Youtube Peer Benchmark

Pack Availability Guard

Before telling the user to run a skill from another project-local pack, check .agents/project.json.enabled_packs. If the target pack is not enabled, recommend /pack install <pack> instead of the target skill. Global skills are always valid. Skills from this same pack are valid because the current skill is already running from that pack.

YouTube Peer Benchmark

Invoke as /youtube-peer-benchmark.

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.

Staged Research Workflow

Use this staged workflow for synthesized research or report outputs that would create or update canonical research, spec, or task files.

1. Stage 1 - Research and clarify. Perform the research, run required source/code checks, and ask any needed clarification questions. Write only a non-canonical working packet: flat mode uses research/_working/preliminary-<skill>-research.md; product-path mode uses research/{slug}/_working/preliminary-<skill>-research.md. Replace <skill> with this skill's name value. Do not create or update canonical research, spec, or task files in Stage 1. Raw evidence or search logs may remain as supporting evidence where this skill already requires them, but synthesized deliverables stay in the working packet.
2. Stage 2 - Review alignment. Consume the working packet and build the review HTML alignment page. The page must render the full preliminary packet, evidence matrix, assumptions/confidence register, source coverage gaps, proposed canonical file changes, and approval gates. Stop for either feedback-only YAML or final compiled YAML. Feedback-only YAML revises the working packet and page, then remains in Stage 2.
3. Stage 3 - Finalize approved artifacts. Consume final compiled YAML only when it has no unresolved needs-clarification, unresolved down feedback, or other unresolved negative feedback. Apply approved edits first, archive the working packet to docs/history/archive/YYYY-MM-DD/HHMMSS/<original-working-path>, remove the active working packet, write the approved canonical artifacts to the unchanged output paths below, and convert the alignment page to confirmed with the approval record preserved.

Canonical output paths remain unchanged. Search logs and other supporting evidence remain allowed only where this skill's output contract already requires them.

Given a target YouTube channel, discover comparable creators in the same niche, pull real performance data from yt-dlp, and produce a structured competitive analysis. The output answers: how does this channel compare to peers, where is it ahead, where is it behind, and what specifically explains the gap?

Use this for creator channels, founder-led shows, and topic publications such as @GeorgeLe, WeeklyG, and WeeklySOTA.

Evidence And Feedback Handling

Treat user feedback as input to evaluate, not as automatic ground truth.

• For factual, evidentiary, technical, or source-backed claims: verify against available evidence. If the user appears to misunderstand the evidence or states something factually incorrect, push back clearly and cite the evidence. Do not rewrite findings merely to agree.
• For taste, brand, positioning preference, risk appetite, prioritization, or other subjective judgment calls: weigh user feedback heavily and adapt the recommendation unless it conflicts with verified evidence.
• When feedback mixes facts and preference, separate them explicitly: correct the factual part, then incorporate the preference where it is a legitimate judgment call.
• When uncertain, say what is known, what is inferred, and what would change the conclusion.

Prerequisites

• yt-dlp: Required. Install: brew install yt-dlp or pip install yt-dlp.
• Python 3: Required for data analysis. No special packages needed beyond stdlib.
• Browser cookies (optional): If yt-dlp returns incomplete data (NA values for views/likes), retry with --cookies-from-browser brave (or chrome/firefox/safari). Mention this to the user if needed.

Check command -v yt-dlp before proceeding. If missing, tell the user what to install and stop.

Process

1. Parse Arguments

• $ARGUMENTS must contain a YouTube channel URL or handle.
• Optional --niche 'keyword phrase' overrides automatic niche detection (e.g. --niche 'nextjs tutorial').
• Optional --peers @handle1,@handle2,... specifies known competitors to include (in addition to discovered ones).
• Optional --count N limits the target channel to the N most recent videos (default: all).
• Normalize handles: @handle → https://www.youtube.com/@handle/videos.

2. Gather Target Channel Baseline

If a current /youtube-channel-audit exists for the target channel, read raw metadata from research/youtube/data/<slug>/videos-*.jsonl. Otherwise, fetch directly.

Pull full metadata for the target channel:

yt-dlp --cookies-from-browser brave --skip-download \
  --print "%(id)s|%(title)s|%(upload_date)s|%(view_count)s|%(like_count)s|%(comment_count)s|%(duration)s|%(channel_follower_count)s" \
  "CHANNEL_URL/videos"

If --count N is specified, add --playlist-end N.

From this, compute the target channel profile:

Also compute:

• Performance by category: views, likes, comments, count, avg views per category
• Performance by time period (quarterly): videos published, total views, avg views per quarter

3. Discover Peer Channels

Automatic Discovery

Determine the target channel's niche from its top-performing video titles and tags. Use the 3-5 most distinctive keywords (not generic terms like "tutorial" or "how to").

Search YouTube for creators making similar content:

yt-dlp --cookies-from-browser brave --skip-download \
  --print "%(channel)s|%(channel_id)s|%(channel_follower_count)s|%(view_count)s|%(upload_date)s|%(title).80s" \
  "ytsearch20:KEYWORD1 KEYWORD2 KEYWORD3 2025"

Run 3-5 searches with different keyword combinations from the target's niche to build a broad candidate list.

Filter and Tier Candidates

From search results, extract unique channels. Discard:

• The target channel itself
• Channels with no videos in the last 6 months (inactive)
• Channels that only appeared once across all searches (likely tangential)

Sort remaining channels into tiers:

Include any channels specified via --peers in addition to discovered ones, placed in the appropriate tier.

Select up to 3 actual peers, 3 aspirational peers, and 2 category leaders for analysis. Prefer channels with the most keyword overlap with the target.

4. Fetch Peer Channel Data

For each selected peer channel, pull metadata:

yt-dlp --cookies-from-browser brave --skip-download \
  --print "%(id)s|%(title)s|%(upload_date)s|%(view_count)s|%(like_count)s|%(comment_count)s|%(duration)s|%(channel_follower_count)s" \
  "https://www.youtube.com/@HANDLE/videos"

For channels with 50+ videos, fetch the full catalog to enable stage-matched comparison. Use --playlist-end only if the channel has 200+ videos, in which case fetch the most recent 100 plus the earliest 50 (to capture both current performance and early-stage trajectory).

Compute the same baseline metrics as step 2 for each peer.

5. Stage-Matched Comparison

This is the most important analysis. Do not just compare current stats — compare channels at equivalent stages.

For each aspirational/leader peer, isolate their first N videos where N = the target channel's total video count. This answers: "when this peer had the same number of videos, how were they doing?"

Compute for the stage-matched slice:

6. Diagnose the Gap

For each peer comparison, categorize the gap across five dimensions:

Volume Gap

• How many more videos has the peer published in the same calendar time?
• What is the peer's publishing cadence vs. the target's?
• How many publishing gaps >14 days does each have?

Consistency Gap

• Does the peer have fewer format categories (more focused)?
• Does the peer have a higher floor (fewer low-performing videos)?
• Does the peer maintain cadence without multi-week breaks?

Quality Gap (or lack thereof)

• Compare ceiling: target's best video vs. peer's best at same stage
• Compare like rates on comparable videos
• Compare comment engagement

Conversion Gap

• Views-to-subscriber ratio: how efficiently does each channel convert viewers to subscribers?
• If the target has worse conversion, hypothesize why (format scatter, unclear channel promise, etc.)

Format Gap

• What content types does the peer make vs. the target?
• Which formats drive the most views for each?
• Does the target make content types that consistently underperform?

7. Write Report

Save to research/youtube/peer-benchmark-<primary-slug>-YYYY-MM-DD.md:

Create the research/youtube/ directory if it does not exist.

# YouTube Peer Benchmark — [Channel Name]

> Channel: [URL]
> Subscribers: N
> Total videos: N
> Date: YYYY-MM-DD
> Niche: [detected or specified niche keywords]

## Target Channel Baseline

### Overall Stats
- Subscribers: N
- Total videos: N
- Total views: N
- Channel age: N months
- Publishing cadence: N videos/month
- Avg views/video: N
- Median views: N
- Top video: N views — [title]
- Floor: N videos under 100 views

### Performance by Category

| Category | Videos | Total Views | Avg Views | Like Rate | Notes |
|----------|-------:|------------|----------:|----------:|-------|
| ... | | | | | |

### Performance by Quarter

| Quarter | Videos | Total Views | Avg Views |
|---------|-------:|------------|----------:|
| ... | | | |

### Publishing Gaps (>14 days)

| Gap | From | To |
|----:|------|------|
| ... | | |

## Peer Channels

### Tier: Actual Peers (similar size)

| Channel | Subs | Videos | Since | Cadence | View Range |
|---------|-----:|-------:|-------|---------|------------|
| ... | | | | | |

### Tier: Aspirational Peers (5-25x)

| Channel | Subs | Videos | Since | Cadence | View Range |
|---------|-----:|-------:|-------|---------|------------|
| ... | | | | | |

### Tier: Category Leaders (50x+)

| Channel | Subs | Videos | Since | Cadence | View Range |
|---------|-----:|-------:|-------|---------|------------|
| ... | | | | | |

## Stage-Matched Comparisons

### [Peer Name] at [N] videos vs. [Target Name] at [N] videos

| Metric | [Target] | [Peer] (first N) |
|--------|----------|-------------------|
| Total views | | |
| Avg views/video | | |
| Median views | | |
| Best single video | | |
| Videos under 100 views | | |
| Videos over 1K views | | |
| Time to publish N videos | | |
| Avg days between videos | | |

### ...

## Gap Diagnosis

### Volume
[Is the target underproducing relative to peers? By how much? What does peer cadence look like?]

### Consistency
[Does the target have more format scatter, more gaps, a lower floor?]

### Quality
[Is the target's ceiling competitive? Are like rates comparable?]

### Conversion
[Views-to-sub efficiency compared to peers. Why the difference?]

### Format
[What content types work for peers that the target isn't making? What is the target making that peers avoid?]

## Verdict

### Where [Channel] is ahead
1. [Specific advantage with evidence]
2. ...

### Where [Channel] is behind
1. [Specific gap with evidence]
2. ...

### What explains the gap
[2-3 sentences: is this a quality problem, a volume problem, a format problem, or a conversion problem? Be specific.]

### The single most impactful change
[One concrete action, grounded in the peer data, that would close the biggest gap]

8. Summarize In Thread

After saving the report, output to the user:

• Subscriber count vs. peer range
• The stage-matched headline (e.g. "Your best video outperforms OrcDev's at the same stage, but you publish 4x less often")
• Top 2 advantages
• Top 2 gaps
• The single most impactful change
• Path to the full report

Constraints

• Real data only: Every number must come from yt-dlp output. Never estimate, interpolate, or fabricate metrics.
• Stage-matched comparisons are mandatory: Never compare a 34-video channel's total stats against a 245-video channel's total stats as if that's meaningful. Always isolate the peer's equivalent stage.
• Tier honestly: Don't call a 200K-subscriber channel a "peer" to a 1K-subscriber channel. Tier them correctly and label the comparison as aspirational or ceiling benchmark.
• Diagnose, don't prescribe content: The output is a competitive diagnosis, not a content calendar. Say "you publish 4x less often" not "you should make 8 videos per month."
• Do not compare channels without comparable evidence windows: Mark subscriber counts or external metrics as unavailable unless present in fetched metadata.
• Do not recommend copying peers: Translate benchmark evidence into differentiated positioning.
• Browser cookies: Try without cookies first. If yt-dlp returns NA for view counts or subscriber counts, retry with --cookies-from-browser brave (or whichever browser the user has). Tell the user which browser you're using.
• Rate limiting: If YouTube returns 429 errors, wait 30 seconds and retry once. If it persists, work with whatever data was successfully fetched and note the gaps.
• No API keys required: yt-dlp works against public data. Do not ask for YouTube Data API keys.
• Flat playlist for discovery, full metadata for analysis: Use --flat-playlist when you only need video IDs/titles for discovery. Use full --skip-download metadata fetch when you need view counts, likes, dates.

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-Skill Routing sequence, 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.

Next-Step Routing

After writing the artifact, recommend the next contextual creator-media skill in the final response as Recommended next skill: <command>.

Default recommendation: /youtube-search-positioning.

If the default successor already exists and is current, recommend the first missing or stale downstream creator-media artifact in this order:

/youtube-channel-audit -> /youtube-video-audit -> /youtube-vid-research -> /youtube-concept-research -> /youtube-competitive-research -> /youtube-title-thumbnail-audit -> /youtube-description-optimizer -> /youtube-portfolio -> /youtube-peer-benchmark -> /youtube-search-positioning -> /youtube-cadence-diagnosis -> /creator-positioning -> /content-programming -> /series-spec -> /product-led-media-map -> /creator-metrics-review

If the sequence is ambiguous, multiple upstream artifacts are stale, or the recommendation depends on channel-level strategy vs programming-level changes, recommend /creator-metrics-review when metrics evidence exists, otherwise recommend the default successor and explain the missing artifact.

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-peer-benchmark-{topic}.html.

Default Shipping Contract

Follow the shared shipping contract convention in CLAUDE.md.