# AI & PhD Researcher Dataset Filter β€” recruiting, GTM, research (`crystalbytes/phds`) Actor Turn a raw JSON export of AI / PhD / researcher profiles into a precise, deduplicated, deliverable-grade shortlist in seconds. Built for recruiting teams, B2B growth/SDR teams, and research panels who need clean, targeted lists instead of raw scraping noise. πŸš€ 22.5k records filtered in <6s. - **URL**: https://apify.com/crystalbytes/phds.md - **Developed by:** [CrystalBytes](https://apify.com/crystalbytes) (community) - **Categories:** Jobs, AI, Lead generation - **Stats:** 2 total users, 1 monthly users, 95.7% runs succeeded, NaN bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing from $10.00 / 1,000 delivered records This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## πŸŽ“ AI & PhD Researcher Dataset Filter β€” recruiting, GTM, research **Turn a raw JSON export of AI / PhD / researcher profiles into a precise, deduplicated, deliverable-grade shortlist in seconds.** You do not need extra engineering to get a useful first run. This Actor does **not** browse the web or pull live profiles. You **bring your own** JSON file (a single array of profile objects). The Actor **filters, deduplicates, and shapes** the rows you choose, then writes them to an Apify **Dataset** you can download as JSON or CSV. --- ### Who it is for - **Hiring and talent teams** shortlisting PhD-level AI, ML, or research profiles from an existing export. - **B2B GTM, SDR, and growth teams** who need a clean, ICP-matched list instead of a noisy raw dump. - **Research, policy, and panel coordinators** who need specific countries, languages, or seniority without manual spreadsheet work. - **Data and ops** teams that already have profile JSON and want repeatable, versioned β€œaudience” runs. --- ### Get started in three steps 1. **Try the built-in sample** β€” Open the Actor and run. The bundled demo loads automatically so you can see how filters work; results appear on the **Dataset** tab. 2. **Your own profile file** β€” For production runs, whoever manages your workspace connects the JSON source your organization uses. If you need a different file than the default, ask them to point the Actor at it. 3. **Download results** β€” Open the run’s **Dataset** for the rows. For a step-by-step breakdown, read **`RUN_SUMMARY`** in the run’s default **Key-value store**. > **Note:** The **Input** form is for **filters and export limits** only. **Which JSON file** a run uses is chosen outside the public form (by your workspace setup). --- ### Find the right people (practical playbooks) Use the matching sections in the **Input** form. Leave a field empty to turn that filter off. | I want to… | Start here in the form | |------------|-------------------------| | **US or UK** candidates only | **Location** β€” countries include (and add excludes for regions you do not want). | | **Europe-based** PhD+ researchers | **Location** (continent or country) + **Education** β€” minimum level, schools, or degrees. | | **Senior** AI / product / legal in **software** | **Career** β€” industry, job title, job level; optionally **Company** for size or employer name. | | **Quality contacts** (work email, fewer bad domains) | **Contact quality** β€” require work email, allow / block email domains. | | **A tight shortlist, not the whole file** | **Volume, sampling & pagination** β€” see [How many rows you export](#how-many-rows-you-export) below. | | **No duplicate people** | **Deduplication** β€” pick a primary key (e.g. LinkedIn username) and optional backup key. | | **Safer sharing or demos** (masked email / phone) | **Output shaping & privacy** β€” redact PII, trim fields, or flatten nested fields for CSV. | Narrow with **AND** (every enabled group must match) or explore more broadly with **OR** (at least one group). **Exclusion** lists (countries you block, bad domains, title excludes) are **always** applied, even in OR mode, so you do not β€œleak” blocked rows by accident. --- ### How filters work (short version) - Each enabled field is a **condition**. **Match mode** (AND / OR) controls how **groups** of conditions combine; values **inside** one list are OR’d (e.g. any of several countries). - **Empty** = that filter is off. - **Excludes** (countries, companies, keywords, etc.) are always enforced for safety. The Console form is grouped into sections: **Optional listing** (if used) β†’ **Filter logic** β†’ **Volume** β†’ **Location** through **Output shaping**. Every field has examples and tips inline. --- ### How many rows you export The Actor filters the **entire** file first, then **deduplicates** (if you set dedupe), then optionally takes a **random sample**, and **only then** applies row limits. So limits always apply to the **qualified** list. You can use **either** style β€” **not both** (the run will stop with a clear error if you mix them on purpose). #### A) β€œStart at row” and β€œStop before row” (range) Good when you want a **single slice** without doing math (e.g. β€œrows 0–999” or β€œ100 to 1000”). - **Start at row** β€” 0 = first row in the *matched* list (after filters, dedupe, and optional sample). - **Stop before row** β€” **Exclusive** end: valid rows are **\[Start, Stop)**. Example: start `0`, stop `1000` = first 1000 rows. Start `100`, stop `1000` = 900 rows (indices 100 through 999). **Rows in this export β‰ˆ Stop βˆ’ Start.** **Paid** plans support starting after row 0 (pagination). On the **free** tier, starting after the first row is not supported β€” use the first slice only, or upgrade for offset / pagination. #### B) β€œSkip first N” and β€œMax records” (classic) - **Skip first N** β€” offset after the qualified list (page 2 of 1 000: skip `1000`, max `1000` when each β€œpage” is 1 000 rows). - **Max records to output** β€” `0` means β€œup to the limit allowed by your **plan** and the monthly allowance,” not β€œzero rows.” **Random sample** (optional) shuffles the qualified list *before* skip / cap β€” use it for A/B tests or training splits, not for stable paging unless you know what you are doing. **Billing reminder:** the platform may charge by **delivered** rows; your plan also enforces per-run and per-month caps. See the Actor’s **Pricing** tab in Apify and **`RUN_SUMMARY` β†’ `monetization`**. --- ### Output and transparency - **Dataset** β€” one JSON object per row; download as JSON, CSV, or Excel from the run. - **`RUN_SUMMARY`** (in the run’s default **Key-value store**) β€” how many records were loaded, filtered, deduplicated, sampled, skipped, and exported, plus **monetization** and timing. Use it when results look empty, too small, or when reconciling usage. Set **Flatten nested fields** for wider CSV columns. Use **Redact PII** when you need shareable samples without full email or phone. --- ### Pricing and plans (summary) **Exact** unit prices, events, and any platform fees are on this Actor’s **Pricing** tab in the Apify Console. The table below is the **Actor-side policy** (from our tier file), so you can see run and monthly **caps**; it is not a substitute for the Console invoice. | Tier | Max / run | Max / month | Runs / day | Free tier field limits | |------------|-----------|-------------|------------|-------------------------| | `free` | 50 | 120 | 1 | Yes (basic fields only) | | `starter` | 4 000 | 15 000 | no hard daily cap in Actor | β€” | | `pro` | 4 000 | 25 000 | no hard daily cap in Actor | β€” | | `agency` | 10 000 | 100 000 | no hard daily cap in Actor | β€” | | `development` | (high) | (high) | (high) | For local / owner tests only | - **Free** strips sensitive columns (e.g. work email, phones, some addresses) so you can evaluate fit before upgrading. - **Paid** tiers unlock the full record, **offset pagination** (skip / start-after-first-row), and **overage** past the monthly cap where configured β€” see the Console for **overage** event names and prices. - After each run, check **`RUN_SUMMARY` β†’ `monetization`** and compare to your Apify billing view. --- ### Trust, data, and compliance - You supply the JSON; this run **does not** crawl third-party sites or β€œdiscover” new profiles from the open web. - You are responsible for **lawful** use, consent, and platform terms that apply to your source data (e.g. privacy rules, email outreach laws). - Use **redaction** and **field allow / deny** lists for demos, contractors, or external sharing. - **Who can see a run’s full Input** is controlled in Apify (organization permissions). Do not put passwords or private keys in task input. On **performance** and large files, see **Options** on the run (memory, timeout). A rough guide: a **22k-row** file has been used in development tests in a few seconds at **2 GB** memory; very large single files may need more memory, a longer timeout, or splitting the source file β€” ask your workspace admin if a run times out or runs out of memory. --- ### Reliability and support - Invalid inputs (e.g. bad **regex** patterns, over-claimed **advertised** counts, or **conflicting** volume settings) fail fast with a readable error. - **0 results after filters** β€” widen one group at a time, try **OR** match mode, or check **`RUN_SUMMARY` β†’ `pipeline`** to see where the list went to zero. **Support and feedback:** `crystalbytes@proton.me` β€” usually within one business day. --- *Ready to build a clean, plan-aware shortlist from your own researcher JSON β€” start a run and refine filters using `RUN_SUMMARY` until the numbers match your goal.* # Actor input Schema ## `sourceR2Bucket` (type: `string`): Set via API or task JSON if the publisher uses R2. Hidden from the default form. Leave **empty** for Apify Storage or the bundled local demo. Example: `acme-datasets` ## `sourceR2Key` (type: `string`): Object path when using R2. The Actor also reads `R2_KEY` or `R2_OBJECT_KEY` from the environment if this is left empty. Hidden from the default form. ## `sourceR2Endpoint` (type: `string`): Only if the Actor owner configured a non-default S3-compatible endpoint. Hidden from the default form; usually set in environment. ## `inputJsonPath` (type: `string`): Hidden in the default Console form. For local `apify run`, defaults to `data/demo_sample.json` in code when unset. Set manually in run input or API for custom paths relative to the project root. ## `advertisedRecordCount` (type: `integer`): Optional public-facing count for the listing. Must not exceed the number of records in your file. When empty, the true file count is used. ## `matchMode` (type: `string`): Global combinator for filter **groups**. AND is the default and recommended for narrowing. Switch to OR to widen results without deleting filters. ## `textMatchMode` (type: `string`): Applies to free-text fields (name, job title, LinkedIn username, keywords, etc.). - `contains` β€” substring, most forgiving (default). - `exact` β€” whole-string equality after trim. - `startsWith` / `endsWith` β€” prefix / suffix match. - `regex` β€” JavaScript RegExp. Use `\b`, `|`, `^` etc. Example: `^(chief|head)\s`. ⚠️ `regex` is powerful β€” invalid patterns will fail the run with a clear error. ## `caseSensitive` (type: `boolean`): When enabled, text comparisons respect upper / lower case. Default **off** β€” useful because most of the dataset is stored lowercase. ## `exportFromIndex` (type: `integer`): 0-based index into the **matched** list (after filters, dedupe, and optional random sample). `0` = first row. Only used when **Stop before row** is set; otherwise use *Skip first N* below. ## `exportToExclusiveIndex` (type: `integer`): **Optional.** When set, export only half-open range **[Start at row, Stop before row)** β€” same as a spreadsheet row slice. Example: start `0`, stop `1000` = first 1000 rows. Leave **empty** to use *Skip first N* + *Max records* instead. Must be β‰₯ *Start at row*. ## `maxRecords` (type: `integer`): Maximum rows written to the result. `0` = as many as your **current plan** allows, up to how many rows matched. **Not** used when *Stop before row* is set (use Start/Stop range or this + Skip β€” not both). Tip: about `500` while tuning filters. ## `skipRecords` (type: `integer`): Pagination offset applied after filtering & dedupe. Use with *Max records* for chunked exports β€” e.g. page 2 of 1000 β†’ skip `1000`, max `1000`. ## `randomSample` (type: `integer`): If > 0, take a **random sample** of N records (Fisher–Yates shuffle) from the filtered set before pagination. Useful for A/B outreach tests and model training splits. Seeded by run date for loose reproducibility within the same day. ## `countriesInclude` (type: `array`): Keep only rows where `country` equals one of these (case-insensitive, exact). Examples: `united states`, `united kingdom`, `germany`, `turkey`. ## `countriesExclude` (type: `array`): Drop rows whose `country` matches any value here. Applied **always** (even in OR mode). Use to blocklist markets you can't serve. Examples: `india`, `china`. ## `regionsInclude` (type: `array`): Keep rows where **any** value in `regions[]` contains **any** of these substrings. Good for state / province targeting: `new york`, `bavaria`, `greater london`. ## `regionsExclude` (type: `array`): Drop rows where any `regions[]` entry contains any of these substrings. Always enforced. ## `localityContains` (type: `array`): Substring match across `location_names[]` and `street_addresses[].locality`. Case-insensitive. Examples: `new york`, `san francisco`, `berlin`. ## `continentsInclude` (type: `array`): Keep rows where **any** continent in `location[].continent` or `street_addresses[].continent` is in this list. Valid values: `north america`, `south america`, `europe`, `asia`, `africa`, `oceania`. ## `industriesInclude` (type: `array`): Keep rows where `industry` contains any of these substrings. Examples: `computer software`, `legal services`, `hospital & health care`. ## `industriesExclude` (type: `array`): Drop rows where `industry` contains any of these substrings. Always enforced. Examples: `staffing`, `gambling`. ## `jobTitleContains` (type: `array`): Text match on `job_title` (and all `experience[].title.name`). Uses your **Text match mode** above (contains / exact / regex / …). Examples: `counsel`, `director`, `chief`. ## `jobTitleExclude` (type: `array`): Reject rows whose `job_title` matches any entry under the active Text match mode. Always enforced. Examples: `intern`, `assistant`. ## `jobRolesInclude` (type: `array`): Taxonomy match against `experience[].title.role` **or** `job[].title_role`. Multi-select. Useful when titles vary wildly (e.g. all sales-flavored roles). ## `jobLevelsInclude` (type: `array`): Taxonomy match against `experience[].title.levels[]`. Pick one or more. `cxo` covers CEO / CTO / CFO etc.; `training` = interns and residents. ## `jobSubRolesContains` (type: `array`): Substring match on `experience[].title.sub_role`. Lets you reach into specialties like `lawyer`, `doctor`, `nursing`, `data`, `graphic_design`, `product`. ## `minYearsExperience` (type: `integer`): Require `inferred_years_experience` β‰₯ this value. `0` = no minimum. Great proxy for seniority when titles are messy. ## `maxYearsExperience` (type: `integer`): Require `inferred_years_experience` ≀ this value. `0` = no maximum. ## `currentlyEmployed` (type: `boolean`): Keep rows where `experience[]` contains an `is_primary: true` entry with no `end_date` (i.e. still in that role). ## `companyNamesInclude` (type: `array`): Keep rows whose `experience[].company.name` contains any of these substrings. Examples: `siemens`, `google`, `mckinsey`. ## `companyNamesExclude` (type: `array`): Drop rows where any `experience[].company.name` contains any of these substrings. Always enforced. Useful to exclude competitors or blacklisted employers. ## `currentCompanyContains` (type: `array`): Substring match on `job_company[].name` (the current employer). Use this when you don't care about history, only where they work *right now*. ## `companyIndustriesInclude` (type: `array`): Substring match on `experience[].company.industry`. Broader than the top-level `industry` because it covers every past employer too. Example: `electrical/electronic manufacturing`. ## `companySizeMin` (type: `string`): Lower bound of `experience[].company.size`. A row passes if they ever worked at a company with headcount β‰₯ this bucket. ## `companySizeMax` (type: `string`): Upper bound of `experience[].company.size`. ## `minCompaniesWorked` (type: `integer`): Require `experience[].length` β‰₯ this value. Proxy for career breadth. ## `schoolsInclude` (type: `array`): Keep rows who studied at any of these institutions. Substring match. Examples: `harvard`, `mit`, `hofstra`. ## `degreesInclude` (type: `array`): Any token in `education[].degrees[]` must contain any of these strings. Common tokens: `bachelors`, `masters`, `doctorates`, `doctor of jurisprudence`, `master of business administration`. ## `majorsInclude` (type: `array`): Substring match on `education[].majors[]`. Examples: `law`, `computer science`, `business`. ## `minEducationLevel` (type: `string`): Require at least one education entry at this level or higher. Degree strings are matched heuristically (regex covers PhD, JD, MD, MBA, MSc, BSc, BA, LLB, associate, etc.). ## `languagesAnyOf` (type: `array`): Keep rows who speak at least one of these languages. Examples: `english`, `german`, `arabic`. ## `languagesAllOf` (type: `array`): Stricter: require **every** listed language to be present in `languages[]`. Example: `english` + `german` = bilingual. ## `interestsInclude` (type: `array`): Substring match on `interests[]`. Often qualitative β€” think `human rights`, `science and technology`, `education`. ## `hasAnyCertification` (type: `boolean`): Drop rows where `certifications` is null / empty. Useful for regulated industries (legal, medical, finance). ## `certificationsInclude` (type: `array`): Substring match on `certifications[].name` (or raw string). Examples: `pmp`, `bar exam`, `cpa`. ## `genderInclude` (type: `string`): Filter on `basic_info.gender`. Dataset currently stores `male` / `female`; rows with unspecified gender are passed through on *Any*. ## `nameContains` (type: `array`): Text match on `full_name` using the active *Text match mode*. With `regex`, a pattern like `^a[ln]` matches names starting with *al* or *an*. ## `nameExclude` (type: `array`): Reject rows whose `full_name` matches. Always enforced. Good for suppression lists or test / dummy rows. ## `linkedinUsernameContains` (type: `string`): Text match on `linkedin_username` using the active *Text match mode*. Empty = off. ## `linkedinRequired` (type: `boolean`): Drop rows without a `linkedin_username` β€” handy when your downstream tool keys off LinkedIn URLs. ## `minConnections` (type: `integer`): Require `connections` β‰₯ this value. `0` = no minimum. ## `maxConnections` (type: `integer`): Require `connections` ≀ this value. `0` = no maximum. ## `requireAnyEmail` (type: `boolean`): Drop rows where both `email` and `work_email` are empty or null. ## `requireWorkEmail` (type: `boolean`): Drop rows where `work_email` is missing **and** no `emails[]` entry has a professional-type token (`professional`, `current_professional`, `previous_professional`, `work`, or `business`). The Actor canonicalizes these to the same class before filtering. ## `requirePersonalEmail` (type: `boolean`): Drop rows where `email` is missing **and** no `emails[]` entry has `type = personal`. ## `requirePhone` (type: `boolean`): Drop rows where `phone_numbers` is null or empty. ## `emailDomainsInclude` (type: `array`): Keep rows where **any** email (personal, work, or from `emails[]`) ends with one of these domains. Examples: `gmail.com`, `siemens.com`. ## `emailDomainsExclude` (type: `array`): Drop rows where any email ends with one of these domains. Always enforced. Common blocklist: `mailinator.com`, `guerrillamail.com`, `example.com`. ## `globalKeywordsAny` (type: `array`): Keep rows where **any** of these keywords appears in **any** of the searched fields. Uses the active *Text match mode* (supports regex). Examples: `patent`, `intellectual property`. ## `globalKeywordsExclude` (type: `array`): Drop rows where any of these keywords appears in any searched field. Always enforced. Great for suppressing known irrelevant niches. ## `dedupeByField` (type: `string`): Pick the best unique identifier for your audience. `linkedin_username` is usually safest; `email` is great for deliverability lists. ## `secondaryDedupeField` (type: `string`): When the primary key is missing or empty on a row, fall back to this key so you don't lose records that lack (e.g.) a LinkedIn handle but do have an email. ## `fieldsToKeep` (type: `array`): If non-empty, only these top-level keys are emitted. Nested paths are **not** supported here β€” use *Fields to omit* for surgical removal. Example keys: `id`, `full_name`, `email`, `linkedin_username`, `country`, `job_title`. ## `fieldsToOmit` (type: `array`): Strip these top-level keys from every emitted record. Applied *after* `fieldsToKeep`. Example keys: `phone_numbers`, `street_addresses`. ## `redactPii` (type: `boolean`): Masks emails as `j***@domain.com` and keeps only the last 4 digits of phone numbers. Useful when sharing samples with contractors or posting demos publicly. Does **not** affect filtering β€” only the output. ## `flattenNestedFields` (type: `boolean`): Expose the most-used nested bits at the top level: `first_name`, `last_name`, `gender`, `primary_city`, `primary_region`, `primary_continent`, `current_company_name`, `primary_email_domain`. Original nested objects remain, unless you also use *Fields to omit*. ## Actor input object example ```json { "sourceR2Bucket": "", "sourceR2Key": "", "sourceR2Endpoint": "", "inputJsonPath": "", "matchMode": "AND", "textMatchMode": "contains", "caseSensitive": false, "exportFromIndex": 100, "exportToExclusiveIndex": 1000, "maxRecords": 5000, "skipRecords": 1000, "randomSample": 250, "countriesInclude": [ "united states", "canada" ], "countriesExclude": [ "india" ], "regionsInclude": [ "new york", "california" ], "regionsExclude": [ "texas" ], "localityContains": [ "new york", "london" ], "continentsInclude": [ "north america", "europe" ], "industriesInclude": [ "computer software", "research" ], "industriesExclude": [ "staffing and recruiting" ], "jobTitleContains": [ "counsel", "chief" ], "jobTitleExclude": [ "intern" ], "jobRolesInclude": [ "legal", "engineering" ], "jobLevelsInclude": [ "cxo", "vp" ], "jobSubRolesContains": [ "lawyer", "data" ], "minYearsExperience": 5, "maxYearsExperience": 25, "currentlyEmployed": false, "companyNamesInclude": [ "google", "siemens" ], "companyNamesExclude": [ "acme" ], "currentCompanyContains": [ "siemens" ], "companyIndustriesInclude": [ "electrical/electronic manufacturing" ], "companySizeMin": "", "companySizeMax": "", "minCompaniesWorked": 3, "schoolsInclude": [ "harvard" ], "degreesInclude": [ "masters", "doctorates" ], "majorsInclude": [ "law" ], "minEducationLevel": "any", "languagesAnyOf": [ "english", "french" ], "languagesAllOf": [ "english", "german" ], "interestsInclude": [ "education" ], "hasAnyCertification": false, "certificationsInclude": [ "pmp" ], "genderInclude": "any", "nameContains": [ "smith" ], "nameExclude": [ "test" ], "linkedinUsernameContains": "mducro", "linkedinRequired": false, "minConnections": 100, "maxConnections": 500, "requireAnyEmail": false, "requireWorkEmail": false, "requirePersonalEmail": false, "requirePhone": false, "emailDomainsInclude": [ "gmail.com" ], "emailDomainsExclude": [ "mailinator.com" ], "globalKeywordsAny": [ "patent", "ip" ], "globalKeywordsExclude": [ "bitcoin", "forex" ], "dedupeByField": "none", "secondaryDedupeField": "none", "fieldsToKeep": [ "full_name", "email", "linkedin_username" ], "fieldsToOmit": [ "phone_numbers" ], "redactPii": false, "flattenNestedFields": false } ```` # Actor output Schema ## `overview` (type: `string`): Browse and export filtered rows from the default dataset. ## `signals` (type: `string`): Year-of-experience and connection signals for quick QA. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "countriesInclude": [], "countriesExclude": [], "regionsInclude": [], "regionsExclude": [], "localityContains": [], "continentsInclude": [], "industriesInclude": [], "industriesExclude": [], "jobTitleContains": [], "jobTitleExclude": [], "jobRolesInclude": [], "jobLevelsInclude": [], "jobSubRolesContains": [], "companyNamesInclude": [], "companyNamesExclude": [], "currentCompanyContains": [], "companyIndustriesInclude": [], "schoolsInclude": [], "degreesInclude": [], "majorsInclude": [], "languagesAnyOf": [], "languagesAllOf": [], "interestsInclude": [], "certificationsInclude": [], "nameContains": [], "nameExclude": [], "emailDomainsInclude": [], "emailDomainsExclude": [], "globalKeywordsAny": [], "globalKeywordsExclude": [], "fieldsToKeep": [], "fieldsToOmit": [] }; // Run the Actor and wait for it to finish const run = await client.actor("crystalbytes/phds").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "countriesInclude": [], "countriesExclude": [], "regionsInclude": [], "regionsExclude": [], "localityContains": [], "continentsInclude": [], "industriesInclude": [], "industriesExclude": [], "jobTitleContains": [], "jobTitleExclude": [], "jobRolesInclude": [], "jobLevelsInclude": [], "jobSubRolesContains": [], "companyNamesInclude": [], "companyNamesExclude": [], "currentCompanyContains": [], "companyIndustriesInclude": [], "schoolsInclude": [], "degreesInclude": [], "majorsInclude": [], "languagesAnyOf": [], "languagesAllOf": [], "interestsInclude": [], "certificationsInclude": [], "nameContains": [], "nameExclude": [], "emailDomainsInclude": [], "emailDomainsExclude": [], "globalKeywordsAny": [], "globalKeywordsExclude": [], "fieldsToKeep": [], "fieldsToOmit": [], } # Run the Actor and wait for it to finish run = client.actor("crystalbytes/phds").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "countriesInclude": [], "countriesExclude": [], "regionsInclude": [], "regionsExclude": [], "localityContains": [], "continentsInclude": [], "industriesInclude": [], "industriesExclude": [], "jobTitleContains": [], "jobTitleExclude": [], "jobRolesInclude": [], "jobLevelsInclude": [], "jobSubRolesContains": [], "companyNamesInclude": [], "companyNamesExclude": [], "currentCompanyContains": [], "companyIndustriesInclude": [], "schoolsInclude": [], "degreesInclude": [], "majorsInclude": [], "languagesAnyOf": [], "languagesAllOf": [], "interestsInclude": [], "certificationsInclude": [], "nameContains": [], "nameExclude": [], "emailDomainsInclude": [], "emailDomainsExclude": [], "globalKeywordsAny": [], "globalKeywordsExclude": [], "fieldsToKeep": [], "fieldsToOmit": [] }' | apify call crystalbytes/phds --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=crystalbytes/phds", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI & PhD Researcher Dataset Filter β€” recruiting, GTM, research", "description": "Turn a raw JSON export of AI / PhD / researcher profiles into a precise, deduplicated, deliverable-grade shortlist in seconds. Built for recruiting teams, B2B growth/SDR teams, and research panels who need clean, targeted lists instead of raw scraping noise.\n\nπŸš€ 22.5k records filtered in <6s.", "version": "0.3", "x-build-id": "L1F3MTXIgrv09FeL3" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/crystalbytes~phds/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-crystalbytes-phds", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/crystalbytes~phds/runs": { "post": { "operationId": "runs-sync-crystalbytes-phds", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/crystalbytes~phds/run-sync": { "post": { "operationId": "run-sync-crystalbytes-phds", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "sourceR2Bucket": { "title": "πŸͺ£ Optional β€” cloud bucket (operator / large files)", "type": "string", "description": "Set via API or task JSON if the publisher uses R2. Hidden from the default form. Leave **empty** for Apify Storage or the bundled local demo. Example: `acme-datasets`", "default": "" }, "sourceR2Key": { "title": "πŸ”‘ Object key in that bucket", "type": "string", "description": "Object path when using R2. The Actor also reads `R2_KEY` or `R2_OBJECT_KEY` from the environment if this is left empty. Hidden from the default form.", "default": "" }, "sourceR2Endpoint": { "title": "🌐 Custom R2 endpoint (optional)", "type": "string", "description": "Only if the Actor owner configured a non-default S3-compatible endpoint. Hidden from the default form; usually set in environment.", "default": "" }, "inputJsonPath": { "title": "πŸ’» Local file path (developers & local runs only)", "type": "string", "description": "Hidden in the default Console form. For local `apify run`, defaults to `data/demo_sample.json` in code when unset. Set manually in run input or API for custom paths relative to the project root.", "default": "" }, "advertisedRecordCount": { "title": "πŸ“Š Advertised record count (optional)", "minimum": 0, "type": "integer", "description": "Optional public-facing count for the listing. Must not exceed the number of records in your file. When empty, the true file count is used." }, "matchMode": { "title": "🧩 Match mode between filter groups", "enum": [ "AND", "OR" ], "type": "string", "description": "Global combinator for filter **groups**. AND is the default and recommended for narrowing. Switch to OR to widen results without deleting filters.", "default": "AND" }, "textMatchMode": { "title": "πŸ”€ Text match mode", "enum": [ "contains", "exact", "startsWith", "endsWith", "regex" ], "type": "string", "description": "Applies to free-text fields (name, job title, LinkedIn username, keywords, etc.).\n\n- `contains` β€” substring, most forgiving (default).\n- `exact` β€” whole-string equality after trim.\n- `startsWith` / `endsWith` β€” prefix / suffix match.\n- `regex` β€” JavaScript RegExp. Use `\\b`, `|`, `^` etc. Example: `^(chief|head)\\s`.\n\n⚠️ `regex` is powerful β€” invalid patterns will fail the run with a clear error.", "default": "contains" }, "caseSensitive": { "title": "πŸ”  Case-sensitive matching", "type": "boolean", "description": "When enabled, text comparisons respect upper / lower case. Default **off** β€” useful because most of the dataset is stored lowercase.", "default": false }, "exportFromIndex": { "title": "πŸ“Œ Start at row (after filters & dedupe)", "minimum": 0, "type": "integer", "description": "0-based index into the **matched** list (after filters, dedupe, and optional random sample). `0` = first row. Only used when **Stop before row** is set; otherwise use *Skip first N* below.", "default": 0 }, "exportToExclusiveIndex": { "title": "πŸ›‘ Stop before row (exclusive end)", "minimum": 0, "type": "integer", "description": "**Optional.** When set, export only half-open range **[Start at row, Stop before row)** β€” same as a spreadsheet row slice. Example: start `0`, stop `1000` = first 1000 rows. Leave **empty** to use *Skip first N* + *Max records* instead. Must be β‰₯ *Start at row*." }, "maxRecords": { "title": "πŸ”’ Max records to output", "minimum": 0, "type": "integer", "description": "Maximum rows written to the result. `0` = as many as your **current plan** allows, up to how many rows matched. **Not** used when *Stop before row* is set (use Start/Stop range or this + Skip β€” not both). Tip: about `500` while tuning filters.", "default": 0 }, "skipRecords": { "title": "⏭️ Skip first N records (offset)", "minimum": 0, "type": "integer", "description": "Pagination offset applied after filtering & dedupe. Use with *Max records* for chunked exports β€” e.g. page 2 of 1000 β†’ skip `1000`, max `1000`.", "default": 0 }, "randomSample": { "title": "🎲 Random sample size (0 = disabled)", "minimum": 0, "type": "integer", "description": "If > 0, take a **random sample** of N records (Fisher–Yates shuffle) from the filtered set before pagination. Useful for A/B outreach tests and model training splits. Seeded by run date for loose reproducibility within the same day.", "default": 0 }, "countriesInclude": { "title": "🌍 Countries β€” include (any-of)", "type": "array", "description": "Keep only rows where `country` equals one of these (case-insensitive, exact). Examples: `united states`, `united kingdom`, `germany`, `turkey`.", "items": { "type": "string" } }, "countriesExclude": { "title": "🚫 Countries β€” exclude", "type": "array", "description": "Drop rows whose `country` matches any value here. Applied **always** (even in OR mode). Use to blocklist markets you can't serve. Examples: `india`, `china`.", "items": { "type": "string" } }, "regionsInclude": { "title": "πŸ—ΊοΈ Regions β€” include (substring, any-of)", "type": "array", "description": "Keep rows where **any** value in `regions[]` contains **any** of these substrings. Good for state / province targeting: `new york`, `bavaria`, `greater london`.", "items": { "type": "string" } }, "regionsExclude": { "title": "🚷 Regions β€” exclude", "type": "array", "description": "Drop rows where any `regions[]` entry contains any of these substrings. Always enforced.", "items": { "type": "string" } }, "localityContains": { "title": "πŸ™οΈ City / locality contains (any-of)", "type": "array", "description": "Substring match across `location_names[]` and `street_addresses[].locality`. Case-insensitive. Examples: `new york`, `san francisco`, `berlin`.", "items": { "type": "string" } }, "continentsInclude": { "title": "🌐 Continents β€” include", "type": "array", "description": "Keep rows where **any** continent in `location[].continent` or `street_addresses[].continent` is in this list. Valid values: `north america`, `south america`, `europe`, `asia`, `africa`, `oceania`.", "items": { "type": "string" } }, "industriesInclude": { "title": "🏭 Industries β€” include (substring, any-of)", "type": "array", "description": "Keep rows where `industry` contains any of these substrings. Examples: `computer software`, `legal services`, `hospital & health care`.", "items": { "type": "string" } }, "industriesExclude": { "title": "🧱 Industries β€” exclude", "type": "array", "description": "Drop rows where `industry` contains any of these substrings. Always enforced. Examples: `staffing`, `gambling`.", "items": { "type": "string" } }, "jobTitleContains": { "title": "πŸ“ Current job title β€” include (any-of)", "type": "array", "description": "Text match on `job_title` (and all `experience[].title.name`). Uses your **Text match mode** above (contains / exact / regex / …). Examples: `counsel`, `director`, `chief`.", "items": { "type": "string" } }, "jobTitleExclude": { "title": "πŸ™… Current job title β€” exclude", "type": "array", "description": "Reject rows whose `job_title` matches any entry under the active Text match mode. Always enforced. Examples: `intern`, `assistant`.", "items": { "type": "string" } }, "jobRolesInclude": { "title": "🧭 Job roles (functional area)", "uniqueItems": true, "type": "array", "description": "Taxonomy match against `experience[].title.role` **or** `job[].title_role`. Multi-select. Useful when titles vary wildly (e.g. all sales-flavored roles).", "items": { "type": "string", "enum": [ "customer_service", "design", "education", "engineering", "finance", "health", "human_resources", "legal", "marketing", "media", "operations", "public_relations", "real_estate", "sales" ] } }, "jobLevelsInclude": { "title": "πŸ… Seniority levels", "uniqueItems": true, "type": "array", "description": "Taxonomy match against `experience[].title.levels[]`. Pick one or more. `cxo` covers CEO / CTO / CFO etc.; `training` = interns and residents.", "items": { "type": "string", "enum": [ "cxo", "vp", "director", "partner", "owner", "senior", "manager", "entry", "training", "unpaid" ] } }, "jobSubRolesContains": { "title": "πŸ§ͺ Sub-roles β€” include (any-of)", "type": "array", "description": "Substring match on `experience[].title.sub_role`. Lets you reach into specialties like `lawyer`, `doctor`, `nursing`, `data`, `graphic_design`, `product`.", "items": { "type": "string" } }, "minYearsExperience": { "title": "πŸ§“ Min years of experience", "minimum": 0, "type": "integer", "description": "Require `inferred_years_experience` β‰₯ this value. `0` = no minimum. Great proxy for seniority when titles are messy.", "default": 0 }, "maxYearsExperience": { "title": "πŸ§‘ Max years of experience", "minimum": 0, "type": "integer", "description": "Require `inferred_years_experience` ≀ this value. `0` = no maximum.", "default": 0 }, "currentlyEmployed": { "title": "🟒 Currently employed (primary role open)", "type": "boolean", "description": "Keep rows where `experience[]` contains an `is_primary: true` entry with no `end_date` (i.e. still in that role).", "default": false }, "companyNamesInclude": { "title": "🏒 Company β€” has worked at (any-of)", "type": "array", "description": "Keep rows whose `experience[].company.name` contains any of these substrings. Examples: `siemens`, `google`, `mckinsey`.", "items": { "type": "string" } }, "companyNamesExclude": { "title": "🚯 Company β€” never worked at", "type": "array", "description": "Drop rows where any `experience[].company.name` contains any of these substrings. Always enforced. Useful to exclude competitors or blacklisted employers.", "items": { "type": "string" } }, "currentCompanyContains": { "title": "πŸ“Œ Current employer contains", "type": "array", "description": "Substring match on `job_company[].name` (the current employer). Use this when you don't care about history, only where they work *right now*.", "items": { "type": "string" } }, "companyIndustriesInclude": { "title": "πŸ—οΈ Company industries (from experience)", "type": "array", "description": "Substring match on `experience[].company.industry`. Broader than the top-level `industry` because it covers every past employer too. Example: `electrical/electronic manufacturing`.", "items": { "type": "string" } }, "companySizeMin": { "title": "πŸ‘₯ Min company size bucket", "enum": [ "", "1-10", "11-50", "51-200", "201-500", "501-1000", "1001-5000", "5001-10000", "10001+" ], "type": "string", "description": "Lower bound of `experience[].company.size`. A row passes if they ever worked at a company with headcount β‰₯ this bucket.", "default": "" }, "companySizeMax": { "title": "πŸ‘€ Max company size bucket", "enum": [ "", "1-10", "11-50", "51-200", "201-500", "501-1000", "1001-5000", "5001-10000", "10001+" ], "type": "string", "description": "Upper bound of `experience[].company.size`.", "default": "" }, "minCompaniesWorked": { "title": "🧳 Minimum number of past employers", "minimum": 0, "type": "integer", "description": "Require `experience[].length` β‰₯ this value. Proxy for career breadth.", "default": 0 }, "schoolsInclude": { "title": "πŸŽ“ Schools β€” include (substring, any-of)", "type": "array", "description": "Keep rows who studied at any of these institutions. Substring match. Examples: `harvard`, `mit`, `hofstra`.", "items": { "type": "string" } }, "degreesInclude": { "title": "πŸ₯‡ Degrees β€” include (any-of)", "type": "array", "description": "Any token in `education[].degrees[]` must contain any of these strings. Common tokens: `bachelors`, `masters`, `doctorates`, `doctor of jurisprudence`, `master of business administration`.", "items": { "type": "string" } }, "majorsInclude": { "title": "πŸ“š Majors β€” include (any-of)", "type": "array", "description": "Substring match on `education[].majors[]`. Examples: `law`, `computer science`, `business`.", "items": { "type": "string" } }, "minEducationLevel": { "title": "πŸ›οΈ Minimum education level", "enum": [ "any", "associates", "bachelors", "masters", "doctorate" ], "type": "string", "description": "Require at least one education entry at this level or higher. Degree strings are matched heuristically (regex covers PhD, JD, MD, MBA, MSc, BSc, BA, LLB, associate, etc.).", "default": "any" }, "languagesAnyOf": { "title": "πŸ—£οΈ Languages β€” any of", "type": "array", "description": "Keep rows who speak at least one of these languages. Examples: `english`, `german`, `arabic`.", "items": { "type": "string" } }, "languagesAllOf": { "title": "🈯 Languages β€” all of", "type": "array", "description": "Stricter: require **every** listed language to be present in `languages[]`. Example: `english` + `german` = bilingual.", "items": { "type": "string" } }, "interestsInclude": { "title": "🎯 Interests contain (any-of)", "type": "array", "description": "Substring match on `interests[]`. Often qualitative β€” think `human rights`, `science and technology`, `education`.", "items": { "type": "string" } }, "hasAnyCertification": { "title": "πŸ“œ Must have β‰₯ 1 certification", "type": "boolean", "description": "Drop rows where `certifications` is null / empty. Useful for regulated industries (legal, medical, finance).", "default": false }, "certificationsInclude": { "title": "🏷️ Certifications contain (any-of)", "type": "array", "description": "Substring match on `certifications[].name` (or raw string). Examples: `pmp`, `bar exam`, `cpa`.", "items": { "type": "string" } }, "genderInclude": { "title": "⚧️ Gender", "enum": [ "any", "male", "female", "unspecified" ], "type": "string", "description": "Filter on `basic_info.gender`. Dataset currently stores `male` / `female`; rows with unspecified gender are passed through on *Any*.", "default": "any" }, "nameContains": { "title": "πŸ‘€ Full name contains (any-of)", "type": "array", "description": "Text match on `full_name` using the active *Text match mode*. With `regex`, a pattern like `^a[ln]` matches names starting with *al* or *an*.", "items": { "type": "string" } }, "nameExclude": { "title": "πŸ™ˆ Full name excludes", "type": "array", "description": "Reject rows whose `full_name` matches. Always enforced. Good for suppression lists or test / dummy rows.", "items": { "type": "string" } }, "linkedinUsernameContains": { "title": "πŸ”— LinkedIn username contains", "type": "string", "description": "Text match on `linkedin_username` using the active *Text match mode*. Empty = off." }, "linkedinRequired": { "title": "πŸ”— Require LinkedIn username present", "type": "boolean", "description": "Drop rows without a `linkedin_username` β€” handy when your downstream tool keys off LinkedIn URLs.", "default": false }, "minConnections": { "title": "πŸ“ˆ Minimum connections", "minimum": 0, "type": "integer", "description": "Require `connections` β‰₯ this value. `0` = no minimum.", "default": 0 }, "maxConnections": { "title": "πŸ“‰ Maximum connections", "minimum": 0, "type": "integer", "description": "Require `connections` ≀ this value. `0` = no maximum.", "default": 0 }, "requireAnyEmail": { "title": "βœ‰οΈ Require at least one email (personal OR work)", "type": "boolean", "description": "Drop rows where both `email` and `work_email` are empty or null.", "default": false }, "requireWorkEmail": { "title": "πŸ§‘β€πŸ’Ό Require work email", "type": "boolean", "description": "Drop rows where `work_email` is missing **and** no `emails[]` entry has a professional-type token (`professional`, `current_professional`, `previous_professional`, `work`, or `business`). The Actor canonicalizes these to the same class before filtering.", "default": false }, "requirePersonalEmail": { "title": "🏠 Require personal email", "type": "boolean", "description": "Drop rows where `email` is missing **and** no `emails[]` entry has `type = personal`.", "default": false }, "requirePhone": { "title": "πŸ“ž Require phone number", "type": "boolean", "description": "Drop rows where `phone_numbers` is null or empty.", "default": false }, "emailDomainsInclude": { "title": "🌐 Email domains β€” include (any-of)", "type": "array", "description": "Keep rows where **any** email (personal, work, or from `emails[]`) ends with one of these domains. Examples: `gmail.com`, `siemens.com`.", "items": { "type": "string" } }, "emailDomainsExclude": { "title": "πŸ›‘ Email domains β€” exclude", "type": "array", "description": "Drop rows where any email ends with one of these domains. Always enforced. Common blocklist: `mailinator.com`, `guerrillamail.com`, `example.com`.", "items": { "type": "string" } }, "globalKeywordsAny": { "title": "πŸ”Ž Smart keyword search β€” any-of", "type": "array", "description": "Keep rows where **any** of these keywords appears in **any** of the searched fields. Uses the active *Text match mode* (supports regex). Examples: `patent`, `intellectual property`.", "items": { "type": "string" } }, "globalKeywordsExclude": { "title": "🧹 Smart keyword β€” exclude", "type": "array", "description": "Drop rows where any of these keywords appears in any searched field. Always enforced. Great for suppressing known irrelevant niches.", "items": { "type": "string" } }, "dedupeByField": { "title": "♻️ Primary dedupe key", "enum": [ "none", "linkedin_username", "email", "work_email", "id", "full_name", "phone" ], "type": "string", "description": "Pick the best unique identifier for your audience. `linkedin_username` is usually safest; `email` is great for deliverability lists.", "default": "none" }, "secondaryDedupeField": { "title": "♻️ Secondary dedupe key (fallback)", "enum": [ "none", "linkedin_username", "email", "work_email", "id", "full_name", "phone" ], "type": "string", "description": "When the primary key is missing or empty on a row, fall back to this key so you don't lose records that lack (e.g.) a LinkedIn handle but do have an email.", "default": "none" }, "fieldsToKeep": { "title": "🧾 Fields to keep (allow-list)", "type": "array", "description": "If non-empty, only these top-level keys are emitted. Nested paths are **not** supported here β€” use *Fields to omit* for surgical removal. Example keys: `id`, `full_name`, `email`, `linkedin_username`, `country`, `job_title`.", "items": { "type": "string" } }, "fieldsToOmit": { "title": "βœ‚οΈ Fields to omit (deny-list)", "type": "array", "description": "Strip these top-level keys from every emitted record. Applied *after* `fieldsToKeep`. Example keys: `phone_numbers`, `street_addresses`.", "items": { "type": "string" } }, "redactPii": { "title": "πŸ›‘οΈ Redact PII (emails & phones)", "type": "boolean", "description": "Masks emails as `j***@domain.com` and keeps only the last 4 digits of phone numbers. Useful when sharing samples with contractors or posting demos publicly. Does **not** affect filtering β€” only the output.", "default": false }, "flattenNestedFields": { "title": "πŸ“ Flatten nested fields", "type": "boolean", "description": "Expose the most-used nested bits at the top level: `first_name`, `last_name`, `gender`, `primary_city`, `primary_region`, `primary_continent`, `current_company_name`, `primary_email_domain`. Original nested objects remain, unless you also use *Fields to omit*.", "default": false } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```