youtube-audit

YouTube Audit — Evidence-First Channel Analysis

Invoke as $youtube-audit.

Report-First Approval Gate

Default to report-only: present findings, evidence coverage, assumptions, recommended artifact path, and proposed file changes in a pre-approval alignment page plus a concise conversation summary for user approval before creating or updating canonical research, spec, or task files.

Do not write or overwrite synthesized deliverables until the user explicitly approves, unless the user invoked an explicit write/update/fix mode or clearly asked to write files upfront. Raw evidence capture may be persisted before analysis when reproducibility requires it; report those raw paths separately and still gate synthesized research/report writes.

When stopping for approval, build and attempt to open the alignment preview page first, then ask the user to review it and approve, question, or request adjustments. Do not include Recommended next skill, Recommended next command, or downstream routing language. The approval request itself is the next action. Only emit next-skill routing after the approved artifact has been written or updated.

Fetch full video metadata and transcripts from a YouTube channel, persist raw evidence, then analyze both performance shape and content quality. The output identifies repeated strengths, repeated weaknesses, portfolio concentration, content roles, archetypes, and cleanup candidates with evidence cited from specific videos.

Prerequisites

Two CLI tools are required. Neither needs API keys.

• yt-dlp: Lists channel videos as JSON. Install: brew install yt-dlp or pip install yt-dlp.
• youtube-transcript-api: Fetches transcripts via Python. If the active Python is externally managed, such as Homebrew Python on macOS under PEP 668, install this package in a project-local virtual environment instead of the system Python.

Check yt-dlp before proceeding. The transcript dependency should be handled through a project-local .venv when it is missing.

Workflow

1. Parse Arguments

• $ARGUMENTS must contain a YouTube channel URL or handle (e.g. @handle).
• Optional --count N sets how many recent videos to fetch (default 20, max 50).
• If no channel is provided, ask the user for one and stop.
• Normalize handles to channel video URLs when needed: @handle -> https://www.youtube.com/@handle/videos.
• Validate that yt-dlp is available first:

command -v yt-dlp

• Select the Python interpreter for transcript fetching. Prefer a workspace-local virtual environment when present; if one is not present, create it before checking the transcript import:

if [ ! -x .venv/bin/python ]; then
  python3 -m venv .venv
fi
TRANSCRIPT_PYTHON=.venv/bin/python
"$TRANSCRIPT_PYTHON" -c "from youtube_transcript_api import YouTubeTranscriptApi; print('ok')"

• If the import check fails, install the missing package into the project-local virtual environment, then rerun the import check:

.venv/bin/python -m pip install youtube-transcript-api
"$TRANSCRIPT_PYTHON" -c "from youtube_transcript_api import YouTubeTranscriptApi; print('ok')"

• Store the selected interpreter path and use it for every transcript-fetching command in this audit.
• If package installation fails because network access is unavailable or denied, stop and report the exact safe install commands below:

python3 -m venv .venv
.venv/bin/python -m pip install youtube-transcript-api

Do not recommend installing youtube-transcript-api into Homebrew/system Python with python3 -m pip install ..., and do not recommend --break-system-packages. The local-venv path avoids PEP 668 externally-managed-environment failures on Homebrew Python.

2. Archive Previous Audit

If research/youtube/youtube-audit-*.md or legacy research/youtube-audit-*.md already exists for the same channel (check the > Channel: header in existing files):

1. Copy the existing file to docs/history/archive/YYYY-MM-DD/HHMMSS/research/youtube-audit-*.md.
2. Preserve the archived snapshot exactly.
3. Then proceed to overwrite the canonical path.

New files do not need archive snapshots.

3. Fetch Video List

Create:

research/youtube/data/<slug>/

Run full metadata export, not flat playlist metadata:

yt-dlp --dump-json --playlist-end N "CHANNEL_URL/videos"

Persist the raw newline-delimited response exactly to:

research/youtube/data/<slug>/videos-YYYY-MM-DD.jsonl

Parse each JSON object for at least:

Field	Notes
id, webpage_url, title, description	Identity and audit links
channel, channel_id, uploader, uploader_id	Channel identity
upload_date, timestamp	Used for recency and views/day
duration	Used for views/minute
view_count, like_count, comment_count	Performance
tags, categories, chapters	Positioning and packaging signals
thumbnail, thumbnails	Packaging review signal

Build a video table sorted by upload date (newest first). Report total videos found.

4. Fetch Transcripts

Use the same Python interpreter that passed the import check. If .venv/bin/python exists and passed the import check, run transcript fetching with .venv/bin/python; otherwise use the Python executable that passed the import check. Fetch transcripts sequentially and persist raw transcript JSON:

from youtube_transcript_api import YouTubeTranscriptApi
from pathlib import Path
import json, sys, time

video_ids = json.loads(sys.argv[1])
out_dir = Path(sys.argv[2])
out_dir.mkdir(parents=True, exist_ok=True)
results = {}
errors = []

for vid in video_ids:
    try:
        transcript = YouTubeTranscriptApi.get_transcript(vid)
        (out_dir / f"{vid}.json").write_text(json.dumps(transcript, ensure_ascii=False, indent=2))
        results[vid] = " ".join(entry["text"] for entry in transcript)
    except Exception as e:
        errors.append({"id": vid, "reason": str(e)})
    time.sleep(1.5)

summary = {"transcripts": results, "errors": errors}
(out_dir / "transcripts-summary.json").write_text(json.dumps(summary, ensure_ascii=False, indent=2))
json.dump(summary, sys.stdout)

• Fetch sequentially with 1.5s delay to avoid rate limiting.
• Skip videos without transcripts and log the reason.
• If more than 80% lack transcripts, warn the user and ask whether to proceed.
• Do not fabricate transcript text; transcript gaps are evidence quality limits.

5. Prepare Analysis Context

Build structured records per video:

Field	Computation
Title, date, URL, duration, views, likes, comments	Direct from metadata
Age in days	From upload date to current date
Views/day	view_count / max(age_days, 1)
Views/minute	view_count / max(duration_minutes, 1)
Like rate	like_count / view_count when both exist
Transcript coverage	yes, no, or partial
Archetype	Format pattern such as founder update, interview, tutorial, analysis, vlog, launch, reaction, teardown
Content role	Strategic role such as acquisition, trust-building, proof, education, launch support, community retention, cleanup candidate

If total transcript text exceeds 80,000 words, truncate each transcript to preserve:

• First 500 words (hook and intro)
• Last 300 words (outro and CTA)
• Trim the middle proportionally across all videos to fit the budget

6. Analyze Content

Analyze performance and content in one pass.

Performance And Portfolio Fields

Include:

• Total videos analyzed, transcript coverage, total views, median views, top video, bottom video.
• Views/day and views/minute leaders.
• Top-video concentration: top 1, top 3, and top 5 share of analyzed views.
• Portfolio distribution by archetype and content role.
• Packaging signals from titles, thumbnails, tags, descriptions, and chapters.
• Cleanup candidates: videos with poor fit, weak performance relative to channel median, stale positioning, or inconsistent audience promise. Mark as keep, refresh, unlist/private candidate, or needs human review; do not recommend deletion as an automated action.

Content Quality Patterns

Split into positive patterns (double down) and critical patterns (fix).

Every finding must cite evidence from 2+ videos to qualify as a pattern. Single-video observations are not patterns — discard them.

Analyze across these dimensions:

Dimension	What to look for
Positioning	Who is this channel for? Is it consistent? Does the creator own a niche?
Hooks	First 30 seconds — do they create curiosity, state a problem, or waste time?
Interview style	If applicable — question quality, follow-ups, guest chemistry, interruptions
Credibility	Does the creator demonstrate expertise? Name-drop experiences? Show receipts?
Topic selection	Are topics timely, evergreen, niche, or scattered? Do they match the audience?
Pacing	Does the content drag or rush? Are there dead zones?
Editing signals	Verbal cues for cuts, chapters, graphics. Does the script feel edited or rambling?
Startup rigor	If startup/business content — are frameworks used? Is advice actionable or generic?
Audience trust	Does the creator build trust or erode it? Sponsorship handling, honesty, consistency
Portfolio fit	Does each video serve a clear role in the channel strategy?
Packaging	Do title, thumbnail, topic, and opening promise align?

7. Write Report

Save to research/youtube/youtube-audit-<slug>-YYYY-MM-DD.md:

# YouTube Audit — [Channel Name]

> Channel: [URL]
> Videos analyzed: N
> Transcripts available: N of M
> Date: YYYY-MM-DD
> Raw metadata: research/youtube/data/<slug>/videos-YYYY-MM-DD.jsonl
> Raw transcripts: research/youtube/data/<slug>/transcripts/

## Videos Analyzed

| # | Title | Date | Views | Likes | Views/day | Views/min | Duration | Archetype | Role | Transcript |
|---|-------|------|-------|-------|-----------|-----------|----------|-----------|------|------------|
| 1 | [Title](URL) | YYYY-MM-DD | N | N | N | N | Xm | [type] | [role] | Yes/No |
| ... | | | | | |

## Performance Snapshot

- **Total views analyzed**: N
- **Median views**: N
- **Top 1 concentration**: N%
- **Top 3 concentration**: N%
- **Views/day leader**: [video]
- **Views/minute leader**: [video]

## Portfolio Shape

| Archetype | Videos | View share | Notes |
|---|---:|---:|---|
| ... | | | |

| Content role | Videos | View share | Notes |
|---|---:|---:|---|
| ... | | | |

## Positive Patterns (Double Down)

### [Pattern Title]
- **Frequency**: Found in N of M videos
- **Evidence**: [Video 1 title] — "[quote or description]"; [Video 2 title] — "[quote or description]"
- **Recommendation**: [What to keep doing or amplify]

### ...

## Critical Patterns (Fix)

### [Pattern Title]
- **Severity**: High / Medium / Low
- **Frequency**: Found in N of M videos
- **Evidence**: [Video 1 title] — "[quote or description]"; [Video 2 title] — "[quote or description]"
- **Impact**: [Why this hurts the channel]
- **Recommendation**: [Specific fix]

### ...

## Cleanup Candidates

| Video | Recommendation | Evidence | Human check |
|---|---|---|---|
| [Title](URL) | Keep / Refresh / Unlist-private candidate / Needs human review | [performance + fit evidence] | [what to inspect before action] |

## Summary

### Top 3 Strengths
1. [Strength] — [one-line why]
2. [Strength] — [one-line why]
3. [Strength] — [one-line why]

### Top 3 Weaknesses
1. [Weakness] — [one-line why]
2. [Weakness] — [one-line why]
3. [Weakness] — [one-line why]

### Strategic Recommendation
[2-3 sentences: the single most impactful change the channel should make, grounded in the patterns found]

Create the research/youtube/ and research/youtube/data/<slug>/ directories if they do not exist.

8. Summarize In Thread

After saving the report, output: top 3 strengths, top 3 weaknesses, performance/portfolio headline, strategic recommendation, report path, and raw data paths.

Constraints

• Pattern-only reporting: Every finding must have evidence from 2+ videos. No single-video observations.
• Sequential fetching: Do not parallelize transcript fetches — use 1.5s delay to avoid IP rate limiting.
• No API keys: Both tools work unauthenticated against public data. Do not ask the user for API keys.
• Smart truncation: When truncating, always preserve hooks (first 500 words) and outros (last 300 words).
• Honest coverage: If transcripts are sparse, say so. Do not fabricate transcript content or hallucinate quotes.
• Raw evidence first: Always persist raw yt-dlp JSONL and transcript JSON before writing analysis.
• No automated destructive content advice: Cleanup candidates are review recommendations, not instructions to delete content.
• Stay in analysis mode: Do not propose video ideas, scripts, or content calendars. The job is to audit what exists.

Approved Artifact Handoff

After an approved synthesized write, explicit write/update mode, or any direct artifact mutation:

• List every created or updated synthesized artifact path in the final response.
• State the verification performed, such as readback, schema/check command, or why no executable verification applies for a Markdown-only strategy artifact.
• Check and report the relevant git status for intended artifacts when the project is a git repository. If intended artifacts are modified or untracked, make the next action shipping, committing, or an explicit dirty-artifact handoff before recommending downstream strategy work.
• Do not imply the research workflow is complete while approved artifacts remain untracked or uncommitted unless the user explicitly asked not to ship.
• If stopping for approval before writing, the approval request remains the next action; do not include downstream routing.

Intent-Aware Routing

Before applying the default next-step routing, classify the user's immediate intent and route to the missing action that best serves that intent:

• Strategy refresh: recommend the missing or stale positioning, programming, portfolio, metrics, or product-media artifact.
• Recording prep: recommend the missing series spec, script, build proof, walkthrough guide, or validation artifact needed before recording.
• Upload prep: recommend packaging, title/thumbnail, description, chapters, or final metadata work before broader strategy work.
• Performance review: recommend metrics, cadence, portfolio, peer benchmark, or owner-analytics export work before new content planning.
• Owner analytics or private/manual platform evidence: route to an explicit manual/guide handoff instead of inventing unavailable metrics.
• Dirty intended artifacts: route to shipping/commit/handoff first, not another creator strategy skill.

Use the default next-skill sequence only when no stronger user intent, missing artifact, manual blocker, or dirty-artifact handoff applies.

Alignment Page

When this skill produces durable deliverables (research, specs, plans, reports, prototypes, or any document output), build a full-depth HTML alignment page following ALIGNMENT-PAGE.md in this skill's directory. Output: alignment/youtube-audit-{topic}.html.

Default Shipping Contract

Follow the shared shipping contract convention in CLAUDE.md.