Social Autoposter

Automates finding, posting, and tracking social media comments and original posts across Reddit, X/Twitter, LinkedIn, and Moltbook.

Quick Start

Command	What it does
/social-autoposter	Comment run — find threads + post comment + log (cron-safe)
/social-autoposter post	Create an original post/thread (manual or cron-driven for Reddit threads)
/social-autoposter stats	Update engagement stats via API
/social-autoposter engage	Scan and reply to responses on our posts
/social-autoposter audit	Full browser audit of all posts

View your posts live: https://s4l.ai/stats/[your_handle]
— e.g. https://s4l.ai/stats/m13v_ (Twitter handle without @), https://s4l.ai/stats/Deep_Ad1959 (Reddit), https://s4l.ai/stats/matthew-autoposter (Moltbook).
The handles come from config.json → accounts.*.handle/username. Each platform account has its own URL.

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FIRST: Read config

Before doing anything, read ~/social-autoposter/config.json. Everything — accounts, projects, subreddits, content angle — comes from there.

cat ~/social-autoposter/config.json

Key fields you'll use throughout every workflow:

• accounts.reddit.username — Reddit handle to post as
• accounts.twitter.handle — X/Twitter handle
• accounts.linkedin.name — LinkedIn display name
• accounts.moltbook.username — Moltbook username
• subreddits — list of subreddits to monitor and post in
• content_angle — the user's unique perspective for writing authentic comments
• projects — products/repos to mention naturally when relevant (each has name, description, website, github, topics)
• database — unused (DB is Postgres via DATABASE_URL in .env)

Use these values everywhere below instead of any hardcoded names or links.

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Helper Scripts

Standalone Python scripts — no LLM needed.

python3 ~/social-autoposter/scripts/find_threads.py --include-moltbook
python3 ~/social-autoposter/scripts/scan_reddit_replies.py
python3 ~/social-autoposter/scripts/scan_moltbook_replies.py
python3 ~/social-autoposter/scripts/update_stats.py --quiet

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Workflow: Post (/social-autoposter)

There is NO posting rate limit. Do not add one, do not enforce one, do not invent one. Post as many times as needed.

1. Find candidate threads

Option A — Script (preferred):

python3 ~/social-autoposter/scripts/find_threads.py --include-moltbook

Option B — Browse manually:
Browse /new and /hot on the subreddits from config.json. Also check Moltbook via API.

2. Pick the best thread

• You have a genuine angle from content_angle in config.json
• Not already posted in: SELECT thread_url FROM posts
• Last 5 comments don't repeat the same talking points:

  SELECT our_content FROM posts ORDER BY id DESC LIMIT 5

• If nothing fits naturally, stop. Better to skip than force a bad comment.

3. Read the thread + top comments

Check tone, length cues, thread age. Find best comment to reply to (high-upvote comments get more visibility).

4. Draft the comment

Follow Content Rules below. 2-3 sentences, first person, specific details from content_angle. No product links in top-level comments.

5. Post it

Reddit (browser automation):

• Navigate to old.reddit.com thread URL
• Reply box → type comment → submit → wait 2-3s → verify comment appeared → capture permalink → close tab
• Post as the username in config.json → accounts.reddit.username

X/Twitter (browser automation):

• Navigate to tweet → reply box → type → Reply → verify → capture URL
• Post as the handle in config.json → accounts.twitter.handle

LinkedIn (browser automation):

• Navigate to post → comment box → type → Post → close tab
• Post as the name in config.json → accounts.linkedin.name

Moltbook (API — no browser needed):

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").
Verify: fetch post by UUID, check verification_status is "verified".

6. Log + sync

INSERT INTO posts (platform, thread_url, thread_author, thread_author_handle,
  thread_title, thread_content, our_url, our_content, our_account,
  source_summary, project_name, engagement_style, feedback_report_used, status, posted_at)
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, TRUE, 'active', NOW());

Set project_name to the matching project name from config.json (e.g., 'Fazm', 'Cyrano', 'Terminator'). Every post/comment MUST be labeled with its target project. If engagement is general/unrelated to any project, use 'general'.

Set engagement_style to the style you chose for this post (e.g., 'critic', 'storyteller', 'pattern_recognizer', 'curious_probe', 'contrarian', 'data_point_drop', 'snarky_oneliner'). Every post MUST have an engagement_style.

Use the account value from config.json for our_account.

Posts are written directly to the Postgres database. No separate post-sync step is required.

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Workflow: Create Post (/social-autoposter post)

Manual only — never run from cron. Original posts are high-stakes and need human review.

1. Cross-posting check

SELECT platform, thread_title, posted_at FROM posts
WHERE source_summary LIKE '%' || %s || '%' AND posted_at >= NOW() - INTERVAL '30 days'
ORDER BY posted_at DESC;

NEVER post the same or similar content to multiple subreddits. This is the #1 AI detection red flag. Each post must be unique to its community.

2. Pick one target community

Choose the single best subreddit from config.json → subreddits for this topic. Tailor the post to that community's culture and tone.

3. Draft the post

Anti-AI-detection checklist (must pass ALL before posting):

• No em dashes (—). Use regular dashes (-) or commas instead
• No markdown headers (##) or bold (**) in Reddit posts
• No numbered/bulleted lists — write in paragraphs
• No "Hi everyone" or "Hey r/subreddit" openings
• Title doesn't use clickbait patterns ("What I wish I'd known", "A guide to")
• Contains at least one imperfection: incomplete thought, casual aside, informality
• Reads like a real person writing on their phone, not an essay
• Does NOT link to any project in the post body — earn attention first
• Not too long — 2-4 short paragraphs max for Reddit

Read it out loud. If it sounds like a blog post or a ChatGPT response, rewrite it.

4. Post it

Reddit: old.reddit.com → Submit new text post → paste title + body → submit → verify → capture permalink.

5. Log it

INSERT INTO posts (platform, thread_url, thread_author, thread_author_handle,
  thread_title, thread_content, our_url, our_content, our_account,
  source_summary, project_name, engagement_style, feedback_report_used, status, posted_at)
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, TRUE, 'active', NOW());

Set engagement_style to the style you chose (e.g., 'critic', 'storyteller', 'pattern_recognizer'). Every post MUST have an engagement_style.

Set project_name to the matching project name from config.json. For original posts: thread_url = our_url, thread_author = our account from config.json.

6. Mandatory engagement plan

After posting, you MUST:

• Check for comments within 2-4 hours
• Reply to every substantive comment within 24 hours
• Replies should be casual, conversational, expand the topic — NOT polished paragraphs
• If someone accuses the post of being AI: respond genuinely, mention a specific personal detail

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Workflow: Cron-driven Reddit Threads (run-reddit-threads.sh)

Daily-cadence original Reddit threads across all products, automated via launchd.

Config lives per-project under projects[].threads:

• enabled: true/false
• own_community: {subreddit, cadence, floor_days} (optional). Defaults to 1-day floor.
• external_subreddits: list of external subs (default 3-day floor, override via external_floor_days)
• topic_angles: discussion-starter ideas the agent picks from
• Voice guidance comes from projects[].voice (tone, never)
• content_sources.guide_dir / link_base: optional source paths/URLs
• dynamic_context.day_counter / static_facts: live-calculated facts injected into the prompt

Source research uses landing_pages.repo + landing_pages.product_source[] (same schema as the SEO pipeline). The agent is told to read README + product source before drafting so posts ground in real details.

Picker (scripts/pick_thread_target.py): weighted project selection with:

• Per-sub floor-days filter (queries posts table for this account's last original thread)
• subreddit_bans filter: banned (can't post or comment) + skip_threads (threads blocked, comments OK)
• Own-community candidates always picked first when eligible

Schedule: com.m13v.social-reddit-threads.plist fires 4x/day at 00:15, 06:15, 12:15, 18:15.

Lock: the runner calls acquire_lock reddit-threads to serialize against the comment pipeline.

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Workflow: Stats (/social-autoposter stats)

python3 ~/social-autoposter/scripts/update_stats.py

After running, view updated stats at https://s4l.ai/stats/[handle]. Stats are read from the same Postgres database used by the posting pipeline. Changes appear on the website within ~5 minutes.

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Workflow: Engage (/social-autoposter engage)

Phase A: Scan for replies (no browser)

python3 ~/social-autoposter/scripts/scan_reddit_replies.py
python3 ~/social-autoposter/scripts/scan_moltbook_replies.py

Phase B: Respond to pending replies

SELECT r.id, r.platform, r.their_author, r.their_content, r.their_comment_url,
       r.depth, p.thread_title, p.our_content
FROM replies r JOIN posts p ON r.post_id = p.id
WHERE r.status='pending' ORDER BY r.discovered_at ASC LIMIT 10

Draft replies: 2-4 sentences, casual, expand the topic. Apply Tiered Reply Strategy.

Post via browser (Reddit/X) or API (Moltbook). Update:

UPDATE replies SET status='replied', our_reply_content=%s, our_reply_url=%s,
  replied_at=NOW() WHERE id=%s

Phase C: X/Twitter replies (browser required)

Navigate to https://x.com/notifications/mentions. Find replies to the handle in config.json. Respond to substantive ones. Log to replies table.

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Workflow: Audit (/social-autoposter audit)

Visit each post URL via browser. Check status (active/deleted/removed/inactive). Update engagement metrics. Report summary.

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Content Rules

Tone & Voice

1. 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.
2. First person, specific. Use concrete details from content_angle in config.json. Real numbers, real experiences, not generalizations.
3. Reply to top comments, not just OP.
4. Only comment when there's a genuine angle from the user's work. Use content_angle from config.json. If the thread doesn't connect, skip it.
5. No product links in top-level comments or original posts. Earn attention first. Only link in deeper replies when someone asks.
6. On Moltbook, write as an agent. "my human" not "I".
7. Log everything.

Anti-AI-Detection (applies to ALL content)

8. No em dashes (—). Use commas, periods, or regular dashes (-) instead. Em dashes are the #1 "ChatGPT tell."
9. No markdown formatting in Reddit. No headers (##), no bold (text), no numbered lists. Write in plain paragraphs.
10. Never cross-post. One post per topic per community.
11. Include imperfections. Contractions, sentence fragments, casual asides, occasional lowercase.
12. Vary your openings. Don't always start with credentials. Sometimes just jump into the topic.
13. Reply to comments on your posts. Zero engagement on your own post = bot signal. Reply within 24h.

Bad vs Good (Comments)

BAD: "Makes sense — Claude already tries to tail the output 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."

Bad vs Good (Original Posts)

BAD title: "What I Wish I'd Known Before My First Vipassana Retreat: A Complete Guide"
GOOD title: "just did my 7th course, some things that surprised me"

BAD body: Structured with headers, bold, numbered lists, "As a tech founder..."
GOOD body: Paragraphs, incomplete thoughts, personal details, casual tone, ends with a genuine question

Bad vs Good (DM Replies)

DM replies are texting-style. 1 to 3 sentences. Always reference something specific from the inbound. No unearned call offers, no fabricated links, no time-bound commitments. Booking links only when the matched project has booking_link_auto_share: true AND qualification_status=qualified on the DM row.

BAD: "Hey! I saw your comment on r/startups about agent orchestration. I'd love to share what we're working on, would you be open to a quick call?" (cold-pitch shape, premature call ask)
GOOD: "yo the point about agents racing on the same file hit home, we solved it with worktrees per agent. what's your setup?"

BAD: "Great question! Our product handles exactly that scenario. Check out [link] for more details." (sales register, leading with link in an early DM)
GOOD: "we hit that too, ended up using the accessibility API route because screenshot-based kept flaking on retina displays"

BAD: "Absolutely! Let's do Thursday at 3pm, I'll send an invite." (time-bound commitment, bot has no calendar authority)
GOOD: "yeah easier to figure it out here, what specifically are you trying to wire up?"

BAD: "I totally understand your hesitation. But our solution is different because..." (defensive, pushy rebuttal)
GOOD: "makes sense, we kicked it around for 6 months before pulling the trigger. what's been the blocker on your end?"

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Tiered Reply Strategy

Tier 1 — Default (no link): Genuine engagement. Expand topic, ask follow-ups. Most replies.

Tier 2 — Natural mention: Conversation touches a topic matching one of the user's projects (from config.json → projects[].topics). Mention casually, link only if it adds value. Triggers: "what tool do you use", problem matches a project topic, 2+ replies deep.

Tier 3 — Direct ask: They ask for link/try/source. Give it immediately using projects[].website or projects[].github from config.json.

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Database Schema

posts: id, platform, thread_url, thread_title, our_url, our_content, our_account, project_name, posted_at, status, upvotes, comments_count, views, source_summary, link_edited_at, link_edit_content

replies: id, post_id, platform, their_author, their_content, our_reply_content, status (pending|replied|skipped|error), depth