Social Media Post logo

Social Media Post

Organization
qf-studio
social-media-post

Generate optimized social media posts for Threads, X (Twitter), and LinkedIn. Analyzes platform algorithms, applies best practices, and creates engaging content tailored to each platform. Local skill for Navigator marketing only.

Overview

Publisherqf-studio
Repositorynavigator
Skill namesocial-media-post
Stars
232
Forks
12
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by qf-studio on GitHub. Read the source before you install it.

Installation

Install the Social Media Post AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/qf-studio/navigator.git /tmp/navigator
mkdir -p .claude/skills
cp -r /tmp/navigator/skills-local/social-media-post .claude/skills/social-media-post
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Social Media Post in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Social Media Post on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Social Media Post is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Social Media Post Generator Skill

Generate platform-optimized social media posts using algorithm insights and best practices.

Note: This is a LOCAL skill for Navigator marketing only. NOT included in the plugin distribution.

When to Invoke

Auto-invoke when user says:

  • "Create a Threads post about [topic]"
  • "Write a social media post for [announcement]"
  • "Generate X post for [feature]"
  • "Create LinkedIn announcement for [release]"
  • "Write Threads post like option 5"

What This Does

Platform-Specific Workflow:

  1. Analyze Content: Extract key points, features, value propositions
  2. Apply Platform Rules: Character limits, formatting, hashtag strategies
  3. Optimize for Algorithm: Engagement tactics, timing recommendations
  4. Generate Variants: Multiple options (short, medium, detailed)
  5. Include Metadata: Character count, hashtag suggestions, posting time

Platforms Supported: Threads, X (Twitter), LinkedIn


Platform Specifications

Threads (Instagram)

Character Limits:

  • Standard post: 500 characters
  • Long-form (with attachment): 10,000 characters
  • Display: Shows "Read more" after ~500 chars

Formatting: ✅ Bold, italic, underline, strikethrough ✅ Emojis (count toward limit) ✅ Bullet points (using • or -) ✅ Line breaks ❌ No hashtags in Threads (algorithm ignores them) ❌ No clickable links in body (use link preview)

Media:

  • Images: Up to 10 per post
  • Video: Up to 5 minutes
  • Link previews: Automatic from URLs

Algorithm Priorities (2025):

  1. Engagement (40%): Likes, comments, shares, reply views
  2. Recency (30%): Fresh content gets priority
  3. Interest/Relevance (20%): Based on user's past interactions
  4. Profile Visits (10%): Likelihood user will click profile

Best Practices: ✅ Conversational, authentic tone (not corporate) ✅ Ask open-ended questions ✅ Create discussions, not announcements ✅ Post consistently (1-3x daily) ✅ Use visuals (images/videos boost engagement) ✅ Respond to comments within 1 hour ❌ No direct cross-posts from Instagram/X ❌ Avoid promotional language ❌ No hashtags (they don't work on Threads)

Optimal Posting Times (US audience):

  • Monday-Friday: 9-11 AM, 1-3 PM, 7-9 PM ET
  • Saturday-Sunday: 10 AM-2 PM ET

Content That Works:

  • Behind-the-scenes insights
  • Quick tips and tricks
  • Relatable experiences
  • Open-ended questions
  • Industry discussions
  • Memes (if relevant)

X (Twitter)

Character Limits:

  • Standard tweet: 280 characters
  • Premium (Blue): 25,000 characters (displays with "Show more")

Formatting: ✅ Emojis ✅ Line breaks (use intentionally) ✅ Mentions (@username) ✅ Hashtags (max 2-3 per tweet) ❌ No rich text formatting

Media:

  • Images: Up to 4 per tweet
  • Video: Up to 2:20 (standard), 10 min (Blue)
  • GIFs: 1 per tweet

Algorithm Priorities (2025):

  1. Engagement rate (likes, retweets, replies)
  2. Recency (fresh tweets prioritized)
  3. Media (tweets with images/video perform better)
  4. Authenticity (verified accounts, genuine engagement)

Best Practices: ✅ Front-load important info (first 100 chars) ✅ Use line breaks for readability ✅ 1-2 hashtags max (more hurts engagement) ✅ Include visual (image/video) ✅ Tag relevant accounts (when appropriate) ✅ Tweet threads for detailed content ❌ Don't overuse hashtags (looks spammy) ❌ Avoid link-only tweets (add context)

Optimal Posting Times (US audience):

  • Monday-Friday: 8-10 AM, 12-1 PM, 5-6 PM ET
  • Saturday-Sunday: 9 AM-12 PM ET

LinkedIn

Character Limits:

  • Post: 3,000 characters (shows "see more" after ~140 chars in feed)
  • Article: 125,000 characters

Formatting: ✅ Emojis (use sparingly) ✅ Bullet points ✅ Line breaks ✅ Bold (using Unicode) ✅ Numbered lists ❌ No official rich text (use workarounds)

Media:

  • Images: Up to 9 per post
  • Video: Up to 10 minutes
  • Documents: PDF uploads

Algorithm Priorities (2025):

  1. Dwell time (how long users read your post)
  2. Engagement (likes, comments, shares)
  3. Relevance (to user's network and interests)
  4. Personal connections (1st-degree connections prioritized)

Best Practices: ✅ Professional but authentic tone ✅ Hook in first 2 lines (before "see more") ✅ Tell stories, share insights ✅ Use data/statistics ✅ Ask for opinions (engagement) ✅ Tag relevant companies/people ✅ Post 2-5x per week ❌ Avoid overly promotional content ❌ Don't overuse hashtags (3-5 max)

Optimal Posting Times (US business hours):

  • Tuesday-Thursday: 8-10 AM, 12-1 PM ET
  • Avoid: Weekends, late evenings

Workflow Protocol

Step 1: Content Analysis

Execute: post_analyzer.py

Extract:

  • Key announcement/feature
  • Value proposition
  • Technical details
  • Target audience
  • Tone (technical, casual, professional)

Example Input:

Topic: Navigator v3.3.1 with nav-upgrade skill
Key features: One-command updates, automatic configuration
Value: 83% time savings (12 min → 2 min)
Audience: Developers using Claude Code

Output:

json
{
  "topic": "Navigator v3.3.1 plugin update automation",
  "key_points": [
    "One-command updates via nav-upgrade skill",
    "Automatic version detection from GitHub",
    "83% time savings",
    "18 total skills"
  ],
  "value_proposition": "Eliminates manual update process",
  "call_to_action": "Install or update Navigator",
  "tone": "technical-casual"
}

Step 2: Platform Optimization

Execute: engagement_optimizer.py --platform threads

Apply Platform Rules:

  • Character limit enforcement
  • Formatting constraints
  • Hashtag strategy
  • Media recommendations
  • CTA placement

Optimize for Algorithm:

  • Engagement hooks
  • Question placement
  • Visual suggestions
  • Timing recommendations

Step 3: Generate Post Variants

Create 3 Variants:

  1. Short & Punchy (Option 5 style)

    • Under 280 chars (X-compatible)
    • Emoji bullets
    • Clear value props
    • Direct CTA
  2. Medium Detailed

    • 300-500 chars (Threads standard)
    • More context
    • Multiple CTAs
    • Conversation starter
  3. Long-Form (Threads attachment / LinkedIn)

    • 800-1500 chars
    • Full story/context
    • Multiple sections
    • Rich formatting

Step 4: Add Metadata

For each variant, include:

markdown
**Platform**: Threads
**Character Count**: 287/500
**Estimated Engagement**: High (question + visual + emojis)
**Hashtags**: None (Threads doesn't use hashtags)
**Media Suggestion**: Screenshot of update command
**Best Time to Post**: Tuesday 9-11 AM ET
**Follow-up**: Reply with technical details after 2 hours

Templates

Template: Product Launch (Threads)

[Hook Question]

[Product Name] [Version] just landed:

✅ [Feature 1]: [Benefit]
✅ [Feature 2]: [Benefit]
✅ [Feature 3]: [Benefit]
✅ [Key Metric]: [Value proposition]

[CTA 1]:
[Command/Installation]

[CTA 2]:
[Command/Update]

[Link]

[Conversation Hook]

Example:

Teach your Claude Code to design like a Product Designer.

Navigator v3.3.1:
✅ Figma MCP (design extraction)
✅ Storybook automation
✅ Chromatic integration
✅ One-command updates

Install:
/plugin marketplace add alekspetrov/navigator

Update:
"Update Navigator"

https://github.com/alekspetrov/navigator

What's your biggest design handoff pain point?

Character Count: 289/500 Engagement Hook: Opening question + closing question


Template: Feature Announcement (X)

[Feature Name] just shipped 🚀

[Key benefit in 1 line]

[Emoji] [Feature detail 1]
[Emoji] [Feature detail 2]
[Emoji] [Feature detail 3]

[CTA with link]

[Optional: Thread continuation →]

Example:

One-command Navigator updates 🚀

No more manual /plugin update, CLAUDE.md editing, or verification.

✅ "Update Navigator"
✅ 2 min vs 12 min manual
✅ 95% success rate

Install: /plugin marketplace add alekspetrov/navigator

https://github.com/alekspetrov/navigator

Character Count: 241/280 Thread continuation: Technical details, user testimonial, or demo


Template: Technical Deep-Dive (LinkedIn)

[Professional Hook - Problem Statement]

[Solution Introduction]

**What we built:**
• [Technical detail 1]
• [Technical detail 2]
• [Technical detail 3]

**The impact:**
[Metric 1]: [Before] → [After] ([Percentage] improvement)
[Metric 2]: [Specific outcome]

**How it works:**
1. [Step 1]
2. [Step 2]
3. [Step 3]

This is what AI-native [domain] looks like: [insight].

[Technical details / GitHub link]

**Thoughts?** [Open-ended question for discussion]

#[Hashtag1] #[Hashtag2] #[Hashtag3]

Predefined Functions

functions/post_analyzer.py

Purpose: Extract key points and structure from content

Usage:

bash
python post_analyzer.py --input "Navigator v3.3.1 announcement" --context release-notes.md

Output:

json
{
  "topic": "...",
  "key_points": [...],
  "value_proposition": "...",
  "tone": "...",
  "suggested_platforms": ["threads", "x", "linkedin"]
}

functions/engagement_optimizer.py

Purpose: Apply platform-specific optimization rules

Usage:

bash
python engagement_optimizer.py --platform threads --content post-draft.md

Output:

json
{
  "optimized_content": "...",
  "character_count": 287,
  "engagement_score": 8.5,
  "improvements": [
    "Added opening question for engagement",
    "Replaced hashtags with conversation hook",
    "Added emojis for visual appeal"
  ],
  "warnings": ["Consider adding visual (screenshot/demo)"]
}

functions/thread_generator.py

Purpose: Generate multi-post threads for detailed announcements

Usage:

bash
python thread_generator.py --platform x --content full-announcement.md --max-tweets 5

Output: Structured thread with optimal character distribution


Examples

Example 1: Quick Feature Announcement (Threads)

Input:

Generate Threads post for Navigator v3.3.1 nav-upgrade skill, short & punchy style

Output:

markdown
**Platform**: Threads
**Style**: Short & Punchy (Option 5)
**Character Count**: 274/500

---

Teach Claude Code to automate like a Pro.

Navigator v3.3.1:
✅ One-command updates
✅ Figma MCP integration
✅ Storybook + Chromatic automation
✅ 18 skills total

"Update Navigator" → Done in 2 min

Install: /plugin marketplace add alekspetrov/navigator

https://github.com/alekspetrov/navigator

What workflow would you automate next?

---

**Metadata**:
- Engagement Hook: Opening statement + closing question
- Visual Suggestion: Terminal screenshot of "Update Navigator" command
- Best Time: Tuesday 10 AM ET
- Follow-up: Reply with technical details after 2 hours

Example 2: Technical Thread (X)

Input:

Create X thread explaining complete design-to-production pipeline

Output:

markdown
**Platform**: X (Twitter)
**Format**: Thread (4 tweets)

---

**Tweet 1/4** (Main)
We just automated the complete design-to-production pipeline.

Figma → Code → Testing in 20 minutes.

Here's how Navigator v3.3.1 makes it work: 🧵👇

(278/280 chars)

---

**Tweet 2/4**
Step 1: Design Extraction

"Review this design from Figma"

Navigator + Figma MCP:
✅ Extracts design tokens (DTCG)
✅ Maps components
✅ Detects drift
✅ Generates plan

15 minutes vs 6-10 hours manually

(195/280 chars)

---

**Tweet 3/4**
Step 2: Visual Regression

"Set up visual regression for Button"

Navigator:
✅ Generates Storybook stories
✅ Configures Chromatic
✅ Creates CI workflows

5 minutes vs 2-3 hours manually

(184/280 chars)

---

**Tweet 4/4**
The result:

Design handoff → Pixel-perfect CI in 20 minutes

All with natural language.
97% token efficiency.
18 skills for complete automation.

Try it: /plugin marketplace add alekspetrov/navigator

https://github.com/alekspetrov/navigator

(242/280 chars)

---

**Metadata**:
- Total thread length: 4 tweets, 899 chars total
- Engagement: Question/discussion starter in replies
- Visual: Attach architecture diagram to tweet 1
- Best Time: Wednesday 9 AM ET

Best Practices by Platform

Threads

  1. Be conversational: Avoid corporate speak
  2. Ask questions: Drive engagement with open-ended questions
  3. No hashtags: They don't work on Threads
  4. Respond fast: Reply to comments within 1 hour
  5. Post consistently: 1-3x daily for best reach
  6. Use visuals: Images/videos boost engagement significantly
  7. Tell stories: Personal experiences > announcements

X (Twitter)

  1. Front-load value: First 100 chars matter most
  2. Use threads: Break complex topics into digestible tweets
  3. Limit hashtags: 1-2 max, more hurts engagement
  4. Add media: Tweets with images get 150% more engagement
  5. Be concise: Shorter tweets (200-250 chars) perform better
  6. Time it right: Post during work hours for tech audience

LinkedIn

  1. Hook early: First 2 lines show in feed, make them count
  2. Be professional: But still authentic and relatable
  3. Use data: Statistics and metrics boost credibility
  4. Tell stories: Case studies and experiences resonate
  5. Engage back: Comment on posts in your niche
  6. Post less, quality more: 2-5x per week is optimal

Usage Patterns

Pattern 1: Quick Announcement

"Create Threads post for v3.3.1 release, option 5 style"

Generates: Short & punchy Threads post with emojis, clear CTAs, character count

Pattern 2: Multi-Platform Campaign

"Generate social media posts for v3.3.1 across Threads, X, and LinkedIn"

Generates: Platform-optimized variants for each channel

Pattern 3: Thread Explanation

"Create X thread explaining visual-regression skill workflow"

Generates: Multi-tweet thread with optimal character distribution


Engagement Scoring

Posts are scored 1-10 based on:

  • Hook strength (2 points): Captures attention in first line
  • Value clarity (2 points): Clear benefit/value proposition
  • Engagement prompts (2 points): Questions, CTAs
  • Visual appeal (2 points): Emojis, formatting, media suggestion
  • Platform fit (2 points): Follows platform best practices

Score 8-10: High engagement potential Score 5-7: Moderate, could be improved Score 1-4: Needs significant revision


Version History

  • v1.0.0: Initial skill for Navigator marketing (Threads, X, LinkedIn support)

Last Updated: 2025-10-21 Skill Type: Local (Navigator marketing only) Not included in plugin distribution

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Social Media Post AI skill do?

Generate optimized social media posts for Threads, X (Twitter), and LinkedIn. Analyzes platform algorithms, applies best practices, and creates engaging content tailored to each platform. Local skill for Navigator marketing only.

Why use Social Media Post on TypingMind?

Because you install it once and use it with any model. Social Media Post is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Social Media Post in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/qf-studio/navigator/tree/main/skills-local/social-media-post. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Social Media Post?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Social Media Post?

As many as you like. As long as a model supports skills, you can use Social Media Post with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Social Media Post AI skill free?

Yes. It is published on GitHub by qf-studio under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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