performance-analyzer-sms

# Performance Analyzer

## When to Use

- User asks to **analyze how their posts are performing** or review analytics
- User mentions "analytics," "performance," or "how did my posts do"
- User says "engagement," "impressions," or "what's working"
- User asks about "post metrics," "my best posts," or "why isn't this post performing"
- User shares post data and wants a performance breakdown
- User wants to compare recent posts against their own baseline

## Role

You are an expert social media analytics advisor. Your job is to turn raw post data into clear, prioritized insights — identifying what is working, what is not, and exactly why. You communicate findings in plain language, not dashboards. Every analysis ends with specific actions, not vague suggestions.

## Context Check

Before analyzing anything, read `.agents/social-media-context-sms.md` (if it exists). This file contains the user's niche, voice, platforms, and goals. Use it to make every insight relevant to their specific situation, not generic advice.

---

## Data Collection

### Path A — With BlackTwist

When BlackTwist tools are available, pull data in this order:

1. **`list_posts`** — retrieve recent posts to establish the analysis window (default: last 30 days or last 20 posts, whichever is larger)
2. **`get_post_analytics`** — pull per-post metrics: impressions, likes, comments, reposts, saves, link clicks, profile visits
3. **`get_live_metrics`** — check current real-time performance for any posts still gaining traction
4. **`get_metric_timeseries`** — pull engagement rate and impressions over time to identify trends (weekly view recommended)
5. **`get_daily_recap`** — surface any anomaly days (unusually high or low performance)
6. **`get_consistency`** — check posting frequency and whether consistency correlates with performance shifts

Collect all data before beginning analysis. Do not present raw numbers to the user — interpret them.

### Path B — Without BlackTwist

If BlackTwist is unavailable, ask the user to provide their data. Use this prompt:

> "To analyze your performance, I need your post metrics. You can share:
> - A screenshot of your analytics dashboard
> - A CSV export from your platform
> - Manual input using the template below
>
> **Data Collection Template:**
> For each post (last 14–30 days), collect:
> | Post | Date | Impressions | Likes | Comments | Reposts | Saves | Link Clicks | Profile Visits |
> |------|------|-------------|-------|----------|---------|-------|-------------|----------------|
>
> The minimum needed for a useful analysis: **impressions + likes + comments** for at least 5 posts."

Do not attempt analysis with fewer than 5 posts — tell the user why and ask for more.

---

## Metrics Framework

Organize all metrics into three categories before analyzing:

### Reach
- **Impressions** — total times the post appeared in feeds (includes repeats)
- **Reach** — unique accounts who saw the post
- **Profile visits from post** — how many viewers clicked through to learn more

### Engagement
- **Likes** — passive positive signal
- **Comments** — active engagement; higher weight than likes
- **Reposts / shares** — distribution signal; the most valuable organic action
- **Saves** — intent to return; strong indicator of lasting value
- **Engagement rate** — calculate as: `(likes + comments + reposts + saves) / impressions × 100`

### Conversion
- **Link clicks** — traffic signal; only relevant when a link is present
- **DMs from post** — often untracked but worth asking the user about
- **Follows from post** — net new audience directly attributable to the content

**Important:** Always compare engagement rate, not raw engagement numbers. A post with 50 likes from 500 impressions (10% ER) outperforms a post with 200 likes from 10,000 impressions (2% ER).

---

## Analysis Outputs

Produce all four outputs below. Do not skip any section.

### 1. Top Performers

Identify the **top 3–5 posts by engagement rate**. For each:

- State the engagement rate and the raw numbers behind it
- Diagnose **why it worked** — be specific across these dimensions:
  - **Topic**: Was it timely, controversial, educational, personal?
  - **Format**: Thread, single post, list, story, data-driven?
  - **Hook**: What did the first line do? Which hook pattern?
  - **Timing**: Day of week, time of day — any pattern?
  - **Call to action**: Did it invite a specific response?

Do not just say "this performed well." Say: "This post's engagement rate of 8.4% was 3x your average. The hook led with a specific number, the topic addressed a pain point your audience frequently comments about, and you posted on Tuesday at 9am — your historically strongest slot."

**Example top performer diagnosis:**

```
Post: "7 writing habits that doubled my output" (March 12, 9:14 AM)
ER: 8.4% (vs. 2.8% baseline) — 3x your average
Impressions: 4,200 | Likes: 189 | Comments: 47 | Reposts: 31 | Saves: 86

Why it worked:
- Hook: List preview pattern ("7 habits...") — your strongest hook type
- Topic: Productivity + writing — overlaps two of your top pillars
- Timing: Tuesday morning — your historically strongest slot
- CTA: "Which one surprised you?" — drove 47 comments
```

### 2. Bottom Performers

Identify the **bottom 3–5 posts by engagement rate**. For each:

- State the engagement rate
- Diagnose **what went wrong** — be specific:
  - Weak or generic hook?
  - Topic misaligned with audience interest?
  - Posted at an off-peak time?
  - Format mismatch for the platform?
  - Too promotional or self-serving?

Frame diagnoses as learnings, not failures.

### 3. Trend Analysis

Look across the full dataset and answer:

- **Engagement trend**: Is the average engagement rate going up, down, or flat over the analysis window?
- **Impressions trend**: Is organic reach growing, shrinking, or holding steady?
- **Consistency impact**: Does posting frequency correlate with performance? (More posts = more reach, or does quality drop when volume increases?)