Social Media Manager

Social Media Manager

By The Agent Ledger — AI-native social media system for solopreneurs.

Your agent manages your social presence across platforms — scheduling, repurposing, tracking engagement, and telling you what's actually working.

What This Does

Most solopreneurs know they should post consistently but can't sustain it. This skill turns your AI agent into a social media manager that:

• Maintains a posting queue per platform with scheduled dates
• Tracks engagement metrics (likes, comments, shares, clicks) per post
• Identifies what's working through weekly performance analysis
• Repurposes content across platforms (one idea → 5 posts)
• Suggests optimal posting times based on your engagement data
• Monitors reply/DM triage priorities
• Runs weekly social media reviews with actionable insights

Setup

Step 1: Create State File

Create social-media/social-state.md in your workspace:

# Social Media State

## Platforms
| Platform | Handle | Status | Posting Cadence | Best Times |
|----------|--------|--------|-----------------|------------|
| Twitter/X | @yourhandle | Active | 5x/week | 9am, 12pm, 5pm |
| LinkedIn | /in/yourname | Active | 3x/week | 8am, 12pm Tue-Thu |
| Reddit | u/yourname | Active | 2x/week | 10am, 7pm |

## Content Pillars
1. [Your expertise topic] — educational, how-to
2. [Your story/journey] — behind-the-scenes, lessons learned
3. [Industry commentary] — opinions, trends, reactions
4. [Community engagement] — questions, polls, responses

## Voice Notes
- Tone: [casual/professional/witty/authoritative]
- Emoji usage: [none/minimal/moderate/heavy]
- Hashtag strategy: [none/1-2 per post/platform-specific]

Step 2: Create Queue File

Create social-media/queue.md:

# Social Media Queue

## Drafts (Ready to Post)
| # | Platform | Content | Pillar | Scheduled | Status |
|---|----------|---------|--------|-----------|--------|
| 1 | Twitter | [draft text] | Education | Mon 9am | Ready |
| 2 | LinkedIn | [draft text] | Journey | Tue 8am | Ready |

## Ideas (Need Drafting)
- [Topic idea from article/conversation/trend]
- [Repurpose from newsletter #X]
- [React to [trending topic]]

## Posted (Last 7 Days)
| Date | Platform | Content Preview | Likes | Comments | Shares | Clicks |
|------|----------|----------------|-------|----------|--------|--------|

Step 3: Add to AGENTS.md

## Social Media
- Check `social-media/queue.md` when I ask about social content
- Draft posts matching my voice profile in `social-media/social-state.md`
- Track engagement metrics when I share them
- NEVER auto-post — always draft and wait for approval
- Repurpose long-form content (newsletters, blogs) into platform-native posts

Usage Patterns

"Draft this week's social posts"

Agent reviews content pillars, recent posts, upcoming content calendar items, and drafts a week of posts across all active platforms. Balances pillars and varies formats (text, thread, question, hot take).

"Log engagement for [post]"

Agent updates the Posted table with metrics. Over time, builds a dataset of what performs.

"What's working on [platform]?"

Agent analyzes recent engagement data:

• Top-performing post types (threads vs. single, questions vs. statements)
• Best posting times (actual data vs. assumptions)
• Which content pillars get most engagement
• Follower growth trends
• Engagement rate calculations

"Repurpose [content] for social"

Agent takes a newsletter, blog post, or long-form piece and creates platform-native versions:

• Twitter/X: Thread (5-8 tweets) + standalone hook tweet
• LinkedIn: Professional narrative version (~200 words)
• Reddit: Discussion-style post with genuine question angle
• Instagram: Carousel concept (slide-by-slide text)

"Social media review"

Weekly performance review format:

## Weekly Social Review — [Date Range]

### Performance Summary
| Platform | Posts | Avg Likes | Avg Comments | Best Post | Worst Post |
|----------|-------|-----------|-------------|-----------|------------|

### What Worked
- [Specific post] got [X] engagement because [analysis]

### What Didn't
- [Specific post] underperformed because [analysis]

### Recommendations
1. Post more [type] on [platform] — consistently 2x avg engagement
2. Reduce [type] — below average 3 weeks running
3. Test [new approach] based on [trend/data]

### Next Week's Focus
- Pillar emphasis: [based on what's working]
- Experiments to run: [one new format/time/topic]

"Batch-create content from [source]"

Agent takes one source (podcast, article, experience) and generates a full content batch:

• 3 tweets (different angles)
• 1 LinkedIn post
• 1 Reddit discussion
• 3 Instagram caption options
• 1 thread outline

"Triage my replies/DMs"

Agent reviews pending social interactions and prioritizes:

• 🔴 Respond now: Potential leads, partnership offers, direct questions
• 🟡 Respond today: Meaningful comments, community engagement
• 🔵 Optional: Generic likes, simple agreements
• ⚫ Skip: Spam, trolls, no-value interactions

Posting Time Optimization

After 2+ weeks of engagement data, agent can analyze optimal times:

## Optimal Posting Times (Based on [X] posts)

### Twitter/X
| Day | Best Time | Avg Engagement | Sample Size |
|-----|-----------|---------------|-------------|
| Mon | 9:15 AM | 47 interactions | 8 posts |
| Tue | 12:30 PM | 62 interactions | 6 posts |
...

### Confidence Level
- High (20+ data points): Mon, Wed, Thu
- Medium (10-19): Tue, Fri
- Low (<10): Sat, Sun — need more data

Content Pillar Rotation

Prevent repetitive content by tracking pillar distribution:

## Pillar Distribution (Last 30 Days)
| Pillar | Posts | % | Target % | Status |
|--------|-------|---|----------|--------|
| Education | 12 | 40% | 35% | ✅ On track |
| Journey | 6 | 20% | 25% | ⚠️ Slightly under |
| Commentary | 8 | 27% | 25% | ✅ On track |
| Community | 4 | 13% | 15% | ⚠️ Slightly under |

Platform-Specific Notes

Twitter/X

• Threads outperform single tweets 2-3x on average
• Hook tweet is everything — spend 50% of effort on first line
• Quote tweets with commentary > plain retweets
• Reply-to-self threads keep engagement in one place

LinkedIn

• First line appears in feed preview — make it a hook
• Personal stories outperform corporate content
• Formatting matters: short paragraphs, line breaks, occasional emoji
• Best engagement: Tue-Thu mornings

Reddit

• Genuine discussion > self-promotion (communities detect and punish shilling)
• Build karma with valuable comments before posting own content
• Match subreddit culture and norms exactly
• Never cross-post the same content to multiple subs simultaneously

Instagram

• Carousel posts get 3x more engagement than single images
• Caption structure: hook → value → CTA → hashtags
• Stories for behind-the-scenes, posts for polished content
• Reels algorithm favors consistent posting cadence

Customization

Multi-Account Management

Track separate accounts (personal + business, multiple brands):

## Accounts
| Account | Platform | Purpose | Voice | Cadence |
|---------|----------|---------|-------|---------|
| @personal | Twitter | Thought leadership | Casual, witty | 3x/week |
| @business | Twitter | Product updates | Professional | 5x/week |
| @personal | LinkedIn | Career/networking | Professional | 2x/week |

Engagement Goals

Set measurable targets:

## Monthly Goals — [Month]
| Platform | Metric | Target | Current | Status |
|----------|--------|--------|---------|--------|
| Twitter | Followers | +200 | +147 | 🟡 73% |
| Twitter | Avg likes/post | 25 | 31 | ✅ 124% |
| LinkedIn | Post impressions | 5,000 | 3,200 | 🟡 64% |

Content Templates

Save reusable post structures:

## Templates
### Hot Take
"Unpopular opinion: [contrarian view]\n\n[3 supporting points]\n\nHere's why this matters: [one sentence]"

### Thread Hook
"I [did X] and learned [Y].\n\nHere are [N] lessons (thread 🧵):"

### Question Post
"Genuine question for [audience]:\n\n[question]\n\nI'll go first: [your answer]"

Integration with Other Skills

Troubleshooting

Privacy & Safety

• Never auto-post. All content is drafted for your review.
• No API credentials stored in state files. Posting happens manually or through your own connected tools.
• Engagement data stays local. Your analytics are in your workspace only.
• No competitor data scraping. Research uses public web search only.

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