Channel Analytics

---
name: channel-analytics
description: Audit and diagnose your own channel with real data — performance, retention, traffic, audience, and comparison against earlier snapshots. Triggered by "how's the channel doing?", "audit my videos", "why did it drop", "what's working". In Spanish, "¿cómo va el canal?", "audita mis vídeos", "por qué ha bajado", "qué está funcionando".
---

# Channel audit

## WHEN

Performance diagnosis, periodic report, "why did this video flop?", "what is
working and what is not?", comparing periods.

## EXECUTION ORDER

1. **Baseline and history**
   `python3 tools/yt_report.py --days 28`
   Gives the overall state, top videos, traffic, audience and geography, and
   appends a snapshot. If earlier snapshots exist it returns
   `change_since_last_snapshot`: **that delta is the real diagnosis**, not the
   still photo.

2. **Rank by retention, not only by views**
   `python3 tools/yt_top_videos.py --days 90 --by-retention`
   A video with few views and high retention is a format signal; one with many
   views and low retention is a promise problem.

3. **Where people come from**
   `python3 tools/yt_traffic.py --days 28`
   `python3 tools/yt_search_terms.py --days 90`
   The second is the one that pays: those are the **real** terms people arrive
   through. Subscriber dependence above 50% means the channel is not reaching
   beyond its own base.

4. **Where they leave** — on the 3 most relevant videos from step 2
   `python3 tools/yt_retention.py --video ID`
   Look at `milestones`: `intro_30s`, `re_hook_min3`, `re_hook_min6`. A drop in
   the intro is a promise problem; a drop at a re-hook is a structure problem.
   If `memory/sop/scripting_sop.md` exists, contrast against it.

5. **Mandatory cross-check before concluding**
   `python3 tools/yt_outliers_channels.py --min-ratio 2.0`
   Without this you cannot tell a channel problem from the whole niche moving.

6. Compare against what was already known: `memory/MEMORY.md`, especially any
   `outcome_*` files, which hold the prediction made before the video against
   the real result.

**The order adapts to the data.** If step 1 reveals a sharp drop, go straight
to step 4 on the affected video. The data outranks the manual.

## SOURCES

Steps 1-4 `youtube_api` (aggregates and percentages `derived`). Step 5
`derived`. No heuristics: this skill estimates nothing.

## OUTPUT

1. Headline: what changed and since when, with the data's date.
2. Status table with the delta against the previous snapshot.
3. Diagnosis: 2-3 causes, each backed by a concrete figure.
4. Priority action: one only, the highest impact.

If CTR is missing to close the diagnosis, say so and point at
`tools/ingest_studio_csv.py`. See `LIMITS.md`.