docs(10_Wiki): Topic_Business/General/Graphic/Programming을 Topics/ 하위로 이동
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Antigravity Agent
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---
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id: wiki-2026-0508-my-videos-check
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title: my_videos_check (Personal YouTube Channel Monitor)
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Channel Health Monitor, YT Self-monitor, Video Stats Watcher]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.85
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verification_status: applied
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tags: [youtube-api, monitoring, cron, analytics, creator-tooling]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: Python 3.12
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framework: YouTube Analytics API + DuckDB + cron
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---
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# my_videos_check (Personal YouTube Channel Monitor)
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## 매 한 줄
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> **"매 own 의 YouTube 채널 의 매 video 마다 매 view/like/comment/CTR/AVD 의 daily snapshot 을 매 fetch → DuckDB → 매 anomaly alert"**. 2026 creator workflow 의 기본 component. 매 YouTube Studio 의 dashboard 보다 훨씬 매 customizable + 매 multi-channel 비교 + 매 LLM 기반 insight.
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## 매 핵심
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### 매 Data sources
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- **YouTube Data API v3**: video metadata, current snapshot stats.
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- **YouTube Analytics API v2**: time-series (impressions, CTR, AVD, retention, traffic source) — 매 OAuth 필요.
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- **YouTube Reporting API**: bulk daily CSV (매 large channel 에 적합).
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### 매 Snapshot schema
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- `video_id, captured_at, views, likes, comments, watch_time_min, avd_sec, ctr, impressions`.
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- Time-series: `(video_id, day, metric)` — 매 partitioned.
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### 매 Alerts
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- View rate (24h growth) 가 매 baseline 의 3σ 밖.
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- 매 Comment rate spike — possible viral 또는 controversy.
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- CTR drop > 30% on recent uploads.
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- 매 watch time 의 sudden cliff at specific timestamp (retention curve).
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### 매 응용
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1. 매 daily morning briefing (Telegram bot).
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2. Auto-thumbnail A/B 결정.
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3. 매 evergreen vs. 매 short-lived video classification.
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4. Topic-level trending in own catalog.
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## 💻 패턴
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### OAuth setup (one-time)
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```python
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from google_auth_oauthlib.flow import InstalledAppFlow
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SCOPES = ['https://www.googleapis.com/auth/yt-analytics.readonly',
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'https://www.googleapis.com/auth/youtube.readonly']
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flow = InstalledAppFlow.from_client_secrets_file('client_secret.json', SCOPES)
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creds = flow.run_local_server(port=0)
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with open('token.json', 'w') as f: f.write(creds.to_json())
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```
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### Daily snapshot
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```python
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from googleapiclient.discovery import build
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from google.oauth2.credentials import Credentials
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import duckdb, datetime as dt
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creds = Credentials.from_authorized_user_file('token.json')
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yt = build('youtube', 'v3', credentials=creds)
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yta = build('youtubeAnalytics', 'v2', credentials=creds)
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con = duckdb.connect('mychannel.db')
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con.execute("""CREATE TABLE IF NOT EXISTS snapshots(
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video_id VARCHAR, captured_at TIMESTAMP, views BIGINT, likes BIGINT,
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comments BIGINT, watch_min DOUBLE, avd_sec DOUBLE, ctr DOUBLE, impressions BIGINT,
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PRIMARY KEY (video_id, captured_at)
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)""")
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def list_my_videos():
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res = yt.search().list(forMine=True, type='video', part='id', maxResults=50).execute()
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return [item['id']['videoId'] for item in res['items']]
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def fetch_snapshot(video_ids):
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res = yt.videos().list(part='statistics,contentDetails,snippet', id=','.join(video_ids)).execute()
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rows = []
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for v in res['items']:
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s = v['statistics']
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rows.append({
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'video_id': v['id'],
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'views': int(s.get('viewCount', 0)),
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'likes': int(s.get('likeCount', 0)),
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'comments': int(s.get('commentCount', 0)),
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})
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return rows
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def fetch_analytics(video_id, days=7):
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end = dt.date.today()
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start = end - dt.timedelta(days=days)
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res = yta.reports().query(
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ids='channel==MINE', startDate=str(start), endDate=str(end),
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metrics='views,estimatedMinutesWatched,averageViewDuration,impressions,impressionsCtr',
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dimensions='video', filters=f'video=={video_id}',
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).execute()
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return res.get('rows', [[]])[0] if res.get('rows') else None
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```
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### Anomaly detector (Z-score)
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```python
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import statistics
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def is_spike(video_id, metric='views', window=14, z=3.0):
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rows = con.execute(f"""
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SELECT {metric} FROM snapshots WHERE video_id=?
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ORDER BY captured_at DESC LIMIT {window+1}
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""", [video_id]).fetchall()
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if len(rows) < window + 1: return False
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today, hist = rows[0][0], [r[0] for r in rows[1:]]
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deltas = [hist[i] - hist[i+1] for i in range(len(hist)-1)]
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today_delta = today - hist[0]
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if not deltas or statistics.pstdev(deltas) == 0: return False
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return abs(today_delta - statistics.mean(deltas)) / statistics.pstdev(deltas) > z
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```
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### Telegram alert
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```python
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import requests, os
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def alert(msg):
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requests.post(
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f"https://api.telegram.org/bot{os.environ['TG_TOKEN']}/sendMessage",
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json={'chat_id': os.environ['TG_CHAT'], 'text': msg, 'parse_mode': 'Markdown'},
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)
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for vid in list_my_videos():
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if is_spike(vid):
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title = con.execute("SELECT title FROM videos WHERE video_id=?", [vid]).fetchone()[0]
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alert(f"*Spike*: [{title}](https://youtu.be/{vid})")
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```
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### LLM weekly digest (Claude)
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```python
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from anthropic import Anthropic
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top = con.execute("""
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SELECT v.title, s.views, s.ctr, s.avd_sec
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FROM snapshots s JOIN videos v USING(video_id)
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WHERE s.captured_at::DATE = current_date
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ORDER BY s.views DESC LIMIT 10
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""").fetchall()
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resp = Anthropic().messages.create(
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model='claude-opus-4-7',
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max_tokens=1000,
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messages=[{'role': 'user', 'content':
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f"Weekly channel digest. Identify 3 actions. Data:\n{top}"}],
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)
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print(resp.content[0].text)
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```
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### Cron (systemd timer)
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```ini
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# ~/.config/systemd/user/yt-check.timer
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[Unit]
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Description=Daily YouTube channel snapshot
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[Timer]
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OnCalendar=*-*-* 09:00:00
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Persistent=true
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[Install]
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WantedBy=timers.target
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```
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## 매 결정 기준
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| 상황 | Approach |
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| Single channel, ≤ 500 videos | API v3 + Analytics v2, daily |
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| 5k+ videos | Reporting API bulk CSV |
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| Real-time spike | poll every 15m for new uploads only |
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| Multi-channel agency | per-channel OAuth tokens, rate-limit pool |
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| Privacy / no Google | 매 X — Analytics 의 own data 만 own 가 access |
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**기본값**: daily 09:00 cron + Analytics v2 + DuckDB + 3σ Z-score alert + Telegram + weekly LLM digest.
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## 🔗 Graph
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- 변형: [[comment_harvester]]
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- 응용: [[Telegram-Notify]] · [[Anomaly-Detection]]
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- Adjacent: [[DuckDB]]
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## 🤖 LLM 활용
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**언제**: weekly digest, anomaly explanation, A/B thumbnail copy 의 generation.
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**언제 X**: 매 ground-truth metric 의 fabrication — always cite raw numbers.
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## ❌ 안티패턴
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- **Polling stats every minute**: quota 의 burn — 매 actual update lag 이 hours.
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- **No baseline window**: every uptick = "spike" = noise.
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- **Storing only current snapshot**: 매 trend 의 재구성 불가.
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- **Hard-coded video list**: 매 new upload 의 miss.
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- **OAuth token in repo**: revoke 즉시 필요. Use secret manager.
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## 🧪 검증 / 중복
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- Verified (YouTube Data API v3, Analytics API v2 docs, YouTube Reporting API guide).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — channel monitor + anomaly + LLM digest |
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