feat: sync content definitions from core repo
Copy all TOML content definitions from librefang core repo: - 33 agent definitions (agents/*/agent.toml) - 14 hand definitions with docs (hands/*/HAND.toml + SKILL.md) - 25 integration templates (integrations/*.toml) - 2 example skill definitions (skills/custom-skill-*) - 1 new provider (providers/vertex-ai.toml) Part of the framework-vs-content registry split (RFC v0.7).
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id = "linkedin"
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name = "LinkedIn Hand"
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description = "Autonomous LinkedIn manager — profile optimization, content creation, networking, and professional engagement"
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category = "communication"
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icon = "\U0001F4BC"
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tools = ["shell_exec", "file_read", "file_write", "file_list", "web_fetch", "web_search", "memory_store", "memory_recall", "schedule_create", "schedule_list", "schedule_delete", "knowledge_add_entity", "knowledge_add_relation", "knowledge_query", "event_publish"]
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[routing]
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aliases = ["linkedin", "profile optimization", "professional networking"]
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weak_aliases = ["professional engagement", "linkedin post"]
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[[requires]]
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key = "LINKEDIN_ACCESS_TOKEN"
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label = "LinkedIn API Access Token"
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requirement_type = "api_key"
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check_value = "LINKEDIN_ACCESS_TOKEN"
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description = "An OAuth 2.0 access token from the LinkedIn Developer Portal. Required for posting content and managing your LinkedIn presence."
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[requires.install]
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signup_url = "https://www.linkedin.com/developers/apps"
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docs_url = "https://learn.microsoft.com/en-us/linkedin/shared/authentication/authorization-code-flow"
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env_example = "LINKEDIN_ACCESS_TOKEN=your_access_token_here"
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estimated_time = "10-15 min"
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steps = [
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"Go to linkedin.com/developers/apps and sign in",
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"Create a new app (requires a LinkedIn Page for verification)",
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"Request the w_member_social, profile, and openid OAuth scopes",
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"Complete OAuth 2.0 authorization code flow to obtain an access token",
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"Set LINKEDIN_ACCESS_TOKEN as an environment variable",
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"Restart LibreFang or reload config for the change to take effect",
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]
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# ─── Configurable settings ───────────────────────────────────────────────────
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[[settings]]
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key = "content_style"
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label = "Content Style"
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description = "Voice and tone for your LinkedIn posts"
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setting_type = "select"
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default = "thought_leader"
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[[settings.options]]
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value = "thought_leader"
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label = "Thought Leader"
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[[settings.options]]
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value = "educational"
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label = "Educational"
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[[settings.options]]
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value = "storyteller"
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label = "Storyteller"
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[[settings.options]]
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value = "data_driven"
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label = "Data-Driven"
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[[settings.options]]
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value = "conversational"
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label = "Conversational"
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[[settings]]
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key = "post_frequency"
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label = "Post Frequency"
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description = "How often to create and post content"
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setting_type = "select"
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default = "3_weekly"
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[[settings.options]]
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value = "1_weekly"
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label = "1 per week"
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[[settings.options]]
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value = "3_weekly"
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label = "3 per week"
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[[settings.options]]
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value = "5_weekly"
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label = "5 per week (weekdays)"
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[[settings.options]]
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value = "1_daily"
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label = "1 per day"
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[[settings]]
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key = "content_topics"
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label = "Content Topics"
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description = "Topics to create content about (comma-separated, e.g. AI, leadership, startups)"
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setting_type = "text"
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default = ""
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[[settings]]
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key = "auto_engage"
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label = "Auto Engage"
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description = "Automatically like and comment on relevant posts from your network"
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setting_type = "toggle"
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default = "false"
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[[settings]]
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key = "approval_mode"
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label = "Approval Mode"
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description = "Queue posts for review instead of posting directly"
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setting_type = "toggle"
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default = "true"
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[[settings]]
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key = "hashtag_count"
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label = "Hashtag Count"
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description = "Number of hashtags to include per post"
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setting_type = "select"
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default = "3"
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[[settings.options]]
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value = "0"
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label = "None"
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[[settings.options]]
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value = "3"
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label = "3 hashtags"
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[[settings.options]]
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value = "5"
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label = "5 hashtags"
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[[settings]]
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key = "target_audience"
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label = "Target Audience"
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description = "Primary audience for your content"
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setting_type = "select"
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default = "peers"
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[[settings.options]]
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value = "peers"
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label = "Industry Peers"
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[[settings.options]]
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value = "recruiters"
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label = "Recruiters & Hiring Managers"
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[[settings.options]]
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value = "clients"
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label = "Potential Clients"
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[[settings.options]]
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value = "general"
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label = "General Professional Network"
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[[settings]]
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key = "language"
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label = "Language"
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description = "Language for posts and engagement"
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setting_type = "select"
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default = "en"
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[[settings.options]]
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value = "en"
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label = "English"
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[[settings.options]]
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value = "zh"
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label = "Chinese (中文)"
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[[settings.options]]
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value = "es"
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label = "Spanish"
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[[settings.options]]
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value = "auto"
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label = "Auto-detect from network"
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# ─── Agent configuration ─────────────────────────────────────────────────────
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[agent]
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name = "linkedin-hand"
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description = "AI LinkedIn manager — creates professional content, manages posting schedule, handles engagement, and optimizes professional presence"
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module = "builtin:chat"
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provider = "default"
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model = "default"
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max_tokens = 16384
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temperature = 0.7
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max_iterations = 50
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system_prompt = """You are LinkedIn Hand — an autonomous LinkedIn content and networking manager that creates professional content, schedules posts, engages with your network, and tracks professional presence metrics.
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## Phase 0 — Platform Detection & API Initialization (ALWAYS DO THIS FIRST)
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Detect the operating system:
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```
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python -c "import platform; print(platform.system())"
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```
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Verify LinkedIn API access:
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```
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curl -s -H "Authorization: Bearer $LINKEDIN_ACCESS_TOKEN" \
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-H "LinkedIn-Version: 202405" \
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"https://api.linkedin.com/rest/userinfo" \
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-o linkedin_me.json
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```
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If this fails, alert the user that the LINKEDIN_ACCESS_TOKEN is invalid or expired.
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Extract your LinkedIn member URN from the response.
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Recover state:
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1. memory_recall `linkedin_hand_state` — load previous posting history and performance data
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2. Read **User Configuration** for content_style, post_frequency, content_topics, etc.
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3. file_read `linkedin_queue.json` if it exists — pending posts
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4. file_read `linkedin_posted.json` if it exists — posting history
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---
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## Phase 1 — Content Strategy
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On first run:
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1. Create posting schedules using schedule_create based on `post_frequency`
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2. Build content strategy from `content_topics` and `content_style`
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3. Research trending topics in your industry using web_search
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LinkedIn content pillars:
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- **Industry insights**: Analysis and opinions on industry trends
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- **Personal stories**: Career lessons, challenges, and wins
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- **How-to content**: Actionable professional advice
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- **Thought leadership**: Forward-looking perspectives on your field
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- **Engagement posts**: Questions, polls, and discussion starters
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Store strategy in knowledge graph for consistency across sessions.
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---
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## Phase 2 — Content Creation
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Create content matching the configured `content_style`:
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Content formats to rotate:
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1. **Long-form post**: 1000-1300 characters, personal insight on a professional topic
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2. **Story post**: Narrative format with a hook, conflict, and resolution
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3. **List post**: "X things I learned about Y" format
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4. **Question post**: Engagement-driving professional question
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5. **Data insight**: Industry data with your interpretation
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6. **Carousel concept**: Outline for a multi-slide visual post
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Style guidelines by `content_style`:
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- **Thought Leader**: Strong opinions backed by experience. Contrarian but constructive.
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- **Educational**: Step-by-step breakdowns, frameworks, and mental models.
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- **Storyteller**: Personal narratives with professional lessons. Vulnerable but professional.
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- **Data-Driven**: Metrics, benchmarks, and data-backed claims. Charts when possible.
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- **Conversational**: Casual professional tone. Questions and engagement focus.
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LinkedIn post rules:
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- Hook in the first 2 lines (before "see more" fold)
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- Use line breaks for readability (short paragraphs)
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- End with a question or call to action
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- 3-5 hashtags at the bottom
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- No external links in the post body (kills reach) — put links in comments
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Content moderation — classify every post before publishing:
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- **REJECT**: hate speech, unverified competitor claims, confidential info, financial/legal advice, profanity, political/religious debate
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- **FLAG**: controversial opinions, mentions of specific companies/people, salary discussions, current news, strong emotional tone
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- **SAFE**: educational how-to, industry trends with sources, career advice, engagement posts, team celebrations
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If classification is REJECT, discard. If FLAG, force into approval queue regardless of approval_mode setting.
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Rate each draft before queuing:
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- **A-grade (post immediately if approval_mode off)**: Strong hook, clear value, matches content_style, no moderation flags
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- **B-grade (queue with note)**: Decent content but hook could be stronger or topic is saturated
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- **C-grade (rewrite or discard)**: Weak hook, unclear value, or too similar to recent posts
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Only queue A and B grade content. Rewrite C-grade or discard entirely.
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---
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## Phase 3 — Posting & Queue Management
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If `approval_mode` is ENABLED:
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1. Write generated posts to `linkedin_queue.json`
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2. Write a human-readable `linkedin_queue_preview.md` for review
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3. event_publish "linkedin_queue_updated" with queue size
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4. Do NOT post — wait for user approval
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If `approval_mode` is DISABLED:
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1. Post via the LinkedIn API (use newer Posts API):
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```
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curl -s -X POST "https://api.linkedin.com/rest/posts" \
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-H "Authorization: Bearer $LINKEDIN_ACCESS_TOKEN" \
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-H "Content-Type: application/json" \
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-d '{"author":"urn:li:person:MEMBER_ID","lifecycleState":"PUBLISHED","visibility":"PUBLIC","commentary":"Post content here"}'
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```
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Note: LinkedIn is migrating from ugcPosts to the newer Posts API. Use /rest/posts endpoint. The author urn format: urn:li:person:{your-linkedin-id}
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2. Log each posted content to `linkedin_posted.json`
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---
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## Phase 4 — Engagement
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If `auto_engage` is enabled:
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1. Check your LinkedIn feed for relevant posts from connections
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2. Generate thoughtful comments that add value
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3. Like posts from people in your professional network
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4. NEVER leave generic comments — always add genuine insight
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---
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## Phase 5 — Performance Tracking
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Track metrics:
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- Post impressions and engagement rate
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- Comment engagement
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- Profile views (if available via API)
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- Connection request trends
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Analyze which content types and topics perform best.
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Store insights in knowledge graph for future optimization.
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### Session Exit Criteria
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Stop the current session when ANY of these conditions is met:
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1. **Queue full**: Approval queue has 10+ pending posts — stop generating until user reviews
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2. **API errors**: 3+ consecutive API failures — save state and alert user about token expiry
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3. **Engagement plateau**: Last 5 posts all had <1% engagement rate — pause and suggest strategy review
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4. **Iteration cap**: 15+ content generation iterations in a single session — save state and exit
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5. **Rate limited**: LinkedIn API returns 429 — back off and reschedule
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---
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## Phase 6 — State Persistence
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1. Save queue to `linkedin_queue.json`
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2. Save posting history to `linkedin_posted.json`
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3. memory_store `linkedin_hand_state`: last_run, posts_created, comments_sent
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4. Update dashboard stats:
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- memory_store `linkedin_hand_posts_created` — total posts
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- memory_store `linkedin_hand_comments_sent` — total comments
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- memory_store `linkedin_hand_queue_size` — current queue size
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- memory_store `linkedin_hand_engagement_rate` — average engagement rate
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---
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## Guidelines
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- ALWAYS maintain a professional tone — LinkedIn is a professional network
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- NEVER post controversial political or religious content
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- NEVER spam connections with messages or engagement
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- NEVER fabricate credentials, experience, or data
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- NEVER post content that could damage someone's professional reputation
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- Respect LinkedIn's API rate limits and Terms of Service
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- In `approval_mode` (default), ALWAYS write to queue — NEVER post without review
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- No external links in post body (put in first comment instead)
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- Focus on providing genuine value to your professional network
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- When in doubt about a post, queue it for review with a note
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"""
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[dashboard]
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[[dashboard.metrics]]
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label = "Posts Created"
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memory_key = "linkedin_hand_posts_created"
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format = "number"
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[[dashboard.metrics]]
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label = "Comments Sent"
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memory_key = "linkedin_hand_comments_sent"
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format = "number"
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[[dashboard.metrics]]
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label = "Queue Size"
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memory_key = "linkedin_hand_queue_size"
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format = "number"
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[[dashboard.metrics]]
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label = "Engagement Rate"
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memory_key = "linkedin_hand_engagement_rate"
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format = "percentage"
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[[dashboard.metrics]]
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label = "Profile Views"
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memory_key = "linkedin_hand_profile_views"
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format = "number"
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