Agent skill

Video Analytics Interpreter

by nicepkg in nicepkg/ai-workflow

Interpret YouTube Analytics, TikTok Analytics, and video performance data.

MITAuto-check passedMarketing & SEO

Install Video Analytics Interpreter

skills CLI
$ npx skills add nicepkg/ai-workflow --skill video-analytics-interpreter -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install nicepkg/ai-workflow video-analytics-interpreter --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/nicepkg/ai-workflow.git skills-src && mkdir -p .claude/skills && cp -r skills-src/workflows/video-creator-workflow/.claude/skills/video-analytics-interpreter .claude/skills/video-analytics-interpreter && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
video-analytics-interpreter
GitHub stars
285
Token cost
~2.3k tokens
SKILL.md length
61 words
Files
1
Skills in repo
75
Repo updated
First seen
Licence
MIT

At a glance

Interpret YouTube Analytics, TikTok Analytics, and video performance data.

  • Works in 3 steps: Identify the Pattern → Diagnose the Problem → Read the Retention Graph
  • Analyzing video performance
  • SKILL.md covers Key Metrics Explained, Analytics Interpretation…, Analytics Report Template and How to Use, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Video Analytics Interpreter is an agent skill from nicepkg/ai-workflow. Interpret YouTube Analytics, TikTok Analytics, and video performance data. Identifies trends, explains metrics, and provides actionable recommendations for growth. Use when analyzing video performance, understanding metrics, or optimizing channel strategy.

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Marketing & SEO, covering Go-to-market strategy. It works with TikTok and YouTube. The repository describes itself as: 🚀 170+ pre-built skills for Claude Code, Cursor, Codex & 14+ AI tools. Stop re-teaching your AI the same things. One command → instant domain expertise. Marketing, SEO, Trading… The licence is MIT.

When your agent uses it

  • Analyzing video performance
  • Understanding metrics
  • Optimizing channel strategy

Example prompts

  • “/video-analytics-interpreter”

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Identify the Pattern
  2. Diagnose the Problem
  3. Read the Retention Graph

What it can do on your machine

Read from SKILL.md and the folder at commit d167b41. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Video Analytics Interpreter loads about 2.3k tokens when it runs. Until then it costs about 71 tokens; SKILL.md has 61 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~71
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from nicepkg/ai-workflow at commit d167b41, republished under its MIT licence (© nicepkg). 61 words, ~2,251 tokens.

Download SKILL.mdSave it as .claude/skills/video-analytics-interpreter/SKILL.md (or your agent's skills folder).
name
video-analytics-interpreter
description
Interpret YouTube Analytics, TikTok Analytics, and video performance data. Identifies trends, explains metrics, and provides actionable recommendations for growth. Use when analyzing video performance, understanding metrics, or optimizing channel strategy.

Video Analytics Interpreter

Transform raw video metrics into actionable growth strategies.

Key Metrics Explained

YouTube Analytics
📊 CORE METRICS:

VIEWS
- What: Total video plays (30+ seconds or full if shorter)
- Good: Trending upward week-over-week
- Warning: Sudden drops may indicate algorithm changes

WATCH TIME
- What: Total minutes watched
- Why it matters: #1 factor for YouTube algorithm
- Good: Higher than channel average

AVERAGE VIEW DURATION (AVD)
- What: Average time viewers watch
- Benchmark: 50%+ of video length is good
- Tip: Longer videos = lower % is acceptable

CLICK-THROUGH RATE (CTR)
- What: Impressions → Clicks percentage
- Good: 4-10% (varies by content type)
- Excellent: 10%+
- Warning: <2% needs thumbnail/title work

IMPRESSIONS
- What: Times thumbnail shown to users
- Note: Higher impressions = YouTube promoting you
- Tip: CTR × Impressions = Views potential

AUDIENCE RETENTION
- What: Graph showing when viewers leave
- Key: Look for drop-off points
- Goal: Flat line is ideal, gradual decline acceptable

ENGAGEMENT RATE
- What: (Likes + Comments) / Views
- Good: 4-8%
- Excellent: 8%+
TikTok Analytics
📊 TIKTOK METRICS:

VIDEO VIEWS
- Includes replays and loops
- Higher than YouTube due to autoplay

AVERAGE WATCH TIME
- Critical for algorithm
- Goal: Above 100% (indicates replays)

WATCH FULL VIDEO RATE
- % who watched entire video
- Good: 30%+ for 15-30 sec videos
- Excellent: 50%+

ENGAGEMENT RATE
- (Likes + Comments + Shares) / Views
- Good: 5-10%
- Viral potential: 15%+

SHARES
- Most important engagement type
- Strong shares = algorithm boost
- Indicates "save for later" or "send to friend"

PROFILE VIEWS
- Viewers who clicked your profile
- Indicates content sparked curiosity
- Goal: 1-3% of views

FOLLOWER CONVERSION
- New followers / Profile views
- Good: 10-20%
- Tip: Pin best content, optimize bio

Analytics Interpretation Framework

Step 1: Identify the Pattern
PERFORMANCE CATEGORIES:

🚀 BREAKOUT SUCCESS (Top 10% of your content)
- Views: 3x+ your average
- CTR: Above your channel average
- Retention: Higher than similar videos
- Action: Double down, create more like this

✅ SOLID PERFORMER (Above average)
- Views: 1.5-3x your average
- CTR: At or above average
- Retention: Consistent with similar content
- Action: Note what worked, iterate

😐 AVERAGE
- Views: Near your typical numbers
- CTR: Around channel average
- Retention: Normal patterns
- Action: Test new elements

⚠️ UNDERPERFORMER (Below average)
- Views: Below your average
- CTR: Lower than normal
- Retention: Early drop-offs
- Action: Analyze what went wrong

❌ FLOP (Bottom 10%)
- Views: Significantly below average
- CTR: Much lower than normal
- Retention: Severe early drop-off
- Action: Don't delete - learn from it
Step 2: Diagnose the Problem
LOW VIEWS DIAGNOSIS:

Q1: Is CTR low?
YES → Thumbnail/title problem
NO → Algorithm not promoting (impressions issue)

Q2: Is retention low?
YES → Content/hook problem
NO → Distribution/timing issue

Q3: Low impressions but good CTR/retention?
YES → Algorithm testing phase, be patient
     → Or topic has limited search volume

DIAGNOSIS FLOWCHART:
┌─────────────────────────────────────────────────────────┐
│ Low Views?                                              │
│     ↓                                                   │
│ Check CTR → Low? → Fix thumbnail/title                  │
│     ↓                                                   │
│ CTR OK? → Check Retention → Low? → Fix hook/content     │
│     ↓                                                   │
│ Both OK? → Check Impressions → Low? → Topic/timing issue│
│     ↓                                                   │
│ All OK but low views? → Give it time, algorithm testing │
└─────────────────────────────────────────────────────────┘
Step 3: Read the Retention Graph
RETENTION PATTERNS:

📉 EARLY DROP (0-30 seconds)
Problem: Weak hook, misleading thumbnail/title
Fix: Stronger hook, match expectations

📉 GRADUAL DECLINE (Steady downward slope)
Normal: Expected for most videos
Optimize: Add pattern breaks every 30-60 sec

📉 CLIFF DROP (Sudden sharp decline)
Problem: Boring section, promise unfulfilled
Fix: Cut/improve that section, better pacing

📈 SPIKE (Retention increases)
Meaning: Replay/rewind point
Opportunity: Create clip from this moment

─── FLAT LINE (Stable)
Ideal: Viewers engaged throughout
Indicates: Strong content-hook match

Analytics Report Template

═══════════════════════════════════════════════════════════════
VIDEO ANALYTICS REPORT
Video: [Title]
Published: [Date]
Analysis Period: [X days since publish]
═══════════════════════════════════════════════════════════════

📊 PERFORMANCE SUMMARY:
─────────────────────────────────────────────────────────────
Views: [X] ([+/-X%] vs channel avg)
Watch Time: [X hours] ([+/-X%] vs channel avg)
CTR: [X%] ([+/-X%] vs channel avg)
Avg View Duration: [X:XX] ([X%] of video)
Engagement: [X%] (Likes: X, Comments: X)

Performance Tier: [🚀 Breakout / ✅ Solid / 😐 Average / ⚠️ Under / ❌ Flop]

📈 RETENTION ANALYSIS:
─────────────────────────────────────────────────────────────
Hook Effectiveness (0-30s): [Strong/Moderate/Weak]
- [X%] still watching at 30 seconds

Key Drop-off Points:
- [Timestamp]: [X%] dropped - Likely cause: [reason]
- [Timestamp]: [X%] dropped - Likely cause: [reason]

Rewatch Spikes:
- [Timestamp]: Viewers replayed - Content: [what happened]

Overall Shape: [Early drop / Gradual decline / Cliff / Flat]

🎯 TRAFFIC SOURCES:
─────────────────────────────────────────────────────────────
1. [Source]: [X%] - [Insight]
2. [Source]: [X%] - [Insight]
3. [Source]: [X%] - [Insight]

Best Performing Source: [Source] - Why: [explanation]
Underperforming Source: [Source] - Recommendation: [action]

👥 AUDIENCE INSIGHTS:
─────────────────────────────────────────────────────────────
New vs Returning: [X% new / X% returning]
Geographic: Top countries [list]
Demographics: Primary age/gender [if available]
Device: [X% mobile / X% desktop / X% TV]

Subscriber Impact: [+X subscribers] ([X%] conversion)

🔍 DIAGNOSIS:
─────────────────────────────────────────────────────────────
Primary Issue: [Identify main problem if any]
Root Cause: [Why this happened]
Secondary Issues: [Other concerns]

💡 RECOMMENDATIONS:
─────────────────────────────────────────────────────────────
IMMEDIATE (This Video):
1. [Action] - Expected impact: [result]
2. [Action] - Expected impact: [result]

FUTURE VIDEOS:
1. [Lesson learned] - Apply to: [future content]
2. [Lesson learned] - Apply to: [future content]

A/B TEST SUGGESTION:
- Test: [Element to test]
- Hypothesis: [What you expect]
- Measure: [Metric to track]

🎬 CLIP OPPORTUNITY:
─────────────────────────────────────────────────────────────
High-engagement moment at [Timestamp]: "[Description]"
Recommended for: [TikTok/Shorts/Reels]
Hook angle: "[Suggested hook]"

📅 NEXT STEPS:
─────────────────────────────────────────────────────────────
[ ] [Action item 1]
[ ] [Action item 2]
[ ] [Action item 3]
═══════════════════════════════════════════════════════════════

How to Use

Full Analytics Review
Analyze this video's performance:
[Paste analytics data or describe metrics]

Comparisons:
- Channel average CTR: [X%]
- Channel average retention: [X%]
- Similar video performance: [description]
Quick Diagnosis
My video has [X views] but my CTR is [X%].
Why is it underperforming and how do I fix it?
Trend Analysis
Analyze these videos' performance trends:
Video 1: [Title] - [Views, CTR, Retention]
Video 2: [Title] - [Views, CTR, Retention]
Video 3: [Title] - [Views, CTR, Retention]

What patterns do you see?
Channel Health Check
Here are my last 10 videos' metrics:
[List videos with key metrics]

How is my channel performing overall?
What should I focus on?

Benchmarks by Content Type

                    CTR      Retention    Engagement
Educational         3-6%     40-60%       3-6%
Entertainment       5-10%    30-50%       5-10%
Gaming              4-8%     25-45%       4-8%
Vlog                3-7%     35-55%       4-8%
Product Review      4-8%     40-60%       3-6%
Tutorial            3-6%     45-65%       3-5%
Short-form          8-15%    70-100%+     8-15%

Warning Signs to Watch

⚠️ IMMEDIATE ATTENTION:
- CTR dropped 50%+ from average
- Retention cliff in first 30 seconds
- Impressions declining week-over-week
- Subscriber loss instead of gain

📉 CONCERNING TRENDS:
- AVD decreasing over time
- Engagement rate declining
- More dislikes than usual
- Comments increasingly negative

© nicepkg, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in workflows/video-creator-workflow/.claude/skills/video-analytics-interpreter of nicepkg/ai-workflow.

Open the folder on GitHubat commit d167b41

Compare with similar skills

Video Analytics Interpreter next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Video Analytics Interpreter compared with similar skills
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Video Analytics Interpreter this skillnicepkg/ai-workflow285—~2.3kAutomated safety check: PassMIT
Paid Ads AuditAgriciDaniel/claude-ads9.9k—~1.5kAutomated safety check: PassMIT
Influencer Discoverytigerless-labs/influencer-discovery212—~2.5kAutomated safety check: NotesNone
Comment MiningScrapeCreators/social-media-research-skills3.4k—~1kAutomated safety check: NotesMIT
Competitor Social ResearchScrapeCreators/social-media-research-skills3.4k—~1.1kAutomated safety check: NotesMIT
Business Contact and Social Links Finderbrowser-act/skills6.1k1 repos~1.6kAutomated safety check: PassMIT

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Works with

Categories

Questions about Video Analytics Interpreter

What does Video Analytics Interpreter do?

Interpret YouTube Analytics, TikTok Analytics, and video performance data. Video Analytics Interpreter is an agent skill from nicepkg/ai-workflow. Interpret YouTube Analytics, TikTok Analytics, and video performance data.

When should I use Video Analytics Interpreter?

Video Analytics Interpreter fits situations like: analyzing video performance; understanding metrics; optimizing channel strategy.

How do I install Video Analytics Interpreter in Claude Code?

Run `npx skills add nicepkg/ai-workflow --skill video-analytics-interpreter -a claude-code`. Or copy the skill folder (workflows/video-creator-workflow/.claude/skills/video-analytics-interpreter in nicepkg/ai-workflow) into .claude/skills/video-analytics-interpreter in your project. Claude Code loads it when a task matches its description.

How do I install Video Analytics Interpreter in Codex?

Run `npx skills add nicepkg/ai-workflow --skill video-analytics-interpreter -a codex`. Or copy the skill folder (workflows/video-creator-workflow/.claude/skills/video-analytics-interpreter in nicepkg/ai-workflow) into .agents/skills/video-analytics-interpreter in your project. Codex loads it when a task matches its description.

Can I use Video Analytics Interpreter in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add nicepkg/ai-workflow --skill video-analytics-interpreter -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/video-analytics-interpreter, .gemini/skills/video-analytics-interpreter, .github/skills/video-analytics-interpreter and .opencode/skills/video-analytics-interpreter in your project.

What does Video Analytics Interpreter need to run?

SKILL.md names no scripts, command-line tools or credentials: Video Analytics Interpreter is instructions for the agent only.

Does Video Analytics Interpreter access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Video Analytics Interpreter safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Video Analytics Interpreter use?

Video Analytics Interpreter is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Video Analytics Interpreter use?

About 2.3k tokens (SKILL.md is roughly 9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Video Analytics Interpreter?

Skills that share tags, products or a category with Video Analytics Interpreter: Paid Ads Audit (AgriciDaniel/claude-ads, 9.9k stars), Influencer Discovery (tigerless-labs/influencer-discovery, 212 stars), Comment Mining (ScrapeCreators/social-media-research-skills, 3.4k stars) and Competitor Social Research (ScrapeCreators/social-media-research-skills, 3.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Video Analytics Interpreter?

nicepkg (a GitHub organization) maintains it in nicepkg/ai-workflow, which has 285 GitHub stars. The repository holds 75 skills in this directory. The repository was last updated on January 20, 2026.

Source: nicepkg/ai-workflow on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.