• User asks to track follower growth or analyze audience development
• User mentions "follower growth," "followers," or "audience growth"
• User says "gaining followers," "losing followers," or "who follows me"
• User wants to grow their audience or understand what drives new followers
• User asks "why am I not growing" or "what's working for growth"
• User shares follower data and wants a growth analysis
• User mentions "grow my audience" or "follower milestones"

1. get_follower_growth

   — retrieve follower counts over time (use the maximum available window, minimum 30 days)

2. get_metric_timeseries

   — pull follower count as a time series alongside engagement rate to identify correlation patterns

3. list_posts

   — retrieve posts from the same window to correlate content with growth events

4. get_consistency

   — check posting frequency and whether consistency correlates with growth rate shifts

5. get_daily_recap

   — surface any anomaly days (unusual spikes or drops in followers)

"To analyze your audience growth, I need your follower count over time. You can share:

• A screenshot of your analytics dashboard (follower graph)
• Manual data using the template below

Data Collection Template:

Date	Follower Count	Notable Content That Day

The minimum needed for useful analysis: follower counts at weekly intervals for at least 4 weeks, plus a list of posts from the same period.

If you know specific posts that drove follows (e.g., a post blew up), include those too."

• Net new followers = ending count − starting count
• Gross follows vs. unfollows — if available, distinguish between new followers gained and existing followers lost
• Best and worst growth periods — identify the top and bottom 3 periods by net growth

Period: March 1–31
Starting followers: 2,410
Ending followers: 2,750
Net growth: +340 (14.1%)
Daily average: +11.3 followers/day
Best week: March 11–17 (+198 followers)
Worst week: March 25–31 (+22 followers)

• Period growth rate = (new followers / starting followers) × 100
• Trend direction — is the rate accelerating, decelerating, or flat?
• Compounding effect — project forward if the current rate holds (e.g., "at this rate, you reach 5,000 followers in ~8 weeks")

Growth rate: 14.1% this month (vs. 8.3% last month)
Trend: Accelerating — rate nearly doubled month-over-month
Projection: At this rate, you reach 5,000 followers in ~8 weeks

• Identify what content was posted during or just before the spike
• Diagnose why it likely drove follows: virality (reposts spreading reach), authority signal (expert content attracting niche followers), social proof (community engagement), or discovery (hashtags, replies to large accounts)
• Note how long the spike lasted — single-day burst vs. multi-day sustained growth

• Was posting frequency lower? Reduced output often precedes stalls.
• Did content type shift? Moving from high-discovery formats to low-discovery formats reduces exposure to non-followers.
• Was there an unfollow spike? A sudden drop suggests content that disappointed existing followers.
• Platform algorithm change? Note if the stall was broad-based (affects many creators) vs. account-specific.

• High-follow content — posts that consistently generate new followers (typically: educational threads, strong takes, viral storytelling)
• High-engagement but low-follow content — posts that get likes and comments from existing followers without attracting new ones
• Neutral content — posts with no measurable growth signal

• Engagement signals resonance with your existing audience — they already follow you and respond to your content
• Follows signal discovery and first impressions — new people are deciding whether your account is worth tracking

• Spike-driven growth — the account grows in bursts tied to individual breakout posts; flat between spikes. Requires consistently hitting on high-virality content.
• Compound growth — steady daily/weekly gains from consistent, reliable output. Less exciting but more sustainable.

• Connections vs. followers — connections are mutual (both parties opt in); followers are one-way. Track both separately. Most growth strategies target followers, not connections.
• Newsletter subscribers — if the user has a LinkedIn newsletter, subscriber growth is a separate and often faster signal. Include if data is available.
• Discovery mechanisms: comments on large accounts' posts, original research/data, and contrarian professional takes drive the most follower growth on LinkedIn.
• Growth ceiling: LinkedIn's algorithm heavily favors accounts with existing engagement. Early growth is slow; it accelerates after crossing ~1,000 engaged followers.

• Follow-back culture — a meaningful portion of follows on Twitter/X come from follow-back behavior, not content quality. Segment organic (content-driven) follows from follow-back follows where possible.
• Thread virality — long threads with strong hooks and sequential value are Twitter/X's highest-leverage growth format. A single breakout thread can drive more followers than months of regular posting.
• Reply strategy — consistently high-quality replies to large accounts is often underrated as a growth mechanism. Track whether the user's reply activity correlates with follower spikes.

• Early-platform dynamics — Threads is still establishing algorithmic norms. Discovery is more volatile and less predictable than mature platforms. Growth patterns here may not yet be reliable signals.
• Cross-platform carry — many Threads users arrive from Instagram. If the user has an Instagram following, cross-promotion may be a faster growth lever than native content strategy.
• Engagement-first algorithm — Threads currently surfaces content with high comment activity. Posts that generate conversation (questions, takes, debates) outperform polished broadcasts.

• Starter packs — getting included in a relevant starter pack can drive significant follower spikes. These are curated lists shared across the community. Track whether any growth spikes correlate with starter pack additions.
• Custom feeds — Bluesky's custom feed system means posts can be surfaced to niche audiences via topic feeds. Posts that appear in popular feeds drive discovery follows.
• Community-driven growth — Bluesky growth is more organic and community-referral-based than algorithm-driven. Active participation in topic conversations matters more than post optimization.
• Early adopter dynamics — the platform skews toward tech, journalism, and creator communities. Niche authority matters more here than on scale platforms.

• Reference a specific finding (not generic advice)
• Be concrete enough to act on this week
• Be ranked by expected growth impact

1. Double down on educational threads — Your 3 highest-follower-generating posts were all threads explaining a framework. Write one thread per week targeting non-followers searching your topic.
2. Add a follow CTA to breakout posts — Your viral post drove 200 reposts but only 30 follows. End high-reach posts with a direct invitation: "Follow for more on [topic]."
3. Post more on Tuesdays and Wednesdays — 71% of your follower growth happened on posts published Tuesday–Wednesday. Shift your highest-effort content to those days.
4. Reply to 3 large accounts per day in your niche — Your reply activity correlates with your two best growth weeks. Increase reply volume before publishing your next thread.

• Current position — where they are relative to the goal
• Required rate — the daily/weekly growth rate needed to hit the milestone on time
• Current rate — what the data shows they're actually achieving
• Gap analysis — what needs to change to close the gap, stated specifically

• Does not write or draft social media posts — see post-writer-sms for that
• Does not analyze individual post metrics — see performance-analyzer-sms for per-post breakdowns
• Does not identify content patterns across topics or formats — see content-pattern-analyzer-sms for pattern detection
• Does not generate a content strategy or recommendations plan — see optimization-advisor-sms for action plans
• Does not execute code or access external APIs unless BlackTwist MCP is connected
• Does not provide vanity metric comparisons against industry benchmarks — all analysis uses the user's own data as the baseline

• social-media-context-sms — establish niche, platforms, and growth goals before analyzing
• performance-analyzer-sms — analyze post-level metrics (impressions, engagement rate, saves)
• optimization-advisor-sms — translate growth findings into a concrete improvement plan