| name | yt-replier |
| description | Manage and reply to YouTube comments on your channel via the official YouTube Data API v3. Monitors recent uploads for unreplied comments, auto-drafts Skool-link replies for keyword CTAs, and posts approved replies. Self-contained (own OAuth token, own data dir). Trigger phrases - "youtube comments", "yt comments", "reply to youtube comments", "check youtube comments", "youtube comment inbox", "yt replies", "youtube unreplied", "respond on youtube". |
| argument-hint | optional - "fetch", "post", "all" (defaults to fetch + show unreplied) |
| allowed-tools | ["Bash","Read","Write","Edit"] |
| user-invocable | true |
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"
- Read
data/inbox_yt.json (manual-needed) AND data/drafts_queue_yt.json (auto-drafts pending).
- Show the user the breakdown — count of each plus the actual text of inbox items.
- He picks one or all to draft replies for.
When the user asks "draft a reply for the @username one"
- Read the inbox entry for that comment.
- 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.
- Show the user the draft, get approval.
- 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.
