Acquisition Target Screener

Acquisition target screening tool that runs 16 data sources in parallel to produce a 0-100 Acquisition Readiness Score for any company. Built for M&A analysts, corporate development teams, and private equity firms who need a data-driven first pass before committing to expensive due diligence engagements.

Enter a company name and get back a scored, graded profile in minutes — covering financial health, IP portfolio strength, workforce stability, technology maturity, and market position, with automatic deal breaker detection built in.

What data can you extract?

Why use Acquisition Target Screener?

Manual M&A target research is expensive and slow. A junior analyst can spend 2-3 days pulling SEC filings, checking patent databases, scanning LinkedIn for hiring signals, and reading customer reviews — before anyone even knows if the target is worth pursuing. Engaging a third-party due diligence firm before you have conviction runs $25,000-$100,000 per engagement.

This actor automates that entire first-pass process. In one run, it queries 16 data sources in parallel, scores the company across five dimensions proven to predict acquisition value, and surfaces deal breakers that should stop a deal before it starts.

• Scheduling — run quarterly scans on a watchlist of targets to track how their readiness scores change over time
• API access — trigger runs from Python, JavaScript, or any HTTP client and pipe results directly into your deal management system
• Proxy rotation — built-in Apify proxy infrastructure handles rate limits across all 16 data sources automatically
• Monitoring — configure Slack or email alerts when a target's score changes significantly between runs
• Integrations — push results to Zapier, Make, HubSpot, Google Sheets, or any webhook endpoint for deal pipeline automation

Features

• 16 sub-actors run in parallel — all data collection happens simultaneously, so total run time is determined by the slowest source, not the sum of all 16
• 5-dimension weighted scoring model — Financial Health (25pts), IP Portfolio (20pts), Workforce Stability (20pts), Technology Maturity (20pts), Market Position (15pts) — each dimension has explicit sub-scores with plain-language findings
• Composite Acquisition Readiness Score (0-100) — single number that ranks any company on a consistent scale, enabling objective comparison across a target list
• Five grade tiers — PRIME TARGET (80+), STRONG TARGET (60-79), MODERATE TARGET (40-59), WEAK TARGET (20-39), NOT RECOMMENDED (0-19)
• Automatic deal breaker detection — three hard-coded conditions trigger a caution flag: massive insider selling with zero buys, more than 200 CFPB consumer complaints, and zero IP protection despite an active technology stack
• Insider trading sentiment analysis — compares buy vs. sell transaction counts from SEC Form 4 filings to produce a POSITIVE, NEUTRAL, or NEGATIVE signal
• Tech debt detection — scans the target's tech stack for outdated frameworks (jQuery, AngularJS, Backbone, CoffeeScript, Flash, Silverlight) and flags them explicitly in findings
• Modern technology scoring — rewards adoption of React, Vue, Next.js, TypeScript, Kubernetes, Docker, GraphQL, and Terraform with up to 5 additional points
• R&D intensity scoring — calculates the ratio of engineering and research job postings to total postings; roles exceeding 40% R&D add 4 points to Workforce Stability
• Workforce role diversity scoring — detects hiring across engineering, sales, marketing, product, and operations from job posting titles; 4+ departments earn 6 additional points
• IP geographic coverage — awards bonus points when the target has both US (USPTO) and EU (EPO/EUIPO) IP protection, indicating international defensibility
• Review aggregation — combines Trustpilot ratings and multi-platform review data into a single average rating with a STRONG/ACCEPTABLE/POOR sentiment classification
• SERP visibility scoring — counts top-10 search engine rankings as a signal of organic market presence and brand authority
• Shopify presence detection — flags e-commerce operations as a positive market position signal for consumer-facing acquisition targets
• Full raw data export — all 16 sub-actor datasets are included in the output under rawData for teams who want to run their own analysis beyond the composite score

Use cases for acquisition target screening

M&A first-pass screening

Analysts at investment banks and corporate development teams run this actor before presenting a target to an investment committee. Instead of spending two days on manual research, they get a scored, graded profile in under 5 minutes. The Acquisition Readiness Score gives deal leads a data-backed number to defend their target recommendation — or explain why they passed.

Private equity add-on evaluation

PE firms maintaining a platform company evaluate dozens of potential add-on acquisitions per quarter. Running each candidate through the screener produces comparable scores that make side-by-side ranking straightforward. The IP Portfolio and Technology Maturity dimensions are particularly valuable for identifying assets that justify a premium multiple.

Corporate development watchlist monitoring

Corporate development teams at mid-to-large companies maintain lists of 20-50 potential targets and check in quarterly. Scheduling this actor to run monthly on each target tracks how scores evolve — a rapidly improving Workforce Stability score, for example, may signal an accelerating growth phase and a narrowing acquisition window.

Investment banking pitch book research

Bankers preparing sell-side pitch books use the actor to generate objective data on comparable companies. The structured JSON output populates patent counts, hiring signals, and review ratings into pitch materials without manual database searches across USPTO, SEC EDGAR, and Glassdoor.

Venture capital exit planning

VC firms evaluating whether a portfolio company is acquisition-ready use the screener from the acquirer's perspective. A score below 40 with specific dimension weaknesses tells founders and boards exactly where to focus before running a formal process — better IP protection, a stronger tech stack, or addressing consumer complaints.

Competitive intelligence and market mapping

Corporate strategy teams screen entire categories of potential targets to map the acquisition landscape. Running the screener across 10-20 companies in a vertical produces a ranked table of acquisition readiness that feeds directly into market entry or consolidation strategies.

How to screen acquisition targets

1. Enter the company name — type the exact legal or trading name (e.g., "Datadog", "HashiCorp", "Semrush"). The actor derives the target domain from the name automatically.
2. Add the stock ticker if public — entering the ticker (e.g., "DDOG") improves precision for SEC EDGAR and insider trading lookups; leave blank for private companies.
3. Optionally add the industry — providing "SaaS", "Cybersecurity", or "E-commerce" adds context to the report and helps downstream analysis.
4. Click Start and wait 2-5 minutes — all 16 sub-actors run in parallel; the run finishes when the last data source responds.
5. Download results — open the Dataset tab and export in JSON, CSV, or Excel for integration with your deal tracking workflow.

Input parameters

Input examples

Standard public company screening:

{
    "companyName": "Datadog",
    "ticker": "DDOG",
    "industry": "Observability"
}

Private company screening (no ticker):

{
    "companyName": "Figma",
    "industry": "Design Software"
}

Minimal input — company name only:

{
    "companyName": "HashiCorp"
}

Input tips

• Use the ticker for public companies — SEC EDGAR and insider trading lookups are significantly more precise with a ticker than a company name alone, especially for companies with common words in their name.
• Match the company name to its website domain — the actor auto-derives the target domain as {companyname}.com (lowercase, spaces removed). For companies with unusual domains, the tech stack and Shopify lookups may miss; you can adjust by entering the domain-matching name.
• Run one company per run — each run screens a single target; batch comparison across 10 targets means 10 separate runs, which you can trigger programmatically via the API.
• Expect richer results for public companies — private companies skip SEC EDGAR, insider trading, and CFPB data, but still score across IP, workforce, technology, and market position.

Output example

{
    "company": "Datadog",
    "ticker": "DDOG",
    "industry": "Observability",
    "screenedAt": "2026-03-20T09:15:00.000Z",
    "acquisitionReadinessScore": 78,
    "grade": "STRONG TARGET",
    "recommendation": "Strong acquisition target. Proceed to detailed due diligence and valuation.",
    "dealBreakers": [],
    "dimensions": {
        "financialHealth": {
            "score": 20,
            "max": 25,
            "findings": [
                "18 SEC filing(s) — financial transparency available",
                "5 annual reports (10-K) — multi-year financial history",
                "Insider sentiment POSITIVE: 11 buys vs 4 sells — management believes in growth",
                "No CFPB complaints — clean consumer track record"
            ]
        },
        "ipPortfolio": {
            "score": 17,
            "max": 20,
            "findings": [
                "34 USPTO patents — strong US IP portfolio",
                "9 EPO patents — international IP coverage",
                "14 EUIPO trademarks — strong brand portfolio",
                "IP coverage spans US and EU — geographic diversity"
            ]
        },
        "workforceStability": {
            "score": 16,
            "max": 20,
            "findings": [
                "38 active job postings — aggressive growth hiring",
                "Hiring across 4 departments — well-rounded organization",
                "47% R&D roles — innovation-focused"
            ]
        },
        "technologyMaturity": {
            "score": 17,
            "max": 20,
            "findings": [
                "27 technologies detected — mature tech stack",
                "22 public GitHub repos — strong open source presence",
                "No major tech debt signals in detected stack",
                "Modern tech stack: React, TypeScript, Kubernetes, Docker, Terraform"
            ]
        },
        "marketPosition": {
            "score": 8,
            "max": 15,
            "findings": [
                "Average review rating 4.3/5 across 189 reviews — strong reputation",
                "Competitive intelligence data available for market positioning analysis",
                "3 top-10 SERP ranking(s) — strong search visibility"
            ]
        }
    },
    "insiderSentiment": {
        "buys": 11,
        "sells": 4,
        "signal": "POSITIVE"
    },
    "reputation": {
        "totalReviews": 189,
        "averageRating": 4.3,
        "sentiment": "STRONG"
    },
    "competitivePosition": {
        "competitiveIntelAvailable": true,
        "companyResearchAvailable": true,
        "shopifyPresence": false,
        "serpVisibility": 12
    },
    "dataSources": {
        "secFilings": 18,
        "insiderTrades": 15,
        "cfpbComplaints": 0,
        "usptoPatents": 34,
        "epoPatents": 9,
        "trademarks": 14,
        "jobPostings": 38,
        "techStackItems": 27,
        "githubRepos": 22,
        "reviews": 189,
        "competitiveIntel": 11,
        "serpRankings": 12
    },
    "rawData": {
        "secFilings": ["... 18 SEC filing objects ..."],
        "insiderTrades": ["... 15 insider trade records ..."],
        "usptoPatents": ["... 34 patent records ..."],
        "jobPostings": ["... 38 job posting records ..."]
    }
}

Output fields

How much does it cost to screen acquisition targets?

Acquisition Target Screener uses pay-per-run pricing — each company screening costs approximately $0.50-$1.50 depending on how many of the 16 data sources return results for that target. Platform compute costs are included.

You can set a maximum spending limit per run to control costs. The actor stops when your budget is reached.

Compare this to commissioning third-party due diligence at $25,000-$100,000 per engagement, or subscribing to CapIQ or PitchBook at $15,000-$30,000/year. Most M&A teams run full sector sweeps of 20-30 targets for under $35 total.

Screen acquisition targets using the API

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("ryanclinton/acquisition-target-screener").call(run_input={
    "companyName": "Datadog",
    "ticker": "DDOG",
    "industry": "Observability"
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    score = item["acquisitionReadinessScore"]
    grade = item["grade"]
    breakers = item["dealBreakers"]
    print(f"{item['company']}: {score}/100 — {grade}")
    if breakers:
        print(f"  DEAL BREAKERS: {'; '.join(breakers)}")
    dims = item["dimensions"]
    print(f"  Financial: {dims['financialHealth']['score']}/25")
    print(f"  IP:        {dims['ipPortfolio']['score']}/20")
    print(f"  Workforce: {dims['workforceStability']['score']}/20")
    print(f"  Tech:      {dims['technologyMaturity']['score']}/20")
    print(f"  Market:    {dims['marketPosition']['score']}/15")

JavaScript

import { ApifyClient } from "apify-client";

const client = new ApifyClient({ token: "YOUR_API_TOKEN" });

const run = await client.actor("ryanclinton/acquisition-target-screener").call({
    companyName: "Datadog",
    ticker: "DDOG",
    industry: "Observability"
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
for (const item of items) {
    console.log(`${item.company}: ${item.acquisitionReadinessScore}/100 — ${item.grade}`);
    if (item.dealBreakers.length > 0) {
        console.log(`DEAL BREAKERS: ${item.dealBreakers.join("; ")}`);
    }
    const d = item.dimensions;
    console.log(`Financial ${d.financialHealth.score}/25 | IP ${d.ipPortfolio.score}/20 | Workforce ${d.workforceStability.score}/20 | Tech ${d.technologyMaturity.score}/20 | Market ${d.marketPosition.score}/15`);
}

cURL

# Start the actor run
curl -X POST "https://api.apify.com/v2/acts/ryanclinton~acquisition-target-screener/runs?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "companyName": "Datadog",
    "ticker": "DDOG",
    "industry": "Observability"
  }'

# Fetch results (replace DATASET_ID from the run response above)
curl "https://api.apify.com/v2/datasets/DATASET_ID/items?token=YOUR_API_TOKEN&format=json"

How Acquisition Target Screener works

Phase 1: Parallel data collection across 16 sources

All 16 sub-actors are dispatched simultaneously using Promise.all. Each sub-actor call has a 300-second timeout and retrieves up to 1,000 records. The 16 sources are grouped into five categories:

• Financial: SEC EDGAR filing search, SEC EDGAR filing analyzer, SEC insider trading (Form 4 data), CFPB consumer complaints
• IP portfolio: USPTO patent search, EPO patent search, EUIPO trademark search
• Workforce: Job market intelligence (active job postings with title parsing)
• Technology: Website tech stack detector (Wappalyzer-style detection), GitHub repo search
• Market position: Trustpilot review analyzer, multi-review analyzer, SaaS competitive intel, company deep research, Shopify store intelligence, SERP rank tracker

If any sub-actor fails or times out, it returns an empty array and scoring continues without it. No single source failure blocks the run.

Phase 2: Five-dimension scoring

Each dimension runs an independent scoring function against the collected data:

Financial Health (max 25) — awards 8 points for any SEC filing presence, 4 bonus points for 3+ annual 10-K filings, up to 6 points for positive insider sentiment (buys > sells), and up to 5 points for clean CFPB complaint history. Private companies without SEC filings receive 2 base points rather than 0.

IP Portfolio (max 20) — awards up to 8 points for USPTO patent depth (20+ patents = 8, 5-20 = 5, 1-5 = 2), up to 5 points for EPO international coverage, up to 5 points for EUIPO trademark breadth, and 2 bonus points when both US and EU IP is present.

Workforce Stability (max 20) — awards up to 10 points for active job posting volume (30+ = 10, 10-30 = 7, any = 4), up to 6 points for hiring across 4+ distinct functional departments, and 4 points when R&D roles exceed 40% of all postings.

Technology Maturity (max 20) — awards up to 6 points for tech stack depth (20+ technologies = 6), up to 5 points for GitHub open source presence (20+ repos = 5), 4 points when no legacy tech debt frameworks are detected, and up to 5 points for 3+ modern framework adoptions from the curated list.

Market Position (max 15) — awards up to 5 points for average review rating (4.0+ = 5, 3.0-3.9 = 3), 3 points for competitive intelligence data, 2 points for company deep research data, 2 points for Shopify e-commerce presence, and 3 points for any top-10 SERP rankings.

Phase 3: Deal breaker identification

Three hard-coded conditions trigger a deal breaker flag regardless of the composite score:

1. More than 10 insider sell transactions with zero buy transactions — interpreted as insiders fleeing
2. More than 200 CFPB consumer complaint records — interpreted as severe customer satisfaction failure
3. Zero patents and zero trademarks despite a Technology Maturity score above 10 — interpreted as unprotected IP assets

Any deal breaker overrides the recommendation text with a caution warning.

Phase 4: Output assembly

The final dataset record includes the composite score, grade, recommendation, deal breakers, all five dimension objects with their sub-scores and findings arrays, summary objects for insider sentiment and reputation, data source record counts, and the complete raw data from all 16 sub-actors nested under rawData.

Tips for best results

1. Provide the stock ticker for public companies. The SEC EDGAR queries are significantly more precise with a ticker like "DDOG" than a name like "Datadog". Insider trading lookups in particular depend on an accurate ticker for reliable results.

2. Cross-check domain-derived lookups. The actor derives the target domain as companyname.com. If the company's actual domain is company.io or company.co, tech stack and review lookups may miss. For these cases, enter the name that matches the company's primary domain (e.g., enter "Figma" not "Figma Inc" since the domain is figma.com).

3. Use scheduling for watchlist monitoring. Corporate development teams tracking 20-50 targets benefit from scheduling monthly runs on each target. A rising Workforce Stability or Technology Maturity score signals an accelerating company — and potentially a narrowing acquisition window.

4. Compare scores relative to each other, not absolutely. A score of 60 in a highly regulated industry with limited public IP data is very different from 60 in an open-source SaaS sector. Use the dimension breakdown, not just the total, when comparing across different industry verticals.

5. Treat the raw data as a starting point. The rawData field contains all 16 sub-actor datasets. Analysts can run custom queries against patent records, job titles, or review text beyond what the scoring model captures.

6. Combine with Company Deep Research for narrative intelligence on the target's business model, competitive positioning, and key personnel — the acquisition screener provides the quantitative score; deep research provides the qualitative context.

7. Run the B2B Lead Qualifier on the target's customer list if available — understanding the quality of the revenue base is a critical acquisition signal not captured by the screener's scoring model.

Combine with other Apify actors

Limitations

• Private companies yield lower scores by design. No SEC filings means the Financial Health dimension is capped — private companies cannot earn the full 25 points. This is accurate, not a bug; it reflects the genuine information asymmetry of private company due diligence.
• Domain derivation is best-effort. The actor constructs companyname.com from the company name. Companies with non-.com domains, subsidiary names, or brand names that differ from their domain will have partial tech stack and review data.
• Patent data reflects public filings only. Pending patents, trade secrets, and defensive IP strategies are not captured. A zero patent count does not necessarily mean zero IP.
• Insider trading data applies to US public companies only. SEC Form 4 data is only available for companies registered with the US Securities and Exchange Commission.
• CFPB complaints apply primarily to financial services companies. A zero CFPB complaint count for a SaaS company is not meaningful; the Financial Health scoring accounts for this by not penalizing tech companies for low complaint counts.
• Review scores depend on the target's Trustpilot presence. B2B SaaS companies are frequently not on Trustpilot. For these targets, use the Multi-Review Analyzer standalone to check G2 and Capterra.
• The 0-100 score is a screening heuristic, not a valuation. It does not incorporate revenue, EBITDA, growth rate, customer concentration, or other financial metrics central to real M&A valuation. It is a first-pass prioritization tool.
• Job posting counts reflect a moment in time. Hiring freezes, recent layoffs, or companies in stealth mode will show zero postings even if the underlying business is healthy. Cross-reference with LinkedIn headcount data for high-confidence assessments.

Integrations

• Zapier — trigger an acquisition screening run automatically when a new target is added to a deal tracking spreadsheet or CRM
• Make — build multi-step workflows that screen a target, format the report, and email the deal team on completion
• Google Sheets — push Acquisition Readiness Scores and dimension breakdowns into a Google Sheet for side-by-side target comparison tables
• Apify API — integrate into internal deal management systems, investment committee dashboards, or data pipelines via REST API
• Webhooks — fire a webhook when a run completes to notify deal teams in Slack, Microsoft Teams, or any HTTP endpoint
• LangChain / LlamaIndex — feed acquisition screening output into an LLM pipeline for natural-language deal summaries and Q&A over raw due diligence data

Troubleshooting

• Score seems lower than expected for a well-known public company — The most common cause is a missing ticker. Without a ticker, SEC EDGAR and insider trading lookups use the company name as a text query, which returns fewer and less precise results. Add the ticker and rerun.

• Many dataSources fields show 0 for a large company — Some sub-actors are rate-limited or return zero results for companies that don't appear prominently in their data source. Check whether the company name matches exactly what appears in the relevant registry (e.g., "Alphabet Inc" versus "Google"). The rawData fields let you inspect what each sub-actor returned.

• Run appears to hang beyond 5 minutes — The actor waits up to 300 seconds per sub-actor call. If the Apify platform is under load or a third-party data source is slow, a run can take up to 8-10 minutes. If a run exceeds 15 minutes, abort and retry.

• Deal breaker flagged for insider selling on a company you believe is healthy — The deal breaker threshold is 10+ sells with zero buys. Some executives sell shares for diversification or tax planning rather than negative sentiment. Review the rawData.insiderTrades field to inspect the specific transactions before treating the flag as conclusive.

• Private company shows zero IP Portfolio score — If the company operates under a different legal entity name in patent and trademark databases, the searches may miss their filings. Check USPTO and EPO directly using the company's full legal name or any parent entity names.

Responsible use

• This actor only accesses publicly available records from SEC EDGAR, CFPB, USPTO, EPO, EUIPO, and other open databases.
• Respect the terms of service of each underlying data source accessed through sub-actor calls.
• Acquisition Readiness Scores are screening tools only. Do not make material investment decisions based solely on automated screening output without qualified professional review.
• Comply with applicable securities laws when using insider trading data as part of investment analysis.
• For guidance on web scraping legality, see Apify's guide.

FAQ

How does the Acquisition Target Screener differ from CapIQ or PitchBook? CapIQ and PitchBook require $15,000-$30,000/year subscriptions and are optimized for financial data and deal history. This actor costs $0.50-$1.50 per run and focuses on operational signals — hiring velocity, tech stack modernity, IP coverage, and consumer sentiment — that financial databases don't provide. It is designed as a first-pass screening tool that complements financial data platforms, not replaces them.

How accurate is the Acquisition Readiness Score? The score is a composite of factual data counts and signals — the number of patents filed, insider buy/sell ratios, review ratings — rather than subjective assessments. Its predictive accuracy depends on data availability. Public companies with active online presence score most reliably. Private companies or those with unusual domain structures will produce lower-confidence scores.

How many companies can I screen in one run? Each run screens a single company. To screen 10 targets, trigger 10 separate runs via the API. The Python and JavaScript examples above are easy to loop over a list of company names and tickers.

How long does an acquisition screening run take? Typically 2-5 minutes. All 16 sub-actors run in parallel, so total time is determined by the slowest data source, not the sum of all 16. High-profile public companies with broad patent and review footprints may take longer as sub-actors return larger result sets.

Can I screen private companies with Acquisition Target Screener? Yes. Private companies score across IP Portfolio, Workforce Stability, Technology Maturity, and Market Position. The Financial Health dimension is limited to consumer complaint data and a small base score since SEC filings are unavailable. Expect total scores to be 10-20 points lower for comparable private versus public targets.

What are the three deal breakers the actor detects? The actor flags three hard-coded conditions: (1) more than 10 insider sell transactions with zero buys — indicating insiders are exiting; (2) more than 200 CFPB consumer complaint records — indicating severe customer satisfaction failure; (3) zero patents and zero trademarks despite a Technology Maturity score above 10 — indicating unprotected IP assets. Any deal breaker overrides the recommendation with a caution warning regardless of the composite score.

Is it legal to use SEC insider trading data for acquisition research? Yes. SEC Form 4 insider trading disclosures are publicly available records required by law. Using public regulatory filings for investment research is standard practice. However, acting on material non-public information (MNPI) is illegal — this actor only accesses public filings, not MNPI.

Can I schedule this actor to monitor targets over time? Yes. Apify's scheduling feature lets you run the actor on a daily, weekly, or monthly cadence for each target. This is useful for corporate development teams tracking a watchlist of 20-50 companies — a rapidly improving Workforce Stability score or new patent filings may signal an accelerating company and a narrowing acquisition window.

How does the actor handle companies that don't have a .com domain? The actor constructs the target domain as companyname.com. If the actual domain is .io, .co, or a different TLD, tech stack detection and Trustpilot lookups may miss. To work around this, enter the name that matches the company's primary .com domain, or note that tech stack and review scores may be understated for that target.

What happens when a sub-actor fails or returns no results? Each sub-actor call is wrapped in a try/catch. If a call fails or times out, it returns an empty array and the scoring continues without that data source. The dataSources field in the output shows exactly how many records each source returned, so you can identify which sources contributed to the score and which returned zero.

Does the Acquisition Readiness Score replace professional due diligence? No. The score is a first-pass screening tool for prioritizing which targets warrant full due diligence. It does not assess revenue quality, customer concentration, management team depth, regulatory risk, or go-to-market strategy — all critical factors in M&A valuation. Use it to decide which companies to investigate further, not to make the final acquisition decision.

How is the grade scale structured? The five grade tiers are: PRIME TARGET (80-100), STRONG TARGET (60-79), MODERATE TARGET (40-59), WEAK TARGET (20-39), and NOT RECOMMENDED (0-19). These thresholds reflect the composite score across all five dimensions. A PRIME TARGET rating means the target scores well across financial health, IP, workforce, technology, and market position simultaneously — rare for most companies.

Help us improve

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1. Go to Account Settings > Privacy
2. Enable Share runs with public Actor creators

This lets us see your run details when something goes wrong, so we can fix issues faster. Your data is only visible to the actor developer, not publicly.

Support

Found a bug or have a feature request? Open an issue in the Issues tab on this actor's page. For custom solutions or enterprise integrations, reach out through the Apify platform.