competitive-intel

name	competitive-intel
version	"2026-02-24T00:00:00.000Z"
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, ensure Xpoz access is configured. Follow these checks in order.

Check 1: Already authenticated?

If you have MCP tools, try calling any Xpoz tool (e.g., checkAccessKeyStatus). If it works → skip to Step 1.

If you have the SDK, try:

from xpoz import XpozClient
client = XpozClient()  # reads XPOZ_API_KEY env var

If this succeeds without error → skip to Step 1.

If neither works, you need to authenticate. Choose the path that fits your environment:

---

Path A: MCP via mcporter (OpenClaw agents)

If mcporter is available:

mcporter call xpoz.checkAccessKeyStatus

If hasAccessKey: true → ready. If not:

mcporter config add xpoz https://mcp.xpoz.ai/mcp --auth oauth

Then authenticate — generate the OAuth URL and send it to the user:

Step 1: Generate authorization URL

import secrets, hashlib, base64, urllib.parse, json, urllib.request, os

verifier = secrets.token_urlsafe(64)
challenge = base64.urlsafe_b64encode(hashlib.sha256(verifier.encode()).digest()).rstrip(b'=').decode()
state = secrets.token_urlsafe(32)

# Dynamic client registration
reg_req = urllib.request.Request(
    'https://mcp.xpoz.ai/oauth/register',
    data=json.dumps({
        'client_name': 'Agent Skills',
        'redirect_uris': ['https://www.xpoz.ai/oauth/openclaw'],
        'grant_types': ['authorization_code'],
        'response_types': ['code'],
        'token_endpoint_auth_method': 'none',
    }).encode(),
    headers={'Content-Type': 'application/json'},
)
reg_resp = json.loads(urllib.request.urlopen(reg_req).read())

params = urllib.parse.urlencode({
    'response_type': 'code',
    'client_id': reg_resp['client_id'],
    'code_challenge': challenge,
    'code_challenge_method': 'S256',
    'redirect_uri': 'https://www.xpoz.ai/oauth/openclaw',
    'state': state,
    'scope': 'mcp:tools',
    'resource': 'https://mcp.xpoz.ai/',
})

auth_url = 'https://mcp.xpoz.ai/oauth/authorize?' + params

# Save state for token exchange
os.makedirs(os.path.expanduser('~/.cache/xpoz-oauth'), exist_ok=True)
with open(os.path.expanduser('~/.cache/xpoz-oauth/state.json'), 'w') as f:
    json.dump({'verifier': verifier, 'state': state, 'client_id': reg_resp['client_id'],
               'redirect_uri': 'https://www.xpoz.ai/oauth/openclaw'}, f)

print(auth_url)

Step 2: Send the URL to the user

Tell them:

"I need to connect to Xpoz for social media data. Please open this link and sign in:

[auth_url]

After authorizing, you'll see a code. Paste it back to me here."

Step 3: WAIT for the user to reply with the code. Do not proceed until they respond.

Step 4: Exchange the code for a token

Once the user provides the code (either a raw code or a URL containing ?code=...), extract the code and exchange it:

import json, urllib.request, urllib.parse, subprocess, os

with open(os.path.expanduser('~/.cache/xpoz-oauth/state.json')) as f:
    oauth = json.load(f)

code = "THE_CODE_FROM_USER"  # Extract from user's reply

data = urllib.parse.urlencode({
    'grant_type': 'authorization_code',
    'code': code,
    'redirect_uri': oauth['redirect_uri'],
    'client_id': oauth['client_id'],
    'code_verifier': oauth['verifier'],
}).encode()

req = urllib.request.Request(
    'https://mcp.xpoz.ai/oauth/token',
    data=data,
    headers={'Content-Type': 'application/x-www-form-urlencoded'},
)
resp = json.loads(urllib.request.urlopen(req).read())
token = resp['access_token']

# Configure mcporter with the token (token is never printed)
subprocess.run(['mcporter', 'config', 'remove', 'xpoz'], capture_output=True)
subprocess.run(['mcporter', 'config', 'add', 'xpoz', 'https://mcp.xpoz.ai/mcp',
                '--header', f'Authorization=Bearer {token}'], check=True)

# Clean up
os.remove(os.path.expanduser('~/.cache/xpoz-oauth/state.json'))
print("Xpoz configured successfully")

Step 5: Verify with mcporter call xpoz.checkAccessKeyStatus → should return hasAccessKey: true.

---

Path B: MCP via Claude Code config

For Claude Code users without mcporter, add to ~/.claude.json:

{
  "mcpServers": {
    "xpoz": {
      "url": "https://mcp.xpoz.ai/mcp",
      "transport": "http-stream"
    }
  }
}

Claude Code handles OAuth automatically on first tool call — the user just needs to authorize in their browser when prompted.

---

Path C: SDK (Python or TypeScript)

Ask the user:

"I need a Xpoz API key to access social media data. Please go to https://xpoz.ai/get-token (it's free, no credit card needed) and paste the key back to me."

WAIT for the user to reply with the key. Then:

Python:

pip install xpoz

from xpoz import XpozClient
client = XpozClient("THE_KEY_FROM_USER")

TypeScript:

npm install @xpoz/xpoz

import { XpozClient } from "@xpoz/xpoz";
const client = new XpozClient({ apiKey: "THE_KEY_FROM_USER" });
await client.connect();

Or set the environment variable and use the default constructor:

export XPOZ_API_KEY=THE_KEY_FROM_USER

---

Auth Errors

Problem	Solution
MCP: "Unauthorized"	Re-run the OAuth flow above
SDK: AuthenticationError	Verify key at xpoz.ai/settings
Token exchange fails	Ask user to re-authorize — codes are single-use

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

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

import { XpozClient } from "@xpoz/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
• For large comparisons (5+ brands), use CSV exports and analyze locally with pandas/Excel