The Tool Desk
Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →To find Shopify, WordPress, and HubSpot signals in a list of domains, send each site to a technology lookup API, match its returned technologies against your target list, and save the result and its evidence for every input row. Python can automate the CSV handling and API requests. A missing match is not proof that a company does not use a platform: the lookup can only report signals its provider detects under the selected scan mode.
What this workflow can—and cannot—tell you
Technology lookup services inspect observable website signals. Wappalyzer’s detection specification includes HTML, JavaScript variables, response headers, DOM elements, scripts, and metadata as possible evidence (Wappalyzer detection specification). A returned match is evidence that a detector found a signal associated with a technology; it does not prove that the company uses that tool across every page, subdomain, or internal workflow.
For example, a company may use HubSpot internally without exposing a detectable signal on its public site. A match may also be limited to one subdomain or reflect an older configuration. The sources do not establish a universal recall rate for detecting Shopify, WordPress, or HubSpot, so no script can promise to find every actual user.
- Detected: the provider returned a target technology under the scan mode you used.
- No technology returned: the provider returned no target match. Treat this as inconclusive, not as proof the organization does not use it.
- Lookup failed: the request did not complete successfully, so the domain was not evaluated.
- Pending: the provider has started an asynchronous crawl but has not yet returned its technology results.
Choose a lookup service and scan mode
Wappalyzer documents an API for technology lookup and lead-list enrichment. Its default lookup accepts one to ten website URLs per request, with a maximum of ten URLs and a rate limit of ten requests per second. The documented default uses cached data; setting live=true requests a real-time scan. Recursive lookup follows internal links for broader coverage. See the Wappalyzer lookup API documentation and its API overview for current behavior and access details.
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Cached results can be faster to use, but may not reflect the site’s current configuration. Live recursive scans may be more useful when current, broader coverage matters, but they can take longer and use more credits. The API documentation says a crawl can take up to 15 minutes; an initial response may indicate a crawl without listing technologies. Use a callback or check again later rather than marking such a row as a confirmed no-match.
Under the credit scheme described in Wappalyzer’s API documentation, a normal lookup costs one credit per URL. Combining live=true and recursive=true costs five credits per URL. These are documented values, not a guarantee about your account: verify current limits, pricing, and access before budgeting or deploying. Wappalyzer says older verification windows are more likely to include sites that no longer use a technology. Its denoise option excludes low-confidence detections by default; relaxing it can return more results but raises false-positive risk.
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BuiltWith offers different paths for different jobs: its Lists API is for discovering websites by technology, while its Domain API checks supplied domains, supports multi-domain lookups, and has a bulk job flow for larger batches. BuiltWith requires an API key. The documented features do not establish an apples-to-apples accuracy comparison with Wappalyzer, so choose based on input size, workflow, evidence fields, access, and cost rather than an unsupported accuracy ranking.
Design an auditable CSV workflow
- Read the input without losing row identity. Keep the original domain value and a stable row ID. If two input rows contain the same domain, preserve both unless your own matching rules explicitly call for deduplication.
- Normalize carefully. Trim surrounding whitespace, remove a trailing slash, and add a scheme where needed so the provider receives a URL. Do not collapse distinct subdomains into a registrable domain unless that is the matching rule you intend to use.
- Batch within the provider’s limits. Send normalized URLs in supported batches, respecting rate limits. Record the provider and scan mode with each request.
- Match the returned technologies. Compare technology names or provider slugs against Shopify, WordPress, and HubSpot. Keep the full technology list as well as the target labels so the decision can be reviewed later.
- Write an output for every input row. Include a status that distinguishes a detection, no returned technology, request failure, and pending crawl. Save a check timestamp and any provider confirmation timestamp available.
- Review consequential or uncertain rows. Manually inspect stale, ambiguous, or commercially important matches before using them to qualify a lead.
Python’s standard library includes csv for reading and writing CSV files and urllib.request for HTTP requests. See the Python CSV documentation and Python urllib.request documentation. You can use a maintained HTTP library instead, but an API key, provider access, or paid plan may be required; Python itself does not make the lookup service free.
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Example Python implementation
This example uses Wappalyzer’s documented lookup endpoint and its cached default mode. Set WAPPALYZER_API_KEY in your environment and confirm the current endpoint, authentication format, and account limits in the provider documentation before running it. The code preserves one output row for each input row and marks errors instead of treating them as no-matches.
import csv
import json
import os
import time
from datetime import datetime, timezone
from urllib.error import HTTPError, URLError
from urllib.parse import urlsplit, urlunsplit
from urllib.request import Request, urlopen
API_KEY = os.environ["WAPPALYZER_API_KEY"]
ENDPOINT = "https://api.wappalyzer.com/v2/lookup/"
TARGETS = {"shopify", "wordpress", "hubspot"}
def normalize_domain(value):
original = value.strip()
if not original:
return ""
candidate = original if "://" in original else "https://" + original
parts = urlsplit(candidate)
return urlunsplit((parts.scheme or "https", parts.netloc, parts.path.rstrip("/"), parts.query, ""))
def chunks(items, size=10):
for start in range(0, len(items), size):
yield items[start:start + size]
with open("leads.csv", newline="", encoding="utf-8") as source:
leads = list(csv.DictReader(source))
results = []
for row_number, lead in enumerate(leads, start=1):
results.append({
"row_id": lead.get("id") or str(row_number),
"original_domain": lead.get("domain", ""),
"normalized_url": normalize_domain(lead.get("domain", "")),
"provider": "Wappalyzer",
"scan_mode": "cached default",
"detected_technologies": "",
"target_labels": "",
"status": "not checked",
"checked_at_utc": "",
"provider_confirmed_at": "",
"error_or_pending": "",
"raw_response": "",
})
# Keep a result index per normalized URL so duplicate input rows remain intact.
valid = [(i, r["normalized_url"]) for i, r in enumerate(results) if r["normalized_url"]]
for batch in chunks(valid):
indices, urls = zip(*batch)
checked_at = datetime.now(timezone.utc).isoformat()
body = json.dumps({"urls": list(urls)}).encode("utf-8")
request = Request(
ENDPOINT,
data=body,
headers={
"Content-Type": "application/json",
"x-api-key": API_KEY,
},
method="POST",
)
try:
with urlopen(request, timeout=60) as response:
payload = json.loads(response.read().decode("utf-8"))
# Provider response fields can vary; inspect the current API schema and
# adjust this mapping before relying on it in production.
by_url = {item.get("url", ""): item for item in payload}
for index, url in zip(indices, urls):
item = by_url.get(url)
if item is None:
results[index]["status"] = "no technology returned"
results[index]["checked_at_utc"] = checked_at
continue
technologies = item.get("technologies", [])
names = [t.get("name", "") for t in technologies if t.get("name")]
slugs = [t.get("slug", "") for t in technologies if t.get("slug")]
labels = sorted({name for name in names if name.casefold() in TARGETS})
labels.extend(slug for slug in slugs if slug.casefold() in TARGETS and slug not in labels)
results[index].update({
"detected_technologies": "; ".join(names),
"target_labels": "; ".join(labels),
"status": "detected" if labels else "no technology returned",
"checked_at_utc": checked_at,
"provider_confirmed_at": str(item.get("verifiedAt", "")),
"raw_response": json.dumps(item, ensure_ascii=False),
})
except HTTPError as exc:
message = f"HTTP {exc.code}: {exc.reason}"
for index in indices:
results[index].update({"status": "lookup failed", "checked_at_utc": checked_at, "error_or_pending": message})
except (URLError, TimeoutError, json.JSONDecodeError) as exc:
message = str(exc)
for index in indices:
results[index].update({"status": "lookup failed", "checked_at_utc": checked_at, "error_or_pending": message})
# Avoid running at the documented maximum request rate.
time.sleep(0.11)
for result in results:
if not result["normalized_url"]:
result["status"] = "lookup failed"
result["error_or_pending"] = "Empty domain value"
with open("enriched_leads.csv", "w", newline="", encoding="utf-8") as output:
fields = list(results[0]) if results else ["row_id", "original_domain", "normalized_url", "status"]
writer = csv.DictWriter(output, fieldnames=fields)
writer.writeheader()
writer.writerows(results)
Before using this as a production integration, validate the provider’s current response schema against its documentation. In particular, confirm how it identifies each URL, names its technology and verification-time fields, and represents crawl-pending results. The example’s response mapping is intentionally isolated so it can be adapted without changing the row-preservation and status logic.
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Store enough evidence to audit each label
Keep the provider’s raw response or a stable reference to it where the provider’s terms permit. A useful output should include the original lead value, normalized URL, provider, scan mode, full detected-technology list, target labels, check time, provider confirmation time if returned, lookup status, and any error or pending information. This is a workflow recommendation, not a schema mandated by either vendor.
Keeping both the full response evidence and the target label helps explain how a row was classified and makes later rechecks possible. Avoid overwriting an old result without retaining its timestamp: a detection is tied to the time and mode of the lookup, not a permanent property of a company.
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