The Problem
SEO content requires current competitor data, verified pricing, and real-time SERP analysis. LLMs without search grounding produce stale or fabricated information. Manual fact-checking every claim in every article does not scale.
The Scavio Solution
Build Claude Code skills that combine MCP search tools with content generation. The skill searches for current data, verifies claims against live results, and generates content grounded in real information. Each article costs $0.025-0.05 in search API calls.
Before
Before automation, a content team produced 5 SEO articles per week. Each required 1-2 hours of manual research to verify competitor pricing, feature lists, and integration details. Output: 20 articles/month.
After
After building a Claude Code SEO skill, the team generates 20-25 articles per session. Each article includes 5 verified search calls. API cost per batch: $0.50-1.25. Monthly output increased from 20 to 100+ articles with higher accuracy.
Who It Is For
SEO professionals, content teams, and technical writers who use Claude Code for content production and need real-time data grounding.
Key Benefits
- MCP search integration grounds content in live data
- 5 searches per article at $0.025 total
- Every pricing claim verified against current web data
- 10-25 articles per generation session
- Reproducible skill runs produce consistent quality
Python Example
import requests, os
H = {'x-api-key': os.environ['SCAVIO_API_KEY']}
def research_topic(keyword):
searches = [
{'platform': 'google', 'query': f'{keyword} pricing 2026'},
{'platform': 'google', 'query': f'{keyword} alternatives comparison'},
{'platform': 'reddit', 'query': keyword},
]
context = {}
for s in searches:
r = requests.post('https://api.scavio.dev/api/v1/search',
headers=H, json=s, timeout=10).json()
context[s['query']] = [{'title': r.get('title', ''),
'snippet': r.get('snippet', '')}
for r in r.get('organic_results', [])[:3]]
return context
data = research_topic('SERP API')
print(f'Research complete: {sum(len(v) for v in data.values())} sources')JavaScript Example
const H = {'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json'};
async function researchTopic(keyword) {
const queries = [
{platform: 'google', query: `${keyword} pricing 2026`},
{platform: 'google', query: `${keyword} alternatives`},
{platform: 'reddit', query: keyword},
];
const ctx = {};
for (const q of queries) {
const r = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST', headers: H, body: JSON.stringify(q)
}).then(r => r.json());
ctx[q.query] = (r.organic_results || []).slice(0, 3).map(r => ({title: r.title, snippet: r.snippet}));
}
return ctx;
}
researchTopic('SERP API').then(c => console.log(Object.keys(c).length, 'queries researched'));Platforms Used
Web search with knowledge graph, PAA, and AI overviews
Community, posts & threaded comments from any subreddit
YouTube
Video search with transcripts and metadata