The workflow JSON
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{
"name": "dev_activity_reporter",
"nodes": [
{
"parameters": {
"operation": "get",
"dataTableId": {
"__rl": true,
"value": "61EXjt0nLCKmZ7ON",
"mode": "list",
"cachedResultName": "dev_activity_daemon",
"cachedResultUrl": "/projects/57QGut0RrnCgx05p/datatables/61EXjt0nLCKmZ7ON"
},
"matchType": "allConditions",
"filters": {
"conditions": [
{
"keyName": "start_time",
"condition": "gt",
"keyValue": "={{ $now.minus({days: 7}) }}"
}
]
},
"returnAll": true,
"orderBy": true
},
"type": "n8n-nodes-base.dataTable",
"typeVersion": 1.1,
"position": [
160,
464
],
"id": "9d47f96e-fb80-4160-adb8-79212f7ddba7",
"name": "Weekly getter"
},
{
"parameters": {
"rule": {
"interval": [
{
"field": "weeks",
"triggerAtHour": 22
}
]
}
},
"type": "n8n-nodes-base.scheduleTrigger",
"typeVersion": 1.3,
"position": [
-48,
464
],
"id": "90ac5615-e02d-44fb-b1ba-89095d1d3062",
"name": "Weekly trigger"
},
{
"parameters": {
"rule": {
"interval": [
{
"field": "months"
}
]
}
},
"type": "n8n-nodes-base.scheduleTrigger",
"typeVersion": 1.3,
"position": [
-48,
704
],
"id": "23b00db0-340b-43b5-ad60-a8c8c3d8444c",
"name": "Monthly trigger"
},
{
"parameters": {
"operation": "get",
"dataTableId": {
"__rl": true,
"value": "61EXjt0nLCKmZ7ON",
"mode": "list",
"cachedResultName": "dev_activity_daemon",
"cachedResultUrl": "/projects/57QGut0RrnCgx05p/datatables/61EXjt0nLCKmZ7ON"
},
"matchType": "allConditions",
"filters": {
"conditions": [
{
"keyName": "start_time",
"condition": "lt",
"keyValue": "={{ $now.startOf('month') }}"
},
{
"keyName": "start_time",
"condition": "gt",
"keyValue": "={{ $now.minus({months: 1}).startOf('month') }}"
}
]
},
"returnAll": true,
"orderBy": true
},
"type": "n8n-nodes-base.dataTable",
"typeVersion": 1.1,
"position": [
160,
704
],
"id": "aaf33ad7-4b4b-49e1-b57a-47163c63bfdf",
"name": "Monthly getter"
},
{
"parameters": {
"language": "pythonNative",
"pythonCode": "from datetime import datetime, timezone\nimport pandas as pd\n\n# --- Receive rows from Data Tables node ---\nrows = [item['json'] for item in _items]\ndf = pd.DataFrame(rows)\n\n# --- Parse and clean ---\ndf[\"start_time\"] = pd.to_datetime(df[\"start_time\"], utc=True)\ndf[\"end_time\"] = pd.to_datetime(df[\"end_time\"], utc=True)\ndf[\"duration_seconds\"] = pd.to_numeric(df[\"duration_seconds\"], errors=\"coerce\").fillna(0)\ndf[\"is_idle\"] = df[\"is_idle\"].astype(bool)\ndf[\"category\"] = df[\"category\"].fillna(\"Uncategorized\")\n\n# --- Split active vs idle ---\ndf_active = df[df[\"is_idle\"] == False].copy()\ndf_idle = df[df[\"is_idle\"] == True].copy()\n\ntotal_active = int(df_active[\"duration_seconds\"].sum())\ntotal_idle = int(df_idle[\"duration_seconds\"].sum())\n\n# --- 1. Time per category ---\nby_category = (\n df_active.groupby(\"category\")[\"duration_seconds\"]\n .sum()\n .sort_values(ascending=False)\n .astype(int)\n .to_dict()\n)\n\n# --- 2. Daily activity ---\ndf_active[\"day\"] = df_active[\"start_time\"].dt.strftime(\"%a %d.%m\")\nby_day = (\n df_active.groupby(\"day\")[\"duration_seconds\"]\n .sum()\n .astype(int)\n .to_dict()\n)\n\n# --- 3. Top 10 apps + Others (for pie chart) ---\napp_totals = (\n df_active.groupby(\"window_title\")[\"duration_seconds\"]\n .sum()\n .sort_values(ascending=False)\n)\ntop_10 = app_totals.head(10).astype(int).to_dict()\nothers_sum = int(app_totals.iloc[10:].sum())\nif others_sum > 0:\n top_10[\"Others\"] = others_sum\ntop_apps_pie = top_10\n\n# --- 4. Heatmap: weekday vs hour ---\ndf_active[\"hour\"] = df_active[\"start_time\"].dt.hour\ndf_active[\"weekday\"] = df_active[\"start_time\"].dt.strftime(\"%a\")\nheatmap = (\n df_active.groupby([\"weekday\", \"hour\"])[\"duration_seconds\"]\n .sum()\n .reset_index()\n .rename(columns={\"duration_seconds\": \"seconds\"})\n .astype({\"seconds\": int})\n .to_dict(orient=\"records\")\n)\n\n# --- 5. Stacked bar: category time per day ---\nWEEKDAY_ORDER = [\"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\"]\ndf_active[\"weekday_short\"] = df_active[\"start_time\"].dt.strftime(\"%a\")\nstacked = (\n df_active.groupby([\"weekday_short\", \"category\"])[\"duration_seconds\"]\n .sum()\n .reset_index()\n)\n# Build dict: {category: {day: hours}}\nall_categories = stacked[\"category\"].unique().tolist()\nstacked_by_category = {}\nfor cat in all_categories:\n cat_df = stacked[stacked[\"category\"] == cat]\n day_dict = dict(zip(cat_df[\"weekday_short\"], cat_df[\"duration_seconds\"] / 3600))\n stacked_by_category[cat] = {\n day: round(day_dict.get(day, 0.0), 2)\n for day in WEEKDAY_ORDER\n }\n\nreturn [{\n \"json\": {\n \"total_active_seconds\": total_active,\n \"total_idle_seconds\": total_idle,\n \"by_category\": by_category,\n \"by_day\": by_day,\n \"top_apps_pie\": top_apps_pie,\n \"stacked_by_category\": stacked_by_category,\n \"heatmap\": heatmap,\n \"weekday_order\": WEEKDAY_ORDER,\n \"all_categories\": all_categories,\n }\n}]"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
368,
464
],
"id": "fe4c6bd3-c465-44ed-9f14-3db236fe049b",
"name": "Week analysis"
},
{
"parameters": {
"language": "pythonNative",
"pythonCode": "import json\nimport urllib.parse\nimport urllib.request\n\ndata = _items[0]['json']\n\nCOLORS = [\n '#4C9BE8', '#E87B4C', '#4CE87B', '#E84C9B',\n '#9B4CE8', '#E8E84C', '#4CE8E8', '#E84C4C',\n '#9BE84C', '#4C4CE8', '#888888'\n]\n\nBASE_URL = \"https://quickchart.io/chart?w=600&h=300&bkg=%231a1a2e&c=\"\n\ndef make_short_url(config):\n payload = json.dumps({\"chart\": config, \"width\": 600, \"height\": 300, \"backgroundColor\": \"#1a1a2e\"}).encode('utf-8')\n req = urllib.request.Request(\n \"https://quickchart.io/chart/create\",\n data=payload,\n headers={\"Content-Type\": \"application/json\"},\n method=\"POST\"\n )\n with urllib.request.urlopen(req, timeout=10) as resp:\n result = json.loads(resp.read().decode())\n return result[\"url\"]\n\ndef make_url(config):\n encoded = urllib.parse.quote(json.dumps(config))\n return BASE_URL + encoded\n\ndef sec_to_hm(s):\n h = int(s // 3600)\n m = int((s % 3600) // 60)\n return f\"{h}h {m}m\"\n\n# --- Chart 1: Time per category (horizontal bar) ---\ncat_items = [(k, round(v / 3600, 2)) for k, v in data['by_category'].items() if v > 0]\ncats = [x[0] for x in cat_items]\nvals = [x[1] for x in cat_items]\n\nchart_category = make_short_url({\n \"type\": \"horizontalBar\",\n \"data\": {\n \"labels\": cats,\n \"datasets\": [{\n \"label\": \"Hours\",\n \"data\": vals,\n \"backgroundColor\": COLORS[:len(cats)]\n }]\n },\n \"options\": {\n \"legend\": {\"labels\": {\"fontColor\": \"white\"}},\n \"scales\": {\n \"xAxes\": [{\"ticks\": {\"fontColor\": \"white\"}, \"gridLines\": {\"color\": \"#333\"}}],\n \"yAxes\": [{\"ticks\": {\"fontColor\": \"white\"}, \"gridLines\": {\"color\": \"#333\"}}]\n }\n }\n})\n\n# --- Chart 2: Daily activity bar ---\ndays = data['weekday_order']\nby_day_short = {}\nfor k, v in data['by_day'].items():\n short = k.split(' ')[0]\n by_day_short[short] = by_day_short.get(short, 0) + v\nday_vals = [round(by_day_short.get(d, 0) / 3600, 2) for d in days]\n\nchart_daily = make_url({\n \"type\": \"bar\",\n \"data\": {\n \"labels\": days,\n \"datasets\": [{\n \"label\": \"Hours\",\n \"data\": day_vals,\n \"backgroundColor\": \"#4C9BE8\"\n }]\n },\n \"options\": {\n \"legend\": {\"labels\": {\"fontColor\": \"white\"}},\n \"scales\": {\n \"xAxes\": [{\"ticks\": {\"fontColor\": \"white\"}, \"gridLines\": {\"color\": \"#333\"}}],\n \"yAxes\": [{\"ticks\": {\"fontColor\": \"white\"}, \"gridLines\": {\"color\": \"#333\"}}]\n }\n }\n})\n\n# --- Chart 3: Top apps pie ---\napp_labels = list(data['top_apps_pie'].keys())\napp_vals_seconds = list(data['top_apps_pie'].values())\napp_vals = [round(v / 60) for v in app_vals_seconds] # minutes for chart proportions\n\nshort_labels = [\n (l[:30] + '...' if len(l) > 30 else l) + f\" [{sec_to_hm(v)}]\"\n for l, v in zip(app_labels, app_vals_seconds)\n]\n\ntotal_app_secs = sum(app_vals_seconds)\nshort_labels = [\n (l[:25] + '...' if len(l) > 25 else l)\n + f\" [{round(v / total_app_secs * 100, 1)}% {sec_to_hm(v)}]\"\n for l, v in zip(app_labels, app_vals_seconds)\n]\n\nchart_top_apps = make_short_url({\n \"type\": \"pie\",\n \"data\": {\n \"labels\": short_labels,\n \"datasets\": [{\n \"data\": app_vals,\n \"backgroundColor\": COLORS[:len(app_vals)]\n }]\n },\n \"options\": {\n \"legend\": {\n \"position\": \"right\",\n \"labels\": {\"fontColor\": \"white\", \"fontSize\": 10}\n }\n }\n})\n\n# --- Chart 4: Stacked bar category per day ---\ncategories = data['all_categories']\ndatasets = []\nfor i, cat in enumerate(categories):\n cat_vals = [round(data['stacked_by_category'].get(cat, {}).get(d, 0), 2) for d in days]\n datasets.append({\n \"label\": cat,\n \"data\": cat_vals,\n \"backgroundColor\": COLORS[i % len(COLORS)]\n })\n\nchart_stacked = make_url({\n \"type\": \"bar\",\n \"data\": {\n \"labels\": days,\n \"datasets\": datasets\n },\n \"options\": {\n \"scales\": {\n \"xAxes\": [{\"stacked\": True, \"ticks\": {\"fontColor\": \"white\"}, \"gridLines\": {\"color\": \"#333\"}}],\n \"yAxes\": [{\"stacked\": True, \"ticks\": {\"fontColor\": \"white\"}, \"gridLines\": {\"color\": \"#333\"}}]\n },\n \"legend\": {\"labels\": {\"fontColor\": \"white\"}}\n }\n})\n\n# --- Chart 5: Active vs Idle doughnut ---\nactive_h = round(data['total_active_seconds'] / 3600, 2)\nidle_h = round(data['total_idle_seconds'] / 3600, 2)\nif active_h == 0 and idle_h == 0:\n idle_h = 0.01\n\ntotal_h = active_h + idle_h\nactive_pct = round(100 * active_h / total_h, 1) if total_h > 0 else 0\nidle_pct = round(100 * idle_h / total_h, 1) if total_h > 0 else 0\n\nchart_idle = make_short_url({\n \"type\": \"doughnut\",\n \"data\": {\n \"labels\": [\n f\"Active {active_pct}% ({sec_to_hm(data['total_active_seconds'])})\",\n f\"Idle {idle_pct}% ({sec_to_hm(data['total_idle_seconds'])})\"\n ],\n \"datasets\": [{\n \"data\": [active_h, idle_h],\n \"backgroundColor\": [\"#4C9BE8\", \"#E84C4C\"]\n }]\n },\n \"options\": {\n \"legend\": {\n \"position\": \"bottom\",\n \"labels\": {\"fontColor\": \"white\"}\n }\n }\n})\n\n# --- Chart 6: Heatmap (bar per hour) ---\nhour_sums = {}\nfor entry in data['heatmap']:\n h = entry['hour']\n hour_sums[h] = hour_sums.get(h, 0) + entry['seconds']\n\nhours = list(range(24))\nhour_vals = [round(hour_sums.get(h, 0) / 60, 1) for h in hours] # minutes\n\nchart_heatmap = make_url({\n \"type\": \"bar\",\n \"data\": {\n \"labels\": [f\"{h:02d}:00\" for h in hours],\n \"datasets\": [{\n \"label\": \"Activity (minutes)\",\n \"data\": hour_vals,\n \"backgroundColor\": \"#4C9BE8\"\n }]\n },\n \"options\": {\n \"title\": {\n \"display\": True,\n \"text\": \"Activity by Hour of Day\",\n \"fontColor\": \"white\"\n },\n \"legend\": {\"labels\": {\"fontColor\": \"white\"}},\n \"scales\": {\n \"xAxes\": [{\"ticks\": {\"fontColor\": \"white\", \"maxRotation\": 45}, \"gridLines\": {\"color\": \"#333\"}}],\n \"yAxes\": [{\"ticks\": {\"fontColor\": \"white\"}, \"gridLines\": {\"color\": \"#333\"},\n \"scaleLabel\": {\"display\": True, \"labelString\": \"Minutes\", \"fontColor\": \"white\"}}]\n }\n }\n})\n\nreturn [{'json': {\n 'chart_category': chart_category,\n 'chart_daily': chart_daily,\n 'chart_top_apps': chart_top_apps,\n 'chart_stacked': chart_stacked,\n 'chart_idle': chart_idle,\n 'chart_heatmap': chart_heatmap,\n 'total_active_seconds': data['total_active_seconds'],\n 'total_idle_seconds': data['total_idle_seconds'],\n 'by_category': data['by_category'],\n 'weekday_order': data['weekday_order'],\n 'all_categories': data['all_categories'],\n}}]"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
576,
464
],
"id": "dc37ee95-9825-4173-ae99-9d33e460f2a6",
"name": "Graphs creator"
},
{
"parameters": {
"language": "pythonNative",
"pythonCode": "data = _items[0]['json']\n\ndef seconds_to_hm(s):\n h = int(s // 3600)\n m = int((s % 3600) // 60)\n return f\"{h}h {m}m\"\n\ntotal_active = seconds_to_hm(data['total_active_seconds'])\ntotal_idle = seconds_to_hm(data['total_idle_seconds'])\n\ncategory_rows = \"\"\nfor cat, secs in data['by_category'].items():\n category_rows += f\"<tr><td style='padding:6px 12px;color:#ccc;'>{cat}</td><td style='padding:6px 12px;color:#4C9BE8;font-weight:bold;'>{seconds_to_hm(secs)}</td></tr>\"\n\nhtml = f\"\"\"\n<!DOCTYPE html>\n<html>\n<head>\n<meta charset=\"utf-8\">\n<style>\n body {{ font-family: Arial, sans-serif; background: #0f0f1a; color: #ffffff; margin: 0; padding: 0; }}\n .container {{ max-width: 800px; margin: 0 auto; padding: 30px 20px; }}\n .header {{ background: linear-gradient(135deg, #1a1a2e, #16213e); border-radius: 12px; padding: 30px; margin-bottom: 30px; text-align: center; border: 1px solid #4C9BE8; }}\n .header h1 {{ margin: 0; font-size: 28px; color: #4C9BE8; }}\n .header p {{ margin: 8px 0 0; color: #aaa; font-size: 14px; }}\n .stats {{ width: 100%; margin-bottom: 30px; }}\n .stat-box {{ display: inline-block; width: 30%; background: #1a1a2e; border-radius: 10px; padding: 20px; text-align: center; border: 1px solid #333; vertical-align: top; }}\n .stat-box .value {{ font-size: 26px; font-weight: bold; color: #4C9BE8; }}\n .stat-box .label {{ font-size: 12px; color: #888; margin-top: 5px; }}\n .section {{ background: #1a1a2e; border-radius: 10px; padding: 20px; margin-bottom: 25px; border: 1px solid #333; }}\n .section h2 {{ margin: 0 0 15px; font-size: 16px; color: #4C9BE8; border-bottom: 1px solid #333; padding-bottom: 10px; }}\n .chart img {{ width: 100% !important; max-width: 100% !important; height: auto !important; border-radius: 8px; display: block; }}\n table {{ width: 100%; border-collapse: collapse; }}\n tr:nth-child(even) {{ background: #16213e; }}\n .footer {{ text-align: center; color: #555; font-size: 11px; margin-top: 30px; }}\n</style>\n</head>\n<body>\n<div class=\"container\">\n\n <div class=\"header\">\n <h1>Dev Activity Report</h1>\n <p>Weekly summary \u2014 last 7 days</p>\n </div>\n\n <div class=\"stats\">\n <div class=\"stat-box\">\n <div class=\"value\">{total_active}</div>\n <div class=\"label\">Active Time</div>\n </div>\n <div class=\"stat-box\">\n <div class=\"value\">{total_idle}</div>\n <div class=\"label\">Idle Time</div>\n </div>\n <div class=\"stat-box\">\n <div class=\"value\">{len(data['by_category'])}</div>\n <div class=\"label\">Categories</div>\n </div>\n </div>\n\n <div class=\"section\">\n <h2>Time per Category</h2>\n <table>{category_rows}</table>\n </div>\n\n <div class=\"section chart\">\n <h2>Category Breakdown</h2>\n <img width=\"700\" src=\"{data['chart_category']}\">\n </div>\n\n <div class=\"section chart\">\n <h2>Daily Activity</h2>\n <img width=\"700\" src=\"{data['chart_daily']}\">\n </div>\n\n <div class=\"section chart\">\n <h2>Category per Day</h2>\n <img width=\"700\" src=\"{data['chart_stacked']}\">\n </div>\n\n <div class=\"section chart\">\n <h2>Top Apps</h2>\n <img width=\"700\" src=\"{data['chart_top_apps']}\">\n </div>\n\n <div class=\"section chart\">\n <h2>Active vs Idle</h2>\n <img width=\"700\" src=\"{data['chart_idle']}\">\n </div>\n\n <div class=\"section chart\">\n <h2>Productivity Heatmap</h2>\n <img width=\"700\" src=\"{data['chart_heatmap']}\">\n </div>\n\n <div class=\"footer\">\n Generated by Dev-Tracker \u00b7 Privacy-first activity monitoring\n </div>\n\n</div>\n</body>\n</html>\n\"\"\"\n\nreturn [{'json': {'subject': 'Dev Activity \u2014 Weekly Report', 'html': html}}]"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
784,
464
],
"id": "fb4969d7-430e-46ea-bf65-cd14f6130dc7",
"name": "Format to HTML"
},
{
"parameters": {
"httpMethod": "POST",
"path": "f1d6349f-ed13-4960-8809-2451b41d888b",
"options": {}
},
"type": "n8n-nodes-base.webhook",
"typeVersion": 2.1,
"position": [
-48,
224
],
"id": "985a2bad-ca2f-42f4-8dce-73b682423e28",
"name": "Data listener"
},
{
"parameters": {
"fieldToSplitOut": "body.sessions",
"options": {}
},
"type": "n8n-nodes-base.splitOut",
"typeVersion": 1,
"position": [
160,
224
],
"id": "3f778f55-f112-4edc-827a-df04937554c1",
"name": "Split data to rows"
},
{
"parameters": {
"jsCode": "for (const item of $input.all()) {\n item.json.start_time = item.json.start_time.replace(' ', 'T');\n item.json.end_time = item.json.end_time.replace(' ', 'T');\n}\nreturn $input.all();"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
368,
224
],
"id": "d33a4a32-e9a6-4bd3-80da-ebffecc9ab12",
"name": "Change date time"
},
{
"parameters": {
"dataTableId": {
"__rl": true,
"value": "61EXjt0nLCKmZ7ON",
"mode": "list",
"cachedResultName": "dev_activity_daemon",
"cachedResultUrl": "/projects/57QGut0RrnCgx05p/datatables/61EXjt0nLCKmZ7ON"
},
"columns": {
"mappingMode": "defineBelow",
"value": {
"is_idle": "={{ $json.is_idle }}",
"session_id": "={{ $json.id }}",
"window_title": "={{ $json.window_title }}",
"process_name": "={{ $json.process_name }}",
"category": "={{ $json.category }}",
"start_time": "={{ $json.start_time }}",
"end_time": "={{ $json.end_time }}",
"duration_seconds": "={{ $json.duration_seconds }}"
},
"matchingColumns": [],
"schema": [
{
"id": "session_id",
"displayName": "session_id",
"required": false,
"defaultMatch": false,
"display": true,
"type": "number",
"readOnly": false,
"removed": false
},
{
"id": "window_title",
"displayName": "window_title",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"readOnly": false,
"removed": false
},
{
"id": "process_name",
"displayName": "process_name",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"readOnly": false,
"removed": false
},
{
"id": "category",
"displayName": "category",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"readOnly": false,
"removed": false
},
{
"id": "start_time",
"displayName": "start_time",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"readOnly": false,
"removed": false
},
{
"id": "end_time",
"displayName": "end_time",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"readOnly": false,
"removed": false
},
{
"id": "duration_seconds",
"displayName": "duration_seconds",
"required": false,
"defaultMatch": false,
"display": true,
"type": "number",
"readOnly": false,
"removed": false
},
{
"id": "is_idle",
"displayName": "is_idle",
"required": false,
"defaultMatch": false,
"display": true,
"type": "boolean",
"readOnly": false,
"removed": false
}
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {}
},
"type": "n8n-nodes-base.dataTable",
"typeVersion": 1.1,
"position": [
576,
224
],
"id": "5de661c9-17a8-4676-b33c-5502f906bcc0",
"name": "Insert data rows to n8n table"
},
{
"parameters": {
"language": "pythonNative",
"pythonCode": "from datetime import datetime, timezone\nimport pandas as pd\n\nrows = [item['json'] for item in _items]\ndf = pd.DataFrame(rows)\n\n# --- Parse and clean ---\ndf[\"start_time\"] = pd.to_datetime(df[\"start_time\"], utc=True)\ndf[\"end_time\"] = pd.to_datetime(df[\"end_time\"], utc=True)\ndf[\"duration_seconds\"] = pd.to_numeric(df[\"duration_seconds\"], errors=\"coerce\").fillna(0)\ndf[\"is_idle\"] = df[\"is_idle\"].astype(bool)\ndf[\"category\"] = df[\"category\"].fillna(\"Uncategorized\")\n\ndf_active = df[df[\"is_idle\"] == False].copy()\ndf_idle = df[df[\"is_idle\"] == True].copy()\n\ntotal_active = int(df_active[\"duration_seconds\"].sum())\ntotal_idle = int(df_idle[\"duration_seconds\"].sum())\n\n# --- 1. Time per category ---\nby_category = (\n df_active.groupby(\"category\")[\"duration_seconds\"]\n .sum()\n .sort_values(ascending=False)\n .astype(int)\n .to_dict()\n)\n\n# --- 2. Top 10 apps + Others ---\napp_totals = (\n df_active.groupby(\"window_title\")[\"duration_seconds\"]\n .sum()\n .sort_values(ascending=False)\n)\ntop_10 = app_totals.head(10).astype(int).to_dict()\nothers_sum = int(app_totals.iloc[10:].sum())\nif others_sum > 0:\n top_10[\"Others\"] = others_sum\ntop_apps_pie = top_10\n\n# --- 3. Weekly breakdown ---\ndf_active[\"week\"] = df_active[\"start_time\"].dt.isocalendar().week.astype(int)\ndf_active[\"week_label\"] = df_active[\"start_time\"].dt.strftime(\"W%V %d.%m\")\n# Group by week number, use first date as label\nweek_labels = (\n df_active.groupby(\"week\")[\"week_label\"]\n .first()\n .to_dict()\n)\nweekly_totals = (\n df_active.groupby(\"week\")[\"duration_seconds\"]\n .sum()\n .astype(int)\n .to_dict()\n)\nweekly_breakdown = {\n week_labels[w]: round(weekly_totals[w] / 3600, 2)\n for w in sorted(week_labels.keys())\n}\n\n# --- 4. Best day of week ---\nWEEKDAY_ORDER = [\"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\"]\ndf_active[\"weekday\"] = df_active[\"start_time\"].dt.strftime(\"%a\")\nbest_day = (\n df_active.groupby(\"weekday\")[\"duration_seconds\"]\n .sum()\n .reindex(WEEKDAY_ORDER, fill_value=0)\n .astype(int)\n .to_dict()\n)\n\n# --- 5. Hour of day distribution ---\ndf_active[\"hour\"] = df_active[\"start_time\"].dt.hour\nhour_dist = (\n df_active.groupby(\"hour\")[\"duration_seconds\"]\n .sum()\n .reindex(range(24), fill_value=0)\n .astype(int)\n .to_dict()\n)\nhour_dist = {str(k): v for k, v in hour_dist.items()}\n\n# --- 6. Daily calendar heatmap ---\ndf_active[\"day\"] = df_active[\"start_time\"].dt.strftime(\"%Y-%m-%d\")\ndaily_totals = (\n df_active.groupby(\"day\")[\"duration_seconds\"]\n .sum()\n .astype(int)\n .to_dict()\n)\n\nreturn [{\n \"json\": {\n \"total_active_seconds\": total_active,\n \"total_idle_seconds\": total_idle,\n \"by_category\": by_category,\n \"top_apps_pie\": top_apps_pie,\n \"weekly_breakdown\": weekly_breakdown,\n \"best_day\": best_day,\n \"hour_dist\": hour_dist,\n \"daily_totals\": daily_totals,\n \"weekday_order\": WEEKDAY_ORDER,\n }\n}]"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
368,
704
],
"id": "46726cc2-06f6-4a6a-a9b0-5ff1e94a8029",
"name": "Month analysis"
},
{
"parameters": {
"language": "pythonNative",
"pythonCode": "import json\nimport urllib.request\nimport urllib.parse\n\ndata = _items[0]['json']\n\nCOLORS = [\n '#4C9BE8', '#E87B4C', '#4CE87B', '#E84C9B',\n '#9B4CE8', '#E8E84C', '#4CE8E8', '#E84C4C',\n '#9BE84C', '#4C4CE8', '#888888'\n]\n\ndef make_short_url(config):\n payload = json.dumps({\n \"chart\": config,\n \"width\": 600,\n \"height\": 300,\n \"backgroundColor\": \"#1a1a2e\"\n }).encode('utf-8')\n req = urllib.request.Request(\n \"https://quickchart.io/chart/create\",\n data=payload,\n headers={\"Content-Type\": \"application/json\"},\n method=\"POST\"\n )\n with urllib.request.urlopen(req, timeout=10) as resp:\n result = json.loads(resp.read().decode())\n return result[\"url\"]\n\n# --- Chart 1: Time per category (horizontal bar) ---\ncat_items = [(k, round(v / 3600, 2)) for k, v in data['by_category'].items() if v > 0]\ncats = [x[0] for x in cat_items]\nvals = [x[1] for x in cat_items]\n\nchart_category = make_short_url({\n \"type\": \"horizontalBar\",\n \"data\": {\n \"labels\": cats,\n \"datasets\": [{\n \"label\": \"Hours\",\n \"data\": vals,\n \"backgroundColor\": COLORS[:len(cats)]\n }]\n },\n \"options\": {\n \"legend\": {\"labels\": {\"fontColor\": \"white\"}},\n \"scales\": {\n \"xAxes\": [{\"ticks\": {\"fontColor\": \"white\"}, \"gridLines\": {\"color\": \"#333\"}}],\n \"yAxes\": [{\"ticks\": {\"fontColor\": \"white\"}, \"gridLines\": {\"color\": \"#333\"}}]\n }\n }\n})\n\n# --- Chart 2: Top apps pie ---\napp_labels = list(data['top_apps_pie'].keys())\napp_vals_seconds = list(data['top_apps_pie'].values())\napp_vals = [round(v / 60) for v in app_vals_seconds] # minutes, for chart display\n\nshort_labels = [\n (l[:30] + '...' if len(l) > 30 else l) + f\" [{sec_to_hm(v)}]\"\n for l, v in zip(app_labels, app_vals_seconds) # legend uses original seconds\n]\n\nchart_top_apps = make_short_url({\n \"type\": \"pie\",\n \"data\": {\n \"labels\": short_labels,\n \"datasets\": [{\n \"data\": app_vals,\n \"backgroundColor\": COLORS[:len(app_vals)]\n }]\n },\n \"options\": {\n \"legend\": {\n \"position\": \"right\",\n \"labels\": {\"fontColor\": \"white\", \"fontSize\": 10}\n }\n }\n})\n\n# --- Chart 3: Active vs Idle doughnut ---\nactive_h = round(data['total_active_seconds'] / 3600, 2)\nidle_h = round(data['total_idle_seconds'] / 3600, 2)\nif active_h == 0 and idle_h == 0:\n idle_h = 0.01\n\ntotal_h = active_h + idle_h\nactive_pct = round(100 * active_h / total_h, 1) if total_h > 0 else 0\nidle_pct = round(100 * idle_h / total_h, 1) if total_h > 0 else 0\n\nchart_idle = make_url({\n \"type\": \"doughnut\",\n \"data\": {\n \"labels\": [f\"Active {active_h}h ({active_pct}%)\", f\"Idle {idle_h}h ({idle_pct}%)\"],\n \"datasets\": [{\n \"data\": [active_h, idle_h],\n \"backgroundColor\": [\"#4C9BE8\", \"#E84C4C\"]\n }]\n },\n \"options\": {\n \"legend\": {\n \"position\": \"bottom\",\n \"labels\": {\"fontColor\": \"white\"}\n }\n }\n})\n\n# --- Chart 4: Weekly breakdown bar ---\nweek_labels = list(data['weekly_breakdown'].keys())\nweek_vals = list(data['weekly_breakdown'].values())\n\nchart_weekly = make_short_url({\n \"type\": \"bar\",\n \"data\": {\n \"labels\": week_labels,\n \"datasets\": [{\n \"label\": \"Hours\",\n \"data\": week_vals,\n \"backgroundColor\": \"#4CE87B\"\n }]\n },\n \"options\": {\n \"legend\": {\"labels\": {\"fontColor\": \"white\"}},\n \"scales\": {\n \"xAxes\": [{\"ticks\": {\"fontColor\": \"white\"}, \"gridLines\": {\"color\": \"#333\"}}],\n \"yAxes\": [{\"ticks\": {\"fontColor\": \"white\"}, \"gridLines\": {\"color\": \"#333\"}}]\n }\n }\n})\n\n# --- Chart 5: Best day of week ---\ndays = data['weekday_order']\nday_vals = [round(data['best_day'].get(d, 0) / 3600, 2) for d in days]\n\nchart_best_day = make_short_url({\n \"type\": \"bar\",\n \"data\": {\n \"labels\": days,\n \"datasets\": [{\n \"label\": \"Hours\",\n \"data\": day_vals,\n \"backgroundColor\": COLORS[:len(days)]\n }]\n },\n \"options\": {\n \"legend\": {\"labels\": {\"fontColor\": \"white\"}},\n \"scales\": {\n \"xAxes\": [{\"ticks\": {\"fontColor\": \"white\"}, \"gridLines\": {\"color\": \"#333\"}}],\n \"yAxes\": [{\"ticks\": {\"fontColor\": \"white\"}, \"gridLines\": {\"color\": \"#333\"}}]\n }\n }\n})\n\n# --- Chart 6: Hour of day distribution ---\nhours = [str(h) for h in range(24)]\nhour_vals = [round(data['hour_dist'].get(str(h), 0) / 3600, 2) for h in range(24)]\n\nchart_hour = make_short_url({\n \"type\": \"bar\",\n \"data\": {\n \"labels\": hours,\n \"datasets\": [{\n \"label\": \"Hours\",\n \"data\": hour_vals,\n \"backgroundColor\": \"#9B4CE8\"\n }]\n },\n \"options\": {\n \"legend\": {\"labels\": {\"fontColor\": \"white\"}},\n \"scales\": {\n \"xAxes\": [{\"ticks\": {\"fontColor\": \"white\"}, \"gridLines\": {\"color\": \"#333\"}}],\n \"yAxes\": [{\"ticks\": {\"fontColor\": \"white\"}, \"gridLines\": {\"color\": \"#333\"}}]\n }\n }\n})\n\n# --- Chart 7: Daily calendar heatmap (bubble) ---\ndaily_data = []\nsorted_days = sorted(data['daily_totals'].keys())\nfor i, day in enumerate(sorted_days):\n secs = data['daily_totals'][day]\n daily_data.append({\n \"x\": i + 1,\n \"y\": 0,\n \"r\": min(20, max(3, int(secs / 60)))\n })\n\nday_labels = [d[8:] + \".\" + d[5:7] for d in sorted_days]\n\nchart_calendar = make_short_url({\n \"type\": \"bubble\",\n \"data\": {\n \"datasets\": [{\n \"label\": \"Activity\",\n \"data\": daily_data,\n \"backgroundColor\": \"#4C9BE8\"\n }]\n },\n \"options\": {\n \"legend\": {\"labels\": {\"fontColor\": \"white\"}},\n \"scales\": {\n \"xAxes\": [{\n \"ticks\": {\"fontColor\": \"white\", \"min\": 1, \"max\": len(sorted_days)},\n \"gridLines\": {\"color\": \"#333\"},\n \"scaleLabel\": {\"display\": True, \"labelString\": \"Day of month\", \"fontColor\": \"white\"}\n }],\n \"yAxes\": [{\n \"ticks\": {\"fontColor\": \"white\", \"display\": False},\n \"gridLines\": {\"color\": \"#333\"}\n }]\n }\n }\n})\n\nreturn [{'json': {\n 'chart_category': chart_category,\n 'chart_top_apps': chart_top_apps,\n 'chart_idle': chart_idle,\n 'chart_weekly': chart_weekly,\n 'chart_best_day': chart_best_day,\n 'chart_hour': chart_hour,\n 'chart_calendar': chart_calendar,\n 'total_active_seconds': data['total_active_seconds'],\n 'total_idle_seconds': data['total_idle_seconds'],\n 'by_category': data['by_category'],\n 'weekday_order': data['weekday_order'],\n}}]"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
576,
704
],
"id": "dd35571d-3919-4202-b14f-c151622f86a8",
"name": "Month graphs creator"
},
{
"parameters": {
"language": "pythonNative",
"pythonCode": "data = _items[0]['json']\n\ndef seconds_to_hm(s):\n h = int(s // 3600)\n m = int((s % 3600) // 60)\n return f\"{h}h {m}m\"\n\ntotal_active = seconds_to_hm(data['total_active_seconds'])\ntotal_idle = seconds_to_hm(data['total_idle_seconds'])\n\ncategory_rows = \"\"\nfor cat, secs in data['by_category'].items():\n category_rows += f\"<tr><td style='padding:6px 12px;color:#ccc;'>{cat}</td><td style='padding:6px 12px;color:#4C9BE8;font-weight:bold;'>{seconds_to_hm(secs)}</td></tr>\"\n\nhtml = f\"\"\"\n<!DOCTYPE html>\n<html>\n<head>\n<meta charset=\"utf-8\">\n<style>\n body {{ font-family: Arial, sans-serif; background: #0f0f1a; color: #ffffff; margin: 0; padding: 0; }}\n .container {{ max-width: 800px; margin: 0 auto; padding: 30px 20px; }}\n .header {{ background: linear-gradient(135deg, #1a1a2e, #16213e); border-radius: 12px; padding: 30px; margin-bottom: 30px; text-align: center; border: 1px solid #4CE87B; }}\n .header h1 {{ margin: 0; font-size: 28px; color: #4CE87B; }}\n .header p {{ margin: 8px 0 0; color: #aaa; font-size: 14px; }}\n .stats {{ width: 100%; margin-bottom: 30px; }}\n .stat-box {{ display: inline-block; width: 30%; background: #1a1a2e; border-radius: 10px; padding: 20px; text-align: center; border: 1px solid #333; vertical-align: top; }}\n .stat-box .value {{ font-size: 26px; font-weight: bold; color: #4CE87B; }}\n .stat-box .label {{ font-size: 12px; color: #888; margin-top: 5px; }}\n .section {{ background: #1a1a2e; border-radius: 10px; padding: 20px; margin-bottom: 25px; border: 1px solid #333; }}\n .section h2 {{ margin: 0 0 15px; font-size: 16px; color: #4CE87B; border-bottom: 1px solid #333; padding-bottom: 10px; }}\n .chart img {{ width: 100% !important; max-width: 100% !important; height: auto !important; border-radius: 8px; display: block; }}\n table {{ width: 100%; border-collapse: collapse; }}\n tr:nth-child(even) {{ background: #16213e; }}\n .footer {{ text-align: center; color: #555; font-size: 11px; margin-top: 30px; }}\n</style>\n</head>\n<body>\n<div class=\"container\">\n\n <div class=\"header\">\n <h1>Dev Activity \u2014 Monthly Report</h1>\n <p>Full month summary</p>\n </div>\n\n <div class=\"stats\">\n <div class=\"stat-box\">\n <div class=\"value\">{total_active}</div>\n <div class=\"label\">Active Time</div>\n </div>\n <div class=\"stat-box\">\n <div class=\"value\">{total_idle}</div>\n <div class=\"label\">Idle Time</div>\n </div>\n <div class=\"stat-box\">\n <div class=\"value\">{len(data['by_category'])}</div>\n <div class=\"label\">Categories</div>\n </div>\n </div>\n\n <div class=\"section\">\n <h2>Time per Category</h2>\n <table>{category_rows}</table>\n </div>\n\n <div class=\"section chart\">\n <h2>Category Breakdown</h2>\n <img width=\"700\" src=\"{data['chart_category']}\">\n </div>\n\n <div class=\"section chart\">\n <h2>Weekly Breakdown</h2>\n <img width=\"700\" src=\"{data['chart_weekly']}\">\n </div>\n\n <div class=\"section chart\">\n <h2>Best Day of Week</h2>\n <img width=\"700\" src=\"{data['chart_best_day']}\">\n </div>\n\n <div class=\"section chart\">\n <h2>Peak Hours</h2>\n <img width=\"700\" src=\"{data['chart_hour']}\">\n </div>\n\n <div class=\"section chart\">\n <h2>Top Apps</h2>\n <img width=\"700\" src=\"{data['chart_top_apps']}\">\n </div>\n\n <div class=\"section chart\">\n <h2>Active vs Idle</h2>\n <img width=\"700\" src=\"{data['chart_idle']}\">\n </div>\n\n <div class=\"section chart\">\n <h2>Daily Activity Calendar</h2>\n <img width=\"700\" src=\"{data['chart_calendar']}\">\n </div>\n\n <div class=\"footer\">\n Generated by Dev-Tracker \u00b7 Privacy-first activity monitoring\n </div>\n\n</div>\n</body>\n</html>\n\"\"\"\n\nreturn [{'json': {'subject': 'Dev Activity \u2014 Monthly Report', 'html': html}}]"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
784,
704
],
"id": "380f096d-fe36-4585-b97a-5b575629f759",
"name": "Month to HTML"
},
{
"parameters": {
"fromEmail": "={{ $env.N8N_AGENT_EMAIL_FROM }}",
"toEmail": "={{ $env.DEV_ACTIVITY_DAEMON_EMAIL_TO }}",
"subject": "={{ $json.subject }}",
"html": "={{ $json.html }}",
"options": {}
},
"type": "n8n-nodes-base.emailSend",
"typeVersion": 2.1,
"position": [
992,
464
],
"id": "f1ce02fc-3a1b-4fef-b283-489a3b8211ef",
"name": "Send weekly report",
"credentials": {
"smtp": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"fromEmail": "={{ $env.N8N_AGENT_EMAIL_FROM }}",
"toEmail": "={{ $env.DEV_ACTIVITY_DAEMON_EMAIL_TO }}",
"subject": "={{ $json.subject }}",
"html": "={{ $json.html }}",
"options": {}
},
"type": "n8n-nodes-base.emailSend",
"typeVersion": 2.1,
"position": [
992,
704
],
"id": "ed6876bf-f22a-4470-bff8-4ae897dc5cda",
"name": "Send monthly report",
"credentials": {
"smtp": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"content": "Data collector",
"height": 208,
"width": 880
},
"type": "n8n-nodes-base.stickyNote",
"position": [
-96,
176
],
"typeVersion": 1,
"id": "6010e37a-e3ff-498b-822b-2d455cfea2ef",
"name": "Sticky Note"
},
{
"parameters": {
"content": "Weekly report",
"height": 208,
"width": 1296
},
"type": "n8n-nodes-base.stickyNote",
"position": [
-96,
416
],
"typeVersion": 1,
"id": "ad6bb791-9b28-4afa-a9bb-33fb48d2586c",
"name": "Sticky Note1"
},
{
"parameters": {
"content": "Monthly report",
"height": 208,
"width": 1296
},
"type": "n8n-nodes-base.stickyNote",
"position": [
-96,
656
],
"typeVersion": 1,
"id": "035d273b-e375-4ce6-bc22-46255f8e9642",
"name": "Sticky Note2"
}
],
"connections": {
"Weekly trigger": {
"main": [
[
{
"node": "Weekly getter",
"type": "main",
"index": 0
}
]
]
},
"Weekly getter": {
"main": [
[
{
"node": "Week analysis",
"type": "main",
"index": 0
}
]
]
},
"Week analysis": {
"main": [
[
{
"node": "Graphs creator",
"type": "main",
"index": 0
}
]
]
},
"Graphs creator": {
"main": [
[
{
"node": "Format to HTML",
"type": "main",
"index": 0
}
]
]
},
"Format to HTML": {
"main": [
[
{
"node": "Send weekly report",
"type": "main",
"index": 0
}
]
]
},
"Monthly trigger": {
"main": [
[
{
"node": "Monthly getter",
"type": "main",
"index": 0
}
]
]
},
"Data listener": {
"main": [
[
{
"node": "Split data to rows",
"type": "main",
"index": 0
}
]
]
},
"Split data to rows": {
"main": [
[
{
"node": "Change date time",
"type": "main",
"index": 0
}
]
]
},
"Change date time": {
"main": [
[
{
"node": "Insert data rows to n8n table",
"type": "main",
"index": 0
}
]
]
},
"Monthly getter": {
"main": [
[
{
"node": "Month analysis",
"type": "main",
"index": 0
}
]
]
},
"Month analysis": {
"main": [
[
{
"node": "Month graphs creator",
"type": "main",
"index": 0
}
]
]
},
"Month graphs creator": {
"main": [
[
{
"node": "Month to HTML",
"type": "main",
"index": 0
}
]
]
},
"Month to HTML": {
"main": [
[
{
"node": "Send monthly report",
"type": "main",
"index": 0
}
]
]
}
},
"active": true,
"settings": {
"executionOrder": "v1",
"binaryMode": "separate",
"availableInMCP": false
},
"versionId": "f38f3889-1802-4c0d-960f-af69bf8208b0",
"meta": {
"templateCredsSetupCompleted": true
},
"id": "zRnQTWANCXVr2WvG",
"tags": [
{
"updatedAt": "2026-04-02T17:33:04.440Z",
"createdAt": "2026-04-02T17:33:04.440Z",
"id": "FQeOsmVLsgzoXLMD",
"name": "data table"
},
{
"updatedAt": "2026-03-12T07:14:31.968Z",
"createdAt": "2026-03-12T07:14:31.968Z",
"id": "LMI2wwmKQQRYls3c",
"name": "mail"
},
{
"updatedAt": "2026-04-02T17:32:50.941Z",
"createdAt": "2026-04-02T17:32:50.941Z",
"id": "m6g38sSfTF4up3Ao",
"name": "webhook"
},
{
"updatedAt": "2026-04-02T17:32:45.662Z",
"createdAt": "2026-04-02T17:32:45.662Z",
"id": "mq5yd5kT2zxvGvAc",
"name": "python"
}
]
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
smtp
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About this workflow
dev_activity_reporter. Uses dataTable, emailSend. Scheduled trigger; 19 nodes.
Source: https://github.com/Jarkendar/dev_activity_deamon/blob/71b43e5a962246e1e25bbdfb9898ecde7dc5f953/n8n/dev_activity_reporter.json — original creator credit. Request a take-down →
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