How do I install the Analytics Feedback skill?

Run npx skillmds@latest add jperrello/analytics-feedback in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Analytics Feedback safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Analytics Feedback?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Analytics Feedback free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Analytics Feedback?

jperrello (@jperrello) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Analytics Feedback by jperrello — Agent Skill

Name: Analytics Feedback
Author: jperrello

Analytics Feedback

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.

jperrello f469ff0 3 files · 15.0 KB Updated Sep 17, 2026

File contents

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.

---name: analytics-feedbackdescription: 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.---# analytics-feedbackTurns 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```bashanalytics-feedback.sh # newest export under ~/Downloads/Content*C0BALT_CUT*/analytics-feedback.sh "/path/Table data.csv" # explicit fileanalytics-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 writes1. **`.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.2. **`.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.3. **`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.

Frequently asked questions