locoagent-social-media-automation

# LocoAgent Social Media Automation > Skill by [ara.so](https://ara.so) — AI Agent Skills collection. LocoAgent is an AI-powered social media agent that autonomously operates social media accounts through real browser automation. It combines an LLM-driven agentic loop with Chrome DevTools Protocol (CDP) to perceive, decide, and act on live web pages — performing tasks like liking posts, writing replies, following users, and publishing content. **Key capabilities:** - Real browser automation with Chrome CDP (uses actual login sessions) - Platform skill system (32+ operations for X.com built-in) - Workflow engine for deterministic automation pipelines - Operation log for persistent deduplication across sessions - Multi-provider LLM support (OpenRouter, DeepSeek, Ollama, etc.) ## Installation ### Prerequisites Install required dependencies: ```bash # Install Bun runtime curl -fsSL https://bun.sh/install | bash # Install agent-browser CLI npm install -g @vercel/agent-browser ``` ### Project Setup ```bash git clone https://github.com/LocoreMind/locoagent.git cd locoagent bun install ``` ### Configuration Create `.env` file in project root: ```env # OpenRouter (recommended - access 200+ models) CLAUDE_CODE_USE_OPENAI=1 OPENAI_API_KEY=sk-or-v1-... OPENAI_BASE_URL=https://openrouter.ai/api/v1 OPENAI_MODEL=anthropic/claude-sonnet-4.5 # Required for automated mode SKIP_PERMISSIONS=1 ``` Alternative provider configurations: ```env # DeepSeek (with thinking mode) CLAUDE_CODE_USE_OPENAI=1 OPENAI_API_KEY=<DEEPSEEK_API_KEY> OPENAI_BASE_URL=https://api.deepseek.com OPENAI_MODEL=deepseek-v4-flash # Ollama (local models) CLAUDE_CODE_USE_OPENAI=1 OPENAI_API_KEY=ollama OPENAI_BASE_URL=http://localhost:11434/v1 OPENAI_MODEL=llama3.2 # Anthropic direct (native SDK) ANTHROPIC_API_KEY=<ANTHROPIC_API_KEY> ``` ### Browser Setup ```bash # One-time: copy Chrome profile and launch with CDP bun run setup-chrome # Connect agent-browser to running Chrome agent-browser connect 9222 ``` ## Core Commands ### Interactive Mode ```bash # Start interactive session bun start # Load X.com skill and execute task > /x-com open home timeline, like first 3 posts about AI # Check operation history > /operation-log recent --limit 20 ``` ### Headless Mode ```bash # Single query execution bun start -p "open X.com and like the first post about AI agents" # With specific model bun start --model anthropic/claude-sonnet-4.5 -p "/x-com like 5 posts about LLMs" # Platform-specific task bun start -p "/x-com like 5 posts about 'large language models', then follow the authors" ``` ## Platform Skills Skills inject complete operation playbooks into the agent's context. ### X.com Skill ```bash # Interactive > /x-com open home timeline, like first 3 posts about AI, reply to the best one # Headless bun start -p "/x-com like 5 posts about 'machine learning', follow authors with >1k followers" ``` Available X.com operations (32+): - Navigation: home, notifications, messages, profile, search - Engagement: like, retweet, reply, quote tweet - Social graph: follow, unfollow, mute, block - Content: post tweet, post thread, upload media - Profile: edit bio, change avatar, update banner - Lists: create, add members, view ### Creating Custom Skills Create `skills/linkedin/SKILL.md`: ```markdown --- description: "LinkedIn platform operations playbook" allowed-tools: - Bash user-invocable: true --- # LinkedIn Operations ## 1. Navigation ### Open Home Feed ```bash agent-browser open https://www.linkedin.com/feed ``` ### Search Posts ```bash agent-browser open "https://www.linkedin.com/search/results/content/?keywords=AI%20agents" agent-browser snapshot -i -c -s 'div[data-post-id]' ``` ## 2. Engagement ### Like Post 1. Find post element with `agent-browser snapshot -i` 2. Locate like button (usually `button[aria-label*="Like"]`) 3. Click: `agent-browser click @e<ref>` ### Comment on Post 1. Find comment input (usually `div[role="textbox"]`) 2. Click to focus: `agent-browser click @e<ref>` 3. Type comment: `agent-browser fill @e<ref> "Insightful post!"` 4. Find submit button and click ``` Load the skill: ```bash bun start > /linkedin search for posts about 'AI safety', like top 3 ``` ## Workflow Engine Workflows are deterministic browser-automation pipelines that run without LLM involvement. ### Built-in Workflows ```bash # List all workflows bun run workflow list # Run once (blocking) bun run workflow run --id hf-papers-to-x # Run once (background) bun run workflow start --id hf-papers-to-x # Daemon mode (every 3 minutes) bun run workflow daemon --id x-search-reply --interval 3 # Stop workflow bun run workflow stop --id x-search-reply # View status bun run workflow status # View execution history bun run workflow history --id hf-papers-to-x ``` ### Creating Custom Workflows **Step 1:** Create workflow definition `workflows/linkedin-engagement.json`: ```json { "id": "linkedin-engagement", "name": "LinkedIn Daily Engagement", "description": "Search for AI posts on LinkedIn and engage", "schedule": "daily", "executor": "executors/linkedin-engagement.ts", "config": { "searchQuery": "artificial intelligence", "maxPosts": 5, "cdpPort": 9222 } } ``` **Step 2:** Create executor `workflows/executors/linkedin-engagement.ts`: ```typescript #!/usr/bin/env bun import { execSync } from 'node:child_process' // Parse config from workflow engine const configArg = process.argv.find((_, i, a) => a[i - 1] === '--config') const config = JSON.parse(configArg!) // agent-browser helper function ab(cmd: string): string { return execSync(`agent-browser --cdp ${config.cdpPort} ${cmd}`, { encoding: 'utf-8', timeout: 30000, }).trim() } // Helper to check operation log function hasEngaged(postUrl: string): boolean { try { execSync(`bun run scripts/log-operation.ts check --platform linkedin --action like --url "${postUrl}"`, { encoding: 'utf-8', stdio: 'ignore' }) return true // exit 0 = already done } catch { return false // exit 1 = not done } } // Helper to log operation function logOperation(postUrl: string, action: string, status: string, note: string) { execSync(`bun run scripts/log-operation.ts add --platform linkedin --action ${action} --url "${postUrl}" --status ${status} --note "${note}"`, { encoding: 'utf-8', stdio: 'inherit' }) } console.error('[linkedin-engagement] Starting workflow...') // Step 1: Navigate to search console.error(`[linkedin-engagement] Searching for: ${config.searchQuery}`) const searchUrl = `https://www.linkedin.com/search/results/content/?keywords=${encodeURIComponent(config.searchQuery)}` ab(`open "${searchUrl}"`) ab('wait 3000') // Step 2: Get posts console.error('[linkedin-engagement] Getting posts...') const snapshot = ab('snapshot -i -c -s \'div[data-post-id]\'') const posts = JSON.parse(snapshot) let engaged = 0 const stepsTotal = Math.min(posts.length, config.maxPosts) // Step 3: Engage with posts for (let i = 0; i < stepsTotal; i++) { const post = posts[i] const postUrl = post.attributes?.['data-urn'] || `post-${i}` // Check if already engaged if (hasEngaged(postUrl)) { console.error(`[linkedin-engagement] Already engaged with ${postUrl}, skipping`) continue } // Find like button const likeButton = post.children?.find((el: any) => el.attributes?.['aria-label']?.includes('Like') ) if (likeButton?.ref) { ab(`click ${likeButton.ref}`) logOperation(postUrl, 'like', 'success', `Workflow: ${config.searchQuery}`) engaged++ console.error(`[linkedin-engagement] Liked post ${i + 1}/${stepsTotal}`) ab('wait 2000') // Rate limiting } } // Output final summary (required) console.log(JSON.stringify({ stepsCompleted: engaged, stepsTotal, searchQuery: config.searchQuery })) ``` **Step 3:** Run workflow: ```bash bun run workflow run --id linkedin-engagement ``` ## Operation Log Persistent memory prevents duplicate actions across sessions. ### Check Before Acting ```typescript import { execSync } from 'node:child_process' function hasLiked(postUrl: string): boolean { try { execSync(`bun run scripts/log-operation.ts check --platform x --action like --url "${postUrl}"`, { encoding: 'utf-8', stdio: 'ignore' }) return true // exit 0 = already done } catch { return false // exit 1 = not done } } const url = "https://x.com/user/status/123" if (hasLiked(url)) { console.log("Already liked this post") } else { // Perform like action execSync(`agent-browser click @e5`) // Log operation execSync(`bun run scripts/log-operation.ts add --platform x --action like --url "${url}" --status success --note "AI research post"`) } ``` ### CLI Operations ```bash # Check if operation was performed (exit 0 = done, exit 1 = not done) bun run scripts/log-operation.ts check \ --platform x \ --action like \ --url "https://x.com/user/status/123" # Record operation bun run scripts/log-operation.ts add \ --platform x \ --action like \ --url "https://x.com/user/status/123" \ --status success \ --note "AI agents research post" # View recent operations bun run scripts/log-operation.ts recent --limit 20 # 30-day summary bun run scripts/log-operation.ts summary --days 30 ``` State stored in `persona/operation-log.json`. ## Task Scheduling Structure daily/weekly tasks instead of ad-hoc prompts. ### Define Tasks Edit `persona/tasks.md`: ```markdown ## Daily Tasks 1. Engage with AI research content (like 5-10 posts) 2. Monitor project mentions and respond 3. Leave 1-2 technical comments on relevant posts ## Weekly Tasks (Monday) 4. Follow 3-5 relevant researchers or developers 5. Post 1 original tweet about recent findings ## Session Constraints | Action | Max per session | |----------|----------------| | Likes | 10 | | Comments | 2 | | Follows | 5 | | Posts | 1 | ``` ### Run Tasks ```bash # Execute today's tasks bun run run-tasks # Preview prompt without running bun run run-tasks:dry # Restrict to one platform bun run run-tasks -- --platform x ``` ## Real-time Trajectory Monitor Watch live execution status instead of black-box `--print` mode. ```bash # Terminal 1: start monitor bun run tail # Terminal 2: run agent bun start -p "/x-com open timeline, like first post" ``` Output shows live execution: ``` ═══ New Task ═══ /x-com open timeline, like first post [6:30:47 PM] ⚡ Bash: agent-browser connect 9222 [6:30:47 PM] ✓ Result: Done [6:31:10 PM] ⚡ Bash: agent-browser open https://x.com/home [6:31:27 PM] ⚡ Bash: agent-browser snapshot -i -c -s 'article' [6:31:44 PM] ● Agent: Found first post, like button ref=e136 [6:31:44 PM] ⚡ Bash: agent-browser click e136 [6:31:45 PM] ✓ Result: Done ``` Additional commands: ```bash # Replay latest session from beginning bun run tail:history # List recent sessions bun run tail:list # Watch specific session bun run tail <session-id> ``` ## Common Patterns ### Pattern: Safe Engagement Loop ```typescript #!/usr/bin/env bun import { execSync } from 'node:child_process' function ab(cmd: string): string {