Tiktok Analytics

Track your TikTok video performance over time using a SQLite database. Combines three data sources:

1. Apify scraping - automated public metrics (views, likes, comments, shares, saves)
2. CSV imports - TikTok's exported Overview and Content CSVs
3. Screenshot reading - you screenshot TikTok's per-video analytics and I extract the deep metrics (watch time, retention, traffic sources, followers)

Database: ~/.claude/skills/tiktok-analytics/data/tiktok.db Analysis files: ~/content/youtube/tiktok-analytics/

Parsing Arguments

Parse $ARGUMENTS to determine the mode:

Mode: screenshots (or user provides screenshot images)

The user has provided TikTok analytics screenshots. Read each image and extract:

• Video title/caption
• Video views
• Total play time
• Average watch time
• Watched full video %
• New followers
• Traffic source breakdown (For You %, Search %, Personal profile %, Following %, DM %, Sound %, Other %)
• Retention rate info (where viewers stopped watching)

For each screenshot:

1. Match the video to an existing record in the database by caption (search with search_videos())
2. If not found, ask the user for the TikTok URL so you can extract the video ID
3. Save the deep metrics using save_deep_metrics()
4. Also update the public metrics from the screenshot header (views, likes, comments, shares) using save_metrics()

After processing all screenshots, show a summary table of what was saved.

Mode: import <csv-path>

Import a TikTok CSV export.

Auto-detect the CSV type by reading the header:

• If headers include "Video Views", "Profile Views" -> Overview CSV
• If headers include "Video title", "Video link" -> Content CSV

Run the appropriate import:

python3 ~/.claude/skills/tiktok-analytics/import_csv.py overview "<csv-path>" --year 2026
# or
python3 ~/.claude/skills/tiktok-analytics/import_csv.py content "<csv-path>" --year 2026

Show results after import.

Mode: scrape

Scrape the user's TikTok profile to pull latest public metrics for all videos.

python3 ~/.claude/skills/tiktok-analytics/scrape_profile.py @codewithtyler --limit 100

Show before/after comparison for any videos that had metrics changes since the last snapshot.

Mode: report (or no arguments, or "how's my tiktok")

Generate a performance report from the database.

python3 ~/.claude/skills/tiktok-analytics/tiktok_db.py

Then query the database directly to build the report:

import sys
sys.path.insert(0, str(__import__('pathlib').Path.home() / '.claude/skills/tiktok-analytics'))
from tiktok_db import get_connection, init_all_tables, get_latest_metrics, get_summary, get_daily_overview

Build a report that includes:

Account Overview

• Total videos tracked
• Total views across all videos
• Date range of data
• Daily overview trends (if available)

Top Videos (by latest views) | # | Video | Posted | Views | Likes | Comments | Shares | Eng% | With deep metrics columns if available: | Avg Watch | Full Watch % | FYP % | Search % | New Followers |

Trends

• Which videos are still growing (compare snapshots)
• Best performing hooks/formats
• Traffic source patterns

Recommendations

• Based on what's working, suggest what to make next

Save the report to ~/content/youtube/tiktok-analytics/YYYY-MM-DD-report.md

Mode: log <search-term> <metrics>

Quick manual entry for a single video's deep metrics. The user will say something like: "log tiktok: carousels video, 13.48s avg watch, 2.9% full watch, 90% FYP, 7% search, 3 new followers"

1. Search for the video by the search term
2. If multiple matches, ask which one
3. Parse the metrics from the natural language
4. Save via save_deep_metrics()
5. Confirm what was saved

Database Access Pattern

All database operations go through the Python module:

import sys
sys.path.insert(0, str(__import__('pathlib').Path.home() / '.claude/skills/tiktok-analytics'))
from tiktok_db import (
    get_connection, init_all_tables,
    upsert_video, get_all_videos, get_video_by_id, search_videos,
    save_metrics, get_metrics_history, get_latest_metrics,
    save_deep_metrics, get_deep_metrics,
    save_daily_overview, get_daily_overview,
    get_summary,
)

Or via direct sqlite3 queries for complex reports:

python3 -c "
import sqlite3
conn = sqlite3.connect(str(__import__('pathlib').Path.home() / '.claude/skills/tiktok-analytics/data/tiktok.db'))
conn.row_factory = sqlite3.Row
# your query here
"

Screenshot Reading Tips

When reading TikTok analytics screenshots, look for these specific elements:

• Top banner: video title, post date, view/like/comment/share counts (small icons with numbers)
• Metrics row: Video views, Total play time, Average watch time, Watched full video %, New followers
• Graph: Daily view trend (first 7 days after posting)
• Retention rate: Shows where viewers dropped off (e.g., "Most viewers stopped watching at 0:02")
• Traffic source: Bar chart with percentages for For You, Search, Personal profile, Following, DM, Sound, Other

Extract ALL of these fields. Don't skip any.

Rules

• Always initialize tables before any DB operation: init_all_tables(conn)
• Use --year 2026 when importing CSVs (TikTok only includes month/day, not year)
• Profile is always @codewithtyler unless the user says otherwise
• When showing reports, include engagement rate: (likes + comments + shares) / views * 100
• Deep metrics are the most valuable - encourage the user to screenshot their top performers
• Save analysis reports to ~/content/youtube/tiktok-analytics/ not in the skill directory
• When processing screenshots, always confirm what you extracted before saving to DB