serpapi-core-workflow-a
Google Search scraping with SerpApi -- organic results, knowledge graph, answer boxes. Use when building search-powered features, SEO monitoring, or extracting structured data from Google results. Trigger: "serpapi google search", "scrape google", "serpapi organic results".
Best use case
serpapi-core-workflow-a is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Google Search scraping with SerpApi -- organic results, knowledge graph, answer boxes. Use when building search-powered features, SEO monitoring, or extracting structured data from Google results. Trigger: "serpapi google search", "scrape google", "serpapi organic results".
Teams using serpapi-core-workflow-a should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/serpapi-core-workflow-a/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How serpapi-core-workflow-a Compares
| Feature / Agent | serpapi-core-workflow-a | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Google Search scraping with SerpApi -- organic results, knowledge graph, answer boxes. Use when building search-powered features, SEO monitoring, or extracting structured data from Google results. Trigger: "serpapi google search", "scrape google", "serpapi organic results".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# SerpApi Core Workflow A: Google Search
## Overview
Extract structured data from Google Search: organic results, answer boxes, knowledge graph, related questions (PAA), local pack, ads, and shopping results. Each search costs 1 API credit.
## Instructions
### Step 1: Full Google Search with All Components
```python
import serpapi, os
client = serpapi.Client(api_key=os.environ["SERPAPI_API_KEY"])
result = client.search(
engine="google",
q="best project management tools",
location="New York, New York",
hl="en", gl="us",
num=10,
)
# 1. Organic Results
for r in result.get("organic_results", []):
print(f"{r['position']}. {r['title']}")
print(f" URL: {r['link']}")
print(f" Snippet: {r.get('snippet', 'N/A')}")
# Rich snippets: sitelinks, rating, date
if "rich_snippet" in r:
print(f" Rating: {r['rich_snippet'].get('top', {}).get('rating')}")
# 2. Answer Box
if ab := result.get("answer_box"):
print(f"\nAnswer Box ({ab.get('type', 'unknown')}):")
print(f" {ab.get('answer') or ab.get('snippet') or ab.get('title')}")
# 3. Knowledge Graph
if kg := result.get("knowledge_graph"):
print(f"\nKnowledge Graph: {kg['title']}")
print(f" Type: {kg.get('type')}")
print(f" Description: {kg.get('description', 'N/A')[:100]}")
# 4. People Also Ask
for paa in result.get("related_questions", []):
print(f"\nPAA: {paa['question']}")
print(f" Answer: {paa.get('snippet', 'N/A')[:100]}")
# 5. Related Searches
for rs in result.get("related_searches", []):
print(f"Related: {rs['query']}")
```
### Step 2: Paginate Through Results
```python
def paginate_google(query: str, pages: int = 3, num: int = 10):
"""Get multiple pages of results (each page = 1 credit)."""
all_results = []
for page in range(pages):
result = client.search(
engine="google", q=query, num=num,
start=page * num, # Offset parameter
)
organic = result.get("organic_results", [])
if not organic:
break
all_results.extend(organic)
return all_results
results = paginate_google("python web frameworks", pages=3)
print(f"Total results: {len(results)}")
```
### Step 3: Google with Filters
```python
# Time-based filtering
recent = client.search(engine="google", q="AI news", tbs="qdr:w") # Past week
# tbs options: qdr:h (hour), qdr:d (day), qdr:w (week), qdr:m (month), qdr:y (year)
# Device-specific results
mobile = client.search(engine="google", q="restaurants near me", device="mobile")
# Safe search
safe = client.search(engine="google", q="query", safe="active")
```
### Step 4: Extract Local Pack Results
```python
result = client.search(engine="google", q="coffee shops austin tx")
for place in result.get("local_results", {}).get("places", []):
print(f"{place['title']} - {place.get('rating', 'N/A')} stars")
print(f" Address: {place.get('address')}")
print(f" Hours: {place.get('hours')}")
print(f" GPS: {place.get('gps_coordinates', {})}")
```
## Output
```
1. Monday.com - Best Project Management Software
URL: https://monday.com
Snippet: Rated #1 project management tool...
Answer Box (organic_result):
Compare the best project management tools...
Knowledge Graph: Project management
Type: Topic
Description: Project management is the application of...
```
## Error Handling
| Error | Cause | Solution |
|-------|-------|----------|
| No `organic_results` | CAPTCHA or unusual query | Check `search_metadata.status` |
| Empty `local_results` | Query not location-specific | Add `location` parameter |
| `search_metadata.status: Error` | Invalid parameters | Check `search_metadata.error` message |
## Resources
- [Google Search API](https://serpapi.com/search-api)
- [Google Search Parameters](https://serpapi.com/search-api#api-parameters)
- [Organic Results](https://serpapi.com/organic-results)
## Next Steps
For Bing, YouTube, and other engines, see `serpapi-core-workflow-b`.Related Skills
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