Yt Replier

YouTube Comment Replier

Two-stage pipeline for managing YouTube comments on your channel - mirrors /tiktok-replier, but YouTube has an official API so there is no Playwright. Both reading comments and posting replies go through the YouTube Data API v3.

Fully self-contained: own OAuth token (token.json), own auth module (auth_yt.py), own data/ dir. No dependency on other skills.

Files

• auth_yt.py — OAuth (youtube.force-ssl scope). Reuses ~/credentials.json client, caches token in token.json next to it. Run python3 auth_yt.py to authorize, --reauth to redo.
• monitor_yt.py — fetches recent uploads, finds unreplied top-level comments, splits new ones into data/inbox_yt.json (manual) + data/drafts_queue_yt.json (auto keyword CTAs). Cron-friendly.
• queue_draft_yt.py — moves a drafted reply from inbox_yt.json → drafts_queue_yt.json.
• reply_yt.py — posts each reply in a queue via comments().insert, tracks done in data/posted_yt.json. Dry-run by default; --post to publish.
• data/inbox_yt.json — comments needing manual reply (full metadata: cid, author, text, video_id, video_url, etc).
• data/drafts_queue_yt.json — replies ready to post.
• data/posted_yt.json — comment IDs already replied to (prevents double-posting).
• data/last_seen_yt.json — dedupe state for the monitor.
• data/drafts_yt.md — human-readable running log.

The hourly cron + manual draft workflow (PRIMARY pattern)

A cron runs monitor_yt.py every hour at :07. It produces three artifacts in data/:

• inbox_yt.json — comments needing manual reply. This is what you ask Claude to help draft.
• drafts_queue_yt.json — auto-drafted Skool-link replies for short keyword CTAs (System / Plan / Skill / Email / Routine / Tools / VFX / schedule / video / workflow). Already ready to post.
• drafts_yt.md — human-readable running log.

When the user says "any new youtube comments?" / "check my yt inbox"

1. Read data/inbox_yt.json (manual-needed) AND data/drafts_queue_yt.json (auto-drafts pending).
2. Show the user the breakdown — count of each plus the actual text of inbox items.
3. He picks one or all to draft replies for.

When the user asks "draft a reply for the @username one"

1. Read the inbox entry for that comment.
2. Compose a reply in your tone (casual, helpful, drives to skool.com/the-ai-agency when relevant). Use /harut for conversion-sensitive wording. No em dashes.
3. Show the user the draft, get approval.
4. When approved, run:

   python3 ~/.claude/skills/yt-replier/queue_draft_yt.py --cid <cid> --reply "<text>"
   

   This appends to drafts_queue_yt.json AND removes from inbox_yt.json.

When the user says "post them"

python3 ~/.claude/skills/yt-replier/reply_yt.py --post

--post is required to actually publish. Without it the script does a dry-run (prints what it would post). 10s spacing between posts.

Standard workflow (manual / first-time)

0. Authorize (one time)

python3 ~/.claude/skills/yt-replier/auth_yt.py

A browser opens, you approve the YouTube scope, token saves to token.json. (The token was migrated from the /yt-upload skill on creation, so this is usually already done.)

1. Fetch unreplied comments

python3 ~/.claude/skills/yt-replier/monitor_yt.py

Scans the 30 most recent uploads, up to 50 top-level comments each. A comment is "unreplied" when it's top-level, not authored by your channel, and you haven't replied in that thread. New ones get split into inbox (manual) vs auto-queue (keyword CTA).

2. Build reply queue

Show the user the unreplied list, confirm wording, then either:

• auto-drafts are already in drafts_queue_yt.json, or
• draft a manual reply and move it with queue_draft_yt.py (see above).

3. Post the replies

Always dry-run first, then test on 1, then batch:

python3 reply_yt.py                      # dry-run (shows everything, posts nothing)
python3 reply_yt.py --post --limit 1     # smoke test - post a single reply
python3 reply_yt.py --post               # batch (10s pause between each)

To post the auto-drafted keyword CTAs from a specific queue:

python3 reply_yt.py --post --queue ~/.claude/skills/yt-replier/data/drafts_queue_yt.json

Editing the auto-reply behavior

monitor_yt.py holds the auto-draft logic near the top:

• SKOOL_LINK_REPLY — the canned reply text for keyword CTAs.
• KEYWORD_REPLIES — the keyword → reply map (System / Plan / Skill / Email / Routine / Tools / VFX / schedule / video / workflow).
• draft_reply_for() — comments longer than 60 chars always go to the manual inbox; short ones matching a keyword get auto-drafted.

Safety rules

• Default to dry-run, then --limit 1 on first run of any new queue.
• Always show drafts to you before bulk posting (matches feedback_confirm_before_scheduling.md — confirm before posting even if approved earlier in the session).
• posted_yt.json tracks done IDs so re-runs after a crash skip what already worked.
• No em dashes in any reply (matches feedback_no_em_dashes.md).
• No automatic re-fetch + re-post loops — you initiate each batch.

Quota

YouTube Data API v3: 10,000 units/day. Per monitor run is ~30-60 units; each posted reply (comments.insert) is 50 units. Plenty of headroom for normal use.

Cron

7 * * * * /Library/Frameworks/Python.framework/Versions/3.12/bin/python3 \
  /Users/tylerreed/.claude/skills/yt-replier/monitor_yt.py \
  >> /Users/tylerreed/.claude/skills/yt-replier/data/monitor_yt.log 2>&1

This runs at :07 each hour (TikTok monitor runs at :05). The monitor posts a macOS notification with the new-comment count. It never posts replies on its own — posting is always a manual reply_yt.py --post.