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---
name: competitive-intel
version: 2026-02-23
description: Compare brands and products across social media — share of voice, sentiment, positioning, and audience overlap using Xpoz. Use when asked to "compare brands", "competitive analysis", "share of voice", "brand vs brand", or "competitive intelligence".
---
# Competitive Intelligence
## Overview
Compare multiple brands or products side by side across Twitter/X, Reddit, and Instagram. Measure share of voice, compare sentiment, identify positioning differences, and discover competitive advantages from real social conversations.
## When to Use
Activate when the user asks:
- "Compare [BRAND A] vs [BRAND B] on social media"
- "Share of voice: [BRAND] vs competitors"
- "Competitive analysis for [PRODUCT]"
- "How does [BRAND A] sentiment compare to [BRAND B]?"
- "What are people saying about [BRAND] vs [COMPETITOR]?"
## Setup & Authentication
Before fetching data, verify Xpoz access is configured. Try them in order:
### Check 1: MCP Server (preferred)
If you have MCP tool access, check if `xpoz` tools are available (e.g., `getTwitterPostsByKeywords`). If yes, authentication is already handled — skip to Step 1.
### Check 2: SDK via Environment Variable
```bash
echo $XPOZ_API_KEY
```
If set, the SDK will authenticate automatically:
```python
from xpoz import XpozClient
client = XpozClient() # auto-reads XPOZ_API_KEY
```
### Check 3: Nothing Configured
If neither MCP nor env var is available, ask the user to:
1. Get a free API key at [xpoz.ai/get-token](https://xpoz.ai/get-token?utm_source=github&utm_medium=agent-skills&utm_campaign=competitive-intel) (100K results/month free)
2. Then EITHER:
- **Set env var:** `export XPOZ_API_KEY=<their-key>` and use the SDK
- **Configure MCP:** Add to `~/.claude.json` (or agent's MCP config):
```json
{
"mcpServers": {
"xpoz": {
"url": "https://mcp.xpoz.ai/mcp",
"transport": "http-stream",
"headers": { "Authorization": "Bearer <their-key>" }
}
}
}
```
- **Pass directly:** `XpozClient("<their-key>")` in Python or `new XpozClient({ apiKey: "<their-key>" })` in TypeScript
### Auth Errors
If you get `AuthenticationError` or 401 responses:
- Verify the API key is valid at [xpoz.ai/settings](https://xpoz.ai/settings)
- Check the key hasn't expired
- Ensure no extra whitespace in the key
## Step-by-Step Instructions
### Step 1: Parse the Request
Extract:
- **Primary brand** and **competitors** (2-5 brands total)
- **Platforms** (default: Twitter + Reddit)
- **Time period** (default: last 7 days)
- **Industry context** for better analysis
Build expanded queries for each brand:
- `"Slack"` → `"Slack" NOT "cut some slack" NOT "slack off"`
- `"Discord"` → `"Discord" NOT "sow discord" NOT "discord between"`
- For stocks: include ticker symbols
### Step 2: Fetch Data for Each Brand
Run parallel searches — one per brand, per platform.
#### Via MCP
For each brand, call:
**Twitter posts:**
```
Call getTwitterPostsByKeywords:
query: "<brand query>"
fields: ["id", "text", "authorUsername", "createdAtDate", "likeCount", "retweetCount", "impressionCount"]
startDate: "<7 days ago>"
endDate: "<today>"
language: "en"
```
**Twitter users discussing the brand:**
```
Call getTwitterUsersByKeywords:
query: "<brand query>"
fields: ["id", "username", "name", "followersCount", "relevantTweetsCount", "relevantTweetsLikesSum"]
startDate: "<7 days ago>"
```
**Reddit (for each brand):**
```
Call getRedditPostsByKeywords:
query: "<brand query>"
fields: ["id", "title", "text", "score", "numComments", "subreddit", "createdAtDate"]
startDate: "<7 days ago>"
```
**CRITICAL:** Each call returns an `operationId` — poll `checkOperationStatus` until "completed".
**Tip:** Launch all brand searches in sequence, collect all operationIds, then poll them. This is faster than waiting for each one.
#### Via Python SDK
```python
from xpoz import XpozClient
client = XpozClient()
brands = {
"Slack": '"Slack" NOT "cut some slack"',
"Discord": '"Discord" NOT "sow discord"',
"Teams": '"Microsoft Teams" OR "MS Teams"',
}
brand_data = {}
for brand_name, query in brands.items():
# Twitter posts
twitter = client.twitter.search_posts(
query,
start_date="2026-02-16",
end_date="2026-02-23",
language="en",
fields=["id", "text", "author_username", "like_count", "retweet_count", "impression_count", "created_at_date"]
)
# Twitter users (for influencer overlap analysis)
users = client.twitter.get_users_by_keywords(
query,
start_date="2026-02-16",
fields=["username", "followers_count", "relevant_tweets_count", "relevant_tweets_likes_sum"]
)
# Reddit posts
reddit = client.reddit.search_posts(
query,
start_date="2026-02-16",
fields=["id", "title", "text", "score", "num_comments", "subreddit", "created_at_date"]
)
brand_data[brand_name] = {
"twitter_posts": twitter,
"twitter_users": users,
"reddit_posts": reddit,
"tweet_count": twitter.pagination.total_rows,
"reddit_count": reddit.pagination.total_rows,
}
client.close()
```
#### Via TypeScript SDK
```typescript
import { XpozClient } from "xpoz";
const client = new XpozClient();
await client.connect();
const brands: Record<string, string> = {
Slack: '"Slack" NOT "cut some slack"',
Discord: '"Discord" NOT "sow discord"',
Teams: '"Microsoft Teams" OR "MS Teams"',
};
const brandData: Record<string, any> = {};
for (const [name, query] of Object.entries(brands)) {
const twitter = await client.twitter.searchPosts(query, {
startDate: "2026-02-16",
endDate: "2026-02-23",
language: "en",
fields: ["id", "text", "authorUsername", "likeCount", "retweetCount", "createdAtDate"],
});
const reddit = await client.reddit.searchPosts(query, {
startDate: "2026-02-16",
fields: ["id", "title", "text", "score", "numComments", "subreddit"],
});
brandData[name] = { twitter, reddit };
}
await client.close();
```
### Step 3: Analyze and Compare
**Share of Voice (SOV):**
```
SOV for Brand A = (Brand A mentions) / (Total mentions across all brands) × 100
```
Calculate separately for Twitter and Reddit.
**Sentiment Comparison:**
For each brand, classify posts into positive/neutral/negative (see social-sentiment-analyzer skill for classification method) and compare:
- Overall sentiment score (0-100)
- Positive/negative ratio
- Sentiment trend over the time period
**Engagement Comparison:**
- Average likes per post
- Average comments/replies per post
- Total impressions (Twitter)
- Total Reddit score
**Audience Overlap:**
- Find users who posted about multiple brands (common usernames across datasets)
- These users are particularly valuable for understanding switching behavior
**Positioning Analysis:**
- What attributes does each brand's audience associate with it?
- What are the unique strengths/weaknesses mentioned for each?
- Common comparison contexts ("I switched from X to Y because...")
### Step 4: Generate Report
```
## Competitive Intelligence: [BRAND] vs Competitors
**Period:** [date range] | **Platforms:** Twitter, Reddit
### Share of Voice
| Brand | Twitter Posts | Reddit Posts | Total | SOV |
|-------|-------------|-------------|-------|-----|
| Slack | 1,234 | 456 | 1,690 | 42% |
| Discord | 890 | 678 | 1,568 | 39% |
| Teams | 456 | 321 | 777 | 19% |
### Sentiment Comparison
| Brand | Score | Positive | Neutral | Negative | Trend |
|-------|-------|----------|---------|----------|-------|
| Slack | 62 | 38% | 42% | 20% | → Stable |
| Discord | 71 | 48% | 35% | 17% | ↑ Improving |
| Teams | 45 | 22% | 45% | 33% | ↓ Declining |
### Engagement Comparison
| Brand | Avg Likes (Twitter) | Avg Score (Reddit) | Avg Comments |
|-------|--------------------|--------------------|--------------|
| ... | ... | ... | ... |
### Key Findings
#### [Brand A] Strengths
- [What people praise, with example quotes]
#### [Brand A] Weaknesses
- [What people complain about, with example quotes]
#### [Brand B] Strengths / Weaknesses
...
### Competitive Positioning Map
- **[Brand A]:** Positioned as [description]
- **[Brand B]:** Positioned as [description]
- **Switching signals:** [users switching from X to Y, with reasons]
### Audience Overlap
[X users posted about multiple brands — analysis of their preferences]
### Recommendations
[3-5 actionable insights based on the competitive landscape]
```
## Example Prompts
- "Compare Tesla vs Rivian vs Lucid on Twitter sentiment"
- "Share of voice: Figma vs Sketch vs Adobe XD"
- "Competitive analysis for Notion vs Obsidian vs Roam Research on Reddit"
- "How does Claude sentiment compare to ChatGPT and Gemini?"
## Notes
- Expand brand names carefully to avoid false positives (common words need exclusions)
- Reddit provides qualitative depth; Twitter provides quantitative breadth
- Free tier: 100K results/month at [xpoz.ai](https://xpoz.ai?utm_source=github&utm_medium=agent-skills&utm_campaign=competitive-intel)
- For large comparisons (5+ brands), use CSV exports and analyze locally with pandas/Excel