper } = createTestWrapper();
     render(<Stickies />, { wrapper: Wrapper });
@@ -90,7 +92,7 @@ describe('Stickies', () => {
   it('should still render sticky note text inside clusters', async () => {
     fetchMock
       .mockReturnValueOnce(mockFetchOk(MOCK_STICKIES))
-      .mockReturnValueOnce(mockFetchOk(MOCK_CLUSTERS));
+      .mockReturnValueOnce(mockFetchOk(MOCK_CLUSTER_RESPONSE));
 
     const { Wrapper } = createTestWrapper();
     render(<Stickies />, { wrapper: Wrapper });
@@ -101,4 +103,41 @@ describe('Stickies', () => {
     expect(await screen.findByText('Login flow feels confusing')).toBeInTheDocument();
     expect(screen.getByText('Export takes too long')).toBeInTheDocument();
   });
+
+  it('should display rank badges on clustered groups', async () => {
+    fetchMock
+      .mockReturnValueOnce(mockFetchOk(MOCK_STICKIES))
+      .mockReturnValueOnce(mockFetchOk(MOCK_CLUSTER_RESPONSE));
+
+    const { Wrapper } = createTestWrapper();
+    render(<Stickies />, { wrapper: Wrapper });
+
+    const btn = await screen.findByRole('button', { name: 'Group Stickies By Topic' });
+    await userEvent.click(btn);
+
+    await waitFor(() => {
+      expect(screen.getByLabelText('Priority 1')).toBeInTheDocument();
+      expect(screen.getByLabelText('Priority 2')).toBeInTheDocument();
+    });
+  });
+
+  it('should make cluster groups draggable', async () => {
+    fetchMock
+      .mockReturnValueOnce(mockFetchOk(MOCK_STICKIES))
+      .mockReturnValueOnce(mockFetchOk(MOCK_CLUSTER_RESPONSE));
+
+    const { Wrapper } = createTestWrapper();
+    render(<Stickies />, { wrapper: Wrapper });
+
+    const btn = await screen.findByRole('button', { name: 'Group Stickies By Topic' });
+    await userEvent.click(btn);
+
+    await waitFor(() => {
+      const groups = document.querySelectorAll('.cluster-draggable');
+      groups.forEach((group) => {
+        expect(group).toHaveAttribute('draggable', 'true');
+      });
+      expect(groups.length).toBe(2);
+    });
+  });
 });

frontend/src/types/types.d.ts

@@ -11,3 +11,12 @@ export type Cluster = {
   label: string;
   noteIds: string[];
 };
+
+export type ClusterResponse = {
+  clusters: Cluster[];
+  score: number;
+};
+
+export type RankedCluster = Cluster & {
+  rank: number;
+};