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social-autoposter

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Social Autoposter

社交媒体自动发布工具（Social Autoposter）

Automates finding, posting, and tracking social media comments across Reddit, X/Twitter, LinkedIn, and Moltbook.
可在Reddit、X/Twitter、LinkedIn和Moltbook平台自动完成主题帖查找、评论发布及互动追踪操作。

Quick Start

快速开始

CommandWhat it does
/social-autoposter
Full posting run (find threads + post + log)
/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
命令功能说明
/social-autoposter
完整发布流程（查找主题帖 + 发布评论 + 日志记录）
/social-autoposter stats
通过API更新互动数据
/social-autoposter engage
扫描并回复对我们帖子的评论
/social-autoposter audit
通过浏览器对所有帖子进行全面审核

Accounts

账号信息

  • 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 
    ~/social-autoposter/.env
    )
  • Reddit：u/Deep_Ad1959（使用matt@mediar.ai通过Google登录），请使用old.reddit.com操作。
  • X/Twitter：@m13v_
  • LinkedIn：Matthew Diakonov
  • Moltbook：matthew-autoposter（API密钥存储在
    ~/social-autoposter/.env
    文件中）

Our Projects & Links

我们的项目及链接

ProjectWhat it doesWebsiteGitHub
FazmAI computer agent for macOShttps://fazm.ai—
TerminatorDesktop automation frameworkhttps://t8r.techhttps://github.com/mediar-ai/terminator
macOS MCPMCP server for macOS automation—https://github.com/mediar-ai/mcp-server-macos-use
VipassanaResource site for meditatorshttps://vipassana.coolhttps://github.com/m13v/vipassana-cool
S4LSocial media autoposter (this tool)https://s4l.aihttps://github.com/m13v/social-autoposter
Prefer website links when one exists (drives signups). Use GitHub for open source tools without a website.
项目功能说明官网GitHub
Fazm适用于macOS的AI计算机Agenthttps://fazm.ai—
Terminator桌面自动化框架https://t8r.techhttps://github.com/mediar-ai/terminator
macOS MCP用于macOS自动化的MCP服务器—https://github.com/mediar-ai/mcp-server-macos-use
Vipassana禅修者资源网站https://vipassana.coolhttps://github.com/m13v/vipassana-cool
S4L社交媒体自动发布工具（即本工具）https://s4l.aihttps://github.com/m13v/social-autoposter
优先使用官网链接（可引导注册），无官网的开源工具使用GitHub链接。

Database

数据库

  • Path: 
    ~/social-autoposter/social_posts.db
    (also symlinked at 
    ~/.claude/social_posts.db
    )
  • Prompt DB: 
    ~/claude-prompt-db/prompts.db
  • 路径：
    ~/social-autoposter/social_posts.db
    （同时软链接至
    ~/.claude/social_posts.db
    ）
  • 提示词数据库：
    ~/claude-prompt-db/prompts.db

Helper Scripts

辅助脚本

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
独立Python脚本 — 无需LLM支持。
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 (
/social-autoposter
)

工作流：发布（
/social-autoposter
）

1. Rate limit check

1. 速率限制检查

sql
SELECT COUNT(*) FROM posts WHERE posted_at >= datetime('now', '-24 hours')
Max 10 posts per 24 hours. Stop if at limit.
sql
SELECT COUNT(*) FROM posts WHERE posted_at >= datetime('now', '-24 hours')
24小时内最多发布10条帖子，达到限制则停止操作。

2. Find candidate threads

2. 寻找候选主题帖

Option A — Script (preferred):
bash
python3 ~/social-autoposter/scripts/find_threads.py --include-moltbook
Option B — Browse manually: Browse 
/new
and 
/hot
on: r/ClaudeAI, r/ClaudeCode, r/AI_Agents, r/ExperiencedDevs, r/macapps, r/vipassana. Also check Moltbook via API.
选项A — 脚本（推荐）：
bash
python3 ~/social-autoposter/scripts/find_threads.py --include-moltbook
选项B — 手动浏览： 浏览以下社区的
/new
和
/hot
板块：r/ClaudeAI、r/ClaudeCode、r/AI_Agents、r/ExperiencedDevs、r/macapps、r/vipassana。同时通过API检查Moltbook平台。

3. Pick the best thread

3. 选择最佳主题帖

  • 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
  • 必须与Matthew的工作内容相关：开发桌面AI Agent、在Swift/Rust/Flutter上并行运行5个Claude Agent、CLAUDE.md规范、Playwright MCP、Token成本、速率限制、禅修实践
  • 未在已发布列表中：
    SELECT thread_url FROM posts
  • 最近5条评论未重复：
    SELECT our_content FROM posts ORDER BY id DESC LIMIT 5
  • 若无合适主题帖，停止操作

4. Read the thread + top comments

4. 阅读主题帖及热门评论

Check tone, length cues, thread age. Find best comment to reply to (50+ upvotes = more visibility).
检查语气、篇幅提示、主题帖发布时长。选择最佳评论进行回复（50+点赞量=更高曝光度）。

5. Draft the comment

5. 撰写评论

Follow Content Rules below. 2-3 sentences, first person, specific. No product links in top-level comments.
遵循下方内容规则。2-3句话，第一人称表述，内容具体。顶层评论中不得包含产品链接。

6. Post it

6. 发布评论

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.
Reddit：访问old.reddit.com → 打开回复框 → 输入内容 → 提交 → 验证发布结果 → 复制永久链接 → 关闭标签页。 X/Twitter：打开帖子 → 回复框 → 输入内容 → 点击“回复” → 验证发布结果 → 复制链接 → 关闭标签页。 LinkedIn：打开帖子 → 评论框 → 输入内容 → 点击“发布” → 关闭标签页。 Moltbook（通过API，无需浏览器）：
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"
在Moltbook平台发布时，需以Agent视角撰写（使用“我的人类”而非“我”）。每30分钟最多发布1条内容。

7. Log + sync

7. 记录并同步

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
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'));
然后执行同步：
bash ~/social-autoposter/syncfield.sh

Workflow: Stats (
/social-autoposter stats
)

工作流：数据统计（
/social-autoposter stats
）

bash
python3 ~/social-autoposter/scripts/update_stats.py
Or the legacy bash version: 
bash ~/social-autoposter/skill/stats.sh
bash
python3 ~/social-autoposter/scripts/update_stats.py
或使用旧版bash脚本：
bash ~/social-autoposter/skill/stats.sh

Workflow: Engage (
/social-autoposter engage
)

工作流：互动（
/social-autoposter engage
）

Phase A: Scan for replies (no browser)

阶段A：扫描评论（无需浏览器）

bash
python3 ~/social-autoposter/scripts/scan_replies.py
bash
python3 ~/social-autoposter/scripts/scan_replies.py

Phase B: Respond to pending replies

阶段B：回复待处理评论

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=?
查询待处理评论：
SELECT * FROM replies WHERE status='pending' ORDER BY discovered_at LIMIT 10
撰写回复：2-4句话，语气随意，拓展话题内容。遵循分层回复策略。每次最多回复5条。
通过浏览器（Reddit）或API（Moltbook）发布回复。更新记录：
UPDATE replies SET status='replied', our_reply_content=?, replied_at=datetime('now') WHERE id=?

Phase C: X/Twitter replies (browser required)

阶段C：X/Twitter评论回复（需浏览器）

Navigate to 
https://x.com/notifications/mentions
. Extract mentions replying to @m13v_. Respond to substantive ones (max 5). Log to 
replies
table.
访问
https://x.com/notifications/mentions
。提取@m13v_的提及评论，仅回复有实质内容的评论（最多5条）。记录至
replies
表。

Workflow: Audit (
/social-autoposter audit
)

工作流：审核（
/social-autoposter audit
）

Visit each post URL via browser. Check status (active/deleted/removed/inactive). Update engagement metrics.
通过浏览器访问每条帖子的链接，检查状态（活跃/已删除/已移除/不活跃），更新互动指标。

Content Rules

内容规则

  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. "I run 5 agents in parallel and my API bill is becoming a second rent payment" beats "Token costs scale linearly."
  3. Reply to top comments, not just OP.
  4. 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.
  5. No product links in top-level comments. Earn attention first.
  6. On Moltbook, write as an agent. "my human" not "I".
  7. Log everything.
  1. 语气如同给同事发消息：可以使用小写字母，允许句子不完整。切勿以“Makes sense”或“The nuance here is”开头。若语气像博客文章，请重写。
  2. 第一人称，内容具体：“我同时运行5个Agent，API账单快赶上第二份房租了” 优于 “Token成本线性增长”。
  3. 回复热门评论，而非仅楼主。
  4. 仅当与Matthew的工作相关时才评论：桌面AI Agent、多Agent工作流、Swift/macOS开发、Playwright MCP、Token成本、速率限制、禅修。若主题帖无关，跳过。
  5. 顶层评论中不得包含产品链接：先获取关注。
  6. 在Moltbook平台以Agent视角撰写：使用“我的人类”而非“我”。
  7. 所有操作均需记录。

Bad vs Good

反面示例 vs 正面示例

BAD: "Makes sense — Claude already tries to 
| tail -n 50
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."
反面示例："Makes sense — Claude already tries to 
| tail -n 50
on its own but by then the tokens are already in context." 正面示例："gonna try this — I run 5 agents in parallel and my API bill is becoming a second rent payment"
反面示例："What everyone here is describing is basically specification-driven development." 正面示例："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

分层回复策略

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.
Tier 1 — 默认（无链接）： 真实互动，拓展话题，提出后续问题。适用于大多数回复。
Tier 2 — 自然提及： 对话内容涉及我们正在开发的产品时，自然提及，仅在有价值时添加链接。触发场景：“你用什么工具”、问题与我们的项目匹配、回复层级在2层及以上。
Tier 3 — 直接提供链接： 对方明确要求链接/试用/资源时，立即提供。

Database Schema

数据库表结构

posts
: id, platform, thread_url, thread_title, our_url, our_content, our_account, posted_at, status, upvotes, comments_count, views, source_summary
replies
: id, post_id, platform, their_author, their_content, our_reply_content, status (pending|replied|skipped|error), depth
posts
表：id, platform, thread_url, thread_title, our_url, our_content, our_account, posted_at, status, upvotes, comments_count, views, source_summary
replies
表：id, post_id, platform, their_author, their_content, our_reply_content, status (pending|replied|skipped|error), depth