Social Autoposter
Automates finding, posting, and tracking social media comments across Reddit, X/Twitter, LinkedIn, and Moltbook.
Quick Start
| Command | What it does |
|---|
| Full posting run (find threads + post + log) |
| Update engagement stats via API |
/social-autoposter engage
| Scan and reply to responses on our posts |
| Full browser audit of all posts |
Accounts
- Reddit: u/Deep_Ad1959 (logged in via Google with matt@mediar.ai). Use old.reddit.com.
- X/Twitter: @m13v_
- LinkedIn: Matthew Diakonov
- Moltbook: matthew-autoposter (API key in )
Our Projects & Links
Prefer website links when one exists (drives signups). Use GitHub for open source tools without a website.
Database
- Path:
~/social-autoposter/social_posts.db
~/.claude/social_posts.db
- Prompt DB:
~/claude-prompt-db/prompts.db
Helper Scripts
Standalone Python scripts — no LLM needed.
bash
python3 ~/social-autoposter/scripts/find_threads.py --topic "macOS automation"
python3 ~/social-autoposter/scripts/scan_replies.py
python3 ~/social-autoposter/scripts/update_stats.py --quiet
Workflow: Post ()
1. Rate limit check
sql
SELECT COUNT(*) FROM posts WHERE posted_at >= datetime('now', '-24 hours')
Max 10 posts per 24 hours. Stop if at limit.
2. Find candidate threads
Option A — Script (preferred):
bash
python3 ~/social-autoposter/scripts/find_threads.py --include-moltbook
Option B — Browse manually:
Browse
and
on: r/ClaudeAI, r/ClaudeCode, r/AI_Agents, r/ExperiencedDevs, r/macapps, r/vipassana.
Also check Moltbook via API.
3. Pick the best thread
- Must have a genuine angle from Matthew's work: building desktop AI agents, running 5 Claude agents in parallel on Swift/Rust/Flutter, CLAUDE.md specs, Playwright MCP, token costs, rate limits, vipassana practice
- Not already posted in:
SELECT thread_url FROM posts
- Last 5 comments don't repeat:
SELECT our_content FROM posts ORDER BY id DESC LIMIT 5
- If nothing fits, stop
4. Read the thread + top comments
Check tone, length cues, thread age. Find best comment to reply to (50+ upvotes = more visibility).
5. Draft the comment
Follow Content Rules below. 2-3 sentences, first person, specific. No product links in top-level comments.
6. Post it
Reddit: old.reddit.com → reply box → type → submit → verify → capture permalink → close tab.
X/Twitter: tweet → reply box → type → Reply → verify → capture URL → close tab.
LinkedIn: post → comment box → type → Post → close tab.
Moltbook (API, no browser):
bash
source ~/social-autoposter/.env
curl -s -X POST -H "Authorization: Bearer $MOLTBOOK_API_KEY" -H "Content-Type: application/json" \
-d '{"title": "...", "content": "...", "type": "text", "submolt_name": "general"}' \
"https://www.moltbook.com/api/v1/posts"
On Moltbook: write as agent ("my human" not "I"). Max 1 post per 30 min.
7. Log + sync
sql
INSERT INTO posts (platform, thread_url, thread_author, thread_author_handle,
thread_title, thread_content, our_url, our_content, our_account,
source_summary, status, posted_at)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, 'active', datetime('now'));
Then sync:
bash ~/social-autoposter/syncfield.sh
Workflow: Stats ()
bash
python3 ~/social-autoposter/scripts/update_stats.py
Or the legacy bash version:
bash ~/social-autoposter/skill/stats.sh
Workflow: Engage (/social-autoposter engage
Phase A: Scan for replies (no browser)
bash
python3 ~/social-autoposter/scripts/scan_replies.py
Phase B: Respond to pending replies
Query pending:
SELECT * FROM replies WHERE status='pending' ORDER BY discovered_at LIMIT 10
Draft replies: 2-4 sentences, casual, expand the topic. Apply Tiered Reply Strategy. Max 5 per run.
Post via browser (Reddit) or API (Moltbook). Update:
UPDATE replies SET status='replied', our_reply_content=?, replied_at=datetime('now') WHERE id=?
Phase C: X/Twitter replies (browser required)
Navigate to
https://x.com/notifications/mentions
. Extract mentions replying to @m13v_. Respond to substantive ones (max 5). Log to
table.
Workflow: Audit ()
Visit each post URL via browser. Check status (active/deleted/removed/inactive). Update engagement metrics.
Content Rules
- Write like you're texting a coworker. Lowercase fine. Sentence fragments fine. Never start with "Makes sense" or "The nuance here is." If it sounds like a blog post, rewrite it.
- First person, specific. "I run 5 agents in parallel and my API bill is becoming a second rent payment" beats "Token costs scale linearly."
- Reply to top comments, not just OP.
- Only comment when you have a real angle from Matthew's work. Desktop AI agents, multi-agent workflows, Swift/macOS dev, Playwright MCP, vipassana. If the thread doesn't connect, skip it.
- No product links in top-level comments. Earn attention first.
- On Moltbook, write as an agent. "my human" not "I".
- Log everything.
Bad vs Good
BAD: "Makes sense — Claude already tries to
on its own but by then the tokens are already in context."
GOOD: "gonna try this — I run 5 agents in parallel and my API bill is becoming a second rent payment"
BAD: "What everyone here is describing is basically specification-driven development."
GOOD: "I spend more time writing CLAUDE.md specs than I ever spent writing code. the irony is I'm basically doing waterfall now and shipping faster than ever."
Tiered Reply Strategy
Tier 1 — Default (no link): Genuine engagement. Expand topic, ask follow-ups. Most replies.
Tier 2 — Natural mention: Conversation touches something we're building. Mention casually, link only if it adds value. Triggers: "what tool do you use", problem matches a project, 2+ replies deep.
Tier 3 — Direct ask: They ask for link/try/source. Give it immediately.
Database Schema
: id, platform, thread_url, thread_title, our_url, our_content, our_account, posted_at, status, upvotes, comments_count, views, source_summary
: id, post_id, platform, their_author, their_content, our_reply_content, status (pending|replied|skipped|error), depth