Analytics feedback

Skill jperrello/C0BALT_CUT/.claude/skills/analytics-feedback

Investigation on using Claude Code to automatically generate profitable YouTube videos.

Install

npx -y skills add jperrello/C0BALT_CUT --skill analytics-feedback

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

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What its author says it does

Copied from the file, not written here

Close the learning loop from real YouTube performance. Reads the newest YouTube Studio "Table data.csv" export, attributes each video's views/CTR/retention to topic + named-entity tokens, and rewrites the AUTO-managed block of schedule-drip's topics.scorelist (GO winners / HOLD dead niches) plus appends proven-winner search queries to scout-sources' niches.txt. Deterministic, no Claude, idempotent (no-op unless the export's mtime changed). The analytics half of the autopilot loop — runs at the top of every autopilot tick so scout + schedule-drip always reflect what actually performed.

SKILL.md

3.4 KB, as published. Nobody here has run it

analytics-feedback

Turns channel analytics into pipeline behavior. YouTube Studio → Analytics → Content → Export → unzips to ~/Downloads/Content <range> <channel>/ with Table data.csv (per-video views, watch hours, CTR). This skill reads that export and re-derives which topics to chase and which to bury.

Usage

analytics-feedback.sh                       # newest export under ~/Downloads/Content*C0BALT_CUT*/
analytics-feedback.sh "/path/Table data.csv"   # explicit file
analytics-feedback.sh --force               # re-learn even if the export's mtime is unchanged

Idempotent: records the consumed export's mtime in work/_autopilot/analytics.csv.mtime and no-ops on an unchanged export (so it's safe to call every autopilot tick). Drop a fresh export into ~/Downloads and the next tick relearns.

What it writes

.claude/skills/schedule-drip/topics.scorelist — preserves everything ABOVE the # ==== AUTO (analytics-feedback) ... sentinel (your hand-curated rules are never touched) and regenerates the block below it with data-derived GO <pattern> / HOLD <pattern> lines. Evidence (n=, med=, ctr=) is on # comment lines — never inline on a rule line, because schedule.py treats everything after the verdict as the regex.
.claude/skills/scout-sources/niches.txt — preserves your manual seed queries and appends a regenerated AUTO block of search queries built from the GO winners (e.g. a winning black[ -]?hole → black hole physics explained), so scout expands into proven niches. Additive only.
work/_autopilot/topic_scores.json — full per-token evidence (n, median/mean/min/max views, mean CTR, median retention, total watch hours, verdict, suppressed-alias flag, manual conflicts). The audit trail.

Classification (env-tunable)

Per token (known entities from a lexicon + auto-discovered title tokens, deduped so #blackhole doesn't double-count black[ -]?hole):

GO when n ≥ AF_GO_MIN_N (3) AND median_views ≥ AF_GO_VIEWS (600) AND mean_ctr ≥ AF_GO_CTR (5%). Median + the n≥3 floor reject a single viral fluke (a 2-video token with one 6-view dud can't earn GO).
HOLD when n ≥ AF_HOLD_MIN_N (2) AND median_views ≤ AF_HOLD_VIEWS (60). Dead-is-dead needs less evidence than proven-winner.
neutral otherwise (no line emitted; schedule.py defaults unmatched sources to HOLD anyway).

A HOLD always wins over a GO in schedule.py, so the data can demote a manual GO (surfaced as a conflict in the JSON) but a manual HOLD veto still sticks.

Where it runs

autopilot tick — first step of autopilot.sh, before scout, so discovery + staging always reflect the latest export.
standalone — run it by hand after any fresh export to retune immediately.