[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-apify-apify-financial-osint":3,"mdc-626cch-key":37,"related-org-apify-apify-financial-osint":3318,"related-repo-apify-apify-financial-osint":3464},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":26,"repoUrl":27,"updatedAt":28,"license":29,"forks":30,"topics":31,"repo":32,"sourceUrl":35,"mdContent":36},"apify-financial-osint","gather social listening signals for portfolio companies","Social-listening signals for tracked portfolio companies via Apify Actors — Reddit sentiment (fatihtahta), Twitter\u002FX real-time mentions (kaitoeasyapi pay-per-result), Trustpilot service quality (getwally.net). Use when the user asks for sentiment, social media mentions, customer reviews, brand perception, crisis signals, OSINT, social listening, \"what are people saying about X\". Reads tracked companies from data\u002Fcompanies.json. Do NOT use for news (use apify-financial-news) or registry lookups (use apify-public-registries).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"apify","Apify","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fapify.png",[12,16,17,20,23],{"name":13,"slug":14,"type":15},"Research","research","tag",{"name":9,"slug":8,"type":15},{"name":18,"slug":19,"type":15},"Marketing","marketing",{"name":21,"slug":22,"type":15},"Web Scraping","web-scraping",{"name":24,"slug":25,"type":15},"Sales","sales",227,"https:\u002F\u002Fgithub.com\u002Fapify\u002Fawesome-skills","2026-05-23T06:06:04.102658",null,56,[],{"repoUrl":27,"stars":26,"forks":30,"topics":33,"description":34},[],"Community collection of Apify agent skills for AI coding assistants","https:\u002F\u002Fgithub.com\u002Fapify\u002Fawesome-skills\u002Ftree\u002FHEAD\u002Fskills\u002Fapify-financial-services\u002Fskills\u002Fapify-financial-osint","---\nname: apify-financial-osint\ndescription: Social-listening signals for tracked portfolio companies via Apify Actors — Reddit sentiment (fatihtahta), Twitter\u002FX real-time mentions (kaitoeasyapi pay-per-result), Trustpilot service quality (getwally.net). Use when the user asks for sentiment, social media mentions, customer reviews, brand perception, crisis signals, OSINT, social listening, \"what are people saying about X\". Reads tracked companies from data\u002Fcompanies.json. Do NOT use for news (use apify-financial-news) or registry lookups (use apify-public-registries).\nauthor: chocholous\nauthor_url: https:\u002F\u002Fgithub.com\u002Fchocholous\n---\n\n# Financial OSINT — Social Listening\n\nDiscover and quantify what the internet is saying about portfolio companies. Three verified Apify Actors only — Reddit (sentiment + threaded discussion), Twitter\u002FX (real-time mentions, crisis monitoring), Trustpilot (customer satisfaction). All actors verified against real demo data with ≥98% success rate.\n\n## Prerequisites\n\n- Apify access — preferred: `apify` CLI (`npm install -g apify-cli && apify login`); fallback: Apify MCP connector (`call-actor` tool). CLI is faster and preferred when both are available.\n- Companies data at `${CLAUDE_PLUGIN_ROOT}\u002Fdata\u002Fcompanies.json` (read fields: `queries.reddit`, `queries.twitter`, `trustpilot_urls`, `identifiers.ticker`)\n- Per-company routing pre-computed at `${CLAUDE_PLUGIN_ROOT}\u002Fskills\u002Fapify-financial-osint\u002Fdata\u002Fosint-targets.json`\n\n`${CLAUDE_PLUGIN_ROOT}` is the plugin's root directory (where `.claude-plugin\u002F` lives). It is resolved automatically by Claude Code when the plugin is installed, or set to the `--plugin-dir` path during development.\n\n## Workflow checklist\n\nCopy this and tick boxes as you progress:\n\n```\nTask Progress:\n- [ ] Step 0: Verify Apify access — try `apify --version && apify info`; if unavailable, check for `call-actor` MCP tool; if neither, tell user to install apify CLI or Apify MCP connector\n- [ ] Step 1: Pick actor(s) by signal type — see \"Choose Actor by Signal\" table\n- [ ] Step 2: Build input — read data\u002Fosint-targets.json or construct from data\u002Fcompanies.json\n- [ ] Step 3: Run actor via apify CLI\n- [ ] Step 4: Output — present top results with sentiment + engagement signals\n```\n\n## Constraints\n\n### Allowed Apify Actors (exhaustive — do NOT use others)\n\n| Actor | Purpose | Cost | Success rate |\n|-------|---------|------|--------------|\n| `fatihtahta\u002Freddit-scraper-search-fast` | Reddit sentiment, acquisition reactions, brand perception | $1.49 \u002F 1k results | 98.4% (40,787 runs\u002F30d) |\n| `kaitoeasyapi\u002Ftwitter-x-data-tweet-scraper-pay-per-result-cheapest` | Real-time mentions, crisis monitoring, dealflow signals | $0.25 \u002F 1k tweets | 99.7% (4.3\u002F5, 58 reviews) |\n| `getwally.net\u002Ftrustpilot-reviews-scraper` | Service quality, complaint patterns (telcos, e-commerce, banks) | $3.00 \u002F 1k results | verified working |\n\nDo NOT use any other actor. Do NOT use WebSearch, WebFetch, or browser tools.\n\n### Choose Actor by Signal\n\n| If you need | Use Actor | When NOT to use |\n|---|---|---|\n| Sentiment \u002F discussion threads \u002F reactions to corporate events | `fatihtahta\u002Freddit-scraper-search-fast` | If company has no consumer base (B2B fintech, biotech) — expect \u003C5 posts |\n| Real-time mentions \u002F crisis signals \u002F dealflow chatter | `kaitoeasyapi\u002Ftwitter-x-data-tweet-scraper-pay-per-result-cheapest` | If you need >1 week historical depth — Twitter API limits |\n| Customer satisfaction \u002F service quality complaints | `getwally.net\u002Ftrustpilot-reviews-scraper` | If company has no Trustpilot page (B2B, holding companies) — see verified URL list in `reference\u002Fosint-actor-schemas.md` Section 3 |\n\n## Pipeline\n\n### Step 1: Pick actor(s)\n\nFor portfolio companies, look up the company in [`data\u002Fosint-targets.json`](data\u002Fosint-targets.json) — it pre-computes which actors to run with templated inputs. Routing rule (mirrors how the file was built):\n\n- `queries.reddit` non-empty → run Reddit actor\n- `queries.twitter` non-empty → run Twitter actor (always set for tracked companies)\n- `trustpilot_urls` non-empty → run Trustpilot actor\n\nFor ad-hoc \u002F non-portfolio targets, construct input from scratch (see Step 2).\n\n### Step 2: Build input\n\n#### Reddit input (key fields)\n\n| Field | Type | Default | Notes |\n|---|---|---|---|\n| `queries` | array of string | required (one of queries \u002F urls \u002F subredditName) | Global Reddit-wide search terms. |\n| `maxPosts` | integer | **50000** (!) | **ALWAYS set explicitly** — typical 30-50 for scans, 100-200 for deep-dives. |\n| `scrapeComments` | boolean | `false` | Set `true` to extract threaded discussion. |\n| `maxComments` | integer | 50000 (!) | Only used when `scrapeComments: true`. Typical 5–10. |\n| `sort` | enum | `\"relevance\"` | One of `relevance`, `hot`, `top`, `new`, `comments`. (NOT `rising` \u002F `best`.) |\n| `timeframe` | enum | `\"all\"` | One of `all`, `year`, `month`, `week`, `day`, `hour`. Must be >= dateFrom–dateTo range. |\n| `dateFrom` | string | — | `YYYY-MM-DD`. Post-fetch filter: keep posts from this date onward. |\n| `dateTo` | string | — | `YYYY-MM-DD`. Post-fetch filter: keep posts up to this date. |\n\nExample:\n\n```bash\napify call fatihtahta\u002Freddit-scraper-search-fast \\\n  --input '{\"queries\":[\"InPost FedEx acquisition\"],\"maxPosts\":50,\"scrapeComments\":true,\"maxComments\":10,\"sort\":\"relevance\",\"timeframe\":\"month\"}' \\\n  --user-agent apify-awesome-skills\u002Fapify-financial-osint\n```\n\n#### Twitter\u002FX input (key fields)\n\n| Field | Type | Default | Notes |\n|---|---|---|---|\n| `twitterContent` | string | — | One of `twitterContent` \u002F `tweetIDs` \u002F `searchTerms`. Twitter advanced-search syntax (`OR`, `-`, `from:`, `since:`). |\n| `tweetIDs` | array of string | — | **Plural** — not `tweetId`. |\n| `searchTerms` | array of string | — | Each term gets `maxItems` results independently. |\n| `maxItems` | integer | 200 | **REQUIRED** — actor fails without it. Pay-per-result. |\n| `queryType` | enum | `\"Latest\"` | One of `Latest`, `Top`, `Photos`, `Videos`. |\n| `lang` | string | `\"en\"` | ISO 639-1. Set `cs`\u002F`pl`\u002F`hu`\u002F`bg`\u002F`sk`\u002F`tr` for single-country B2C; omit for multilingual. |\n| `since` \u002F `until` | string | — | Format: `YYYY-MM-DD_HH:MM:SS_UTC` (NOT ISO 8601). |\n| `filter:news` \u002F `filter:media` \u002F `min_faves` \u002F `min_retweets` | various | — | Engagement \u002F content filters. |\n\nExample:\n\n```bash\napify call kaitoeasyapi\u002Ftwitter-x-data-tweet-scraper-pay-per-result-cheapest \\\n  --input '{\"twitterContent\":\"InPost FedEx acquisition OR INPST\",\"maxItems\":100,\"queryType\":\"Latest\",\"since\":\"2026-01-01_00:00:00_UTC\",\"filter:news\":true}' \\\n  --user-agent apify-awesome-skills\u002Fapify-financial-osint\n```\n\n#### Trustpilot input (only 2 fields exist!)\n\n| Field | Type | Required | Notes |\n|---|---|---|---|\n| `startUrls` | array of `{\"url\": \"...\"}` objects | Yes | **NOT plain strings** — array of objects. |\n| `limit` | integer | No (default 1000) | Set lower to control cost ($3\u002F1k). |\n\nExample:\n\n```bash\napify call getwally.net\u002Ftrustpilot-reviews-scraper \\\n  --input '{\"startUrls\":[{\"url\":\"https:\u002F\u002Fwww.trustpilot.com\u002Freview\u002Finpost.pl\"}],\"limit\":50}' \\\n  --user-agent apify-awesome-skills\u002Fapify-financial-osint\n```\n\nOlder docs reference fields like `maxItems`, `includeStatistics`, `includeCompanyDetails`, `onlyNewerThan` — these **do NOT exist** on this actor.\n\n### Step 3: Cost-bound the run\n\nAlways cap output before running. Defaults are dangerously high.\n\n| Actor | Cap field | Portfolio scan | Deep-dive |\n|---|---|---|---|\n| Reddit | `maxPosts` | 30–50 | 100–200 |\n| Reddit comments | `maxComments` | 5–10 (only if `scrapeComments: true`) | 20–50 |\n| Twitter\u002FX | `maxItems` | 50–100 | 200–500 |\n| Trustpilot | `limit` | 30–50 | 100–200 |\n\nTwitter and Trustpilot are pay-per-result — every returned item is billed.\n\n### Step 4: Run\n\nSingle example pulling Reddit threads + Twitter mentions for InPost (driven by [`data\u002Fosint-targets.json`](data\u002Fosint-targets.json)):\n\n```bash\napify call fatihtahta\u002Freddit-scraper-search-fast \\\n  --input \"$(jq -c '.targets[] | select(.company_id==\"inpost\") | .inputs.reddit' \\\n    ${CLAUDE_PLUGIN_ROOT}\u002Fskills\u002Fapify-financial-osint\u002Fdata\u002Fosint-targets.json)\" \\\n  --user-agent apify-awesome-skills\u002Fapify-financial-osint \\\n  --output-dataset > reddit_inpost.json\n```\n\nFull per-actor input schema (all 51 Twitter properties, every Reddit enum, every Trustpilot edge case) plus 30+ example invocations: [reference\u002Fosint-actor-schemas.md](reference\u002Fosint-actor-schemas.md).\n\n### Step 4b: Post-filter Reddit results\n\nReddit search ignores quotes and matches partial words (\"InPost\" matches \"in post game thread\"). After fetching, filter results client-side: keep only posts where any of the company's search queries appears as a whole word (case-insensitive) in `title` or `body`. Use the `queries` array from `data\u002Fosint-targets.json` for matching (these are the terms the company is actually known by). Normalise diacritics before comparing (Š↔S, ö↔o, etc.).\n\nExpect 90–95% of raw Reddit results to be false positives. This is normal — `maxPosts` is set to 200 to compensate.\n\n### Step 5: Output\n\nKey output fields per actor:\n\n| Actor | Date field | Date format | URL field | Engagement fields |\n|---|---|---|---|---|\n| Reddit | `created_utc` | ISO 8601 (`2026-05-01T17:26:41.000Z`) | `canonical_url` | `score`, `num_comments` |\n| Twitter | `createdAt` | Non-standard (`Fri May 01 17:35:21 +0000 2026`) | `url` | `likeCount`, `retweetCount`, `replyCount` |\n| Trustpilot | `date` | ISO 8601 (`2026-01-27T21:53:45.000Z`) | `url` (review ID, not company page) | `ratingValue` (string \"1\"–\"5\") |\n\nPresent top results with:\n\n- **Sentiment hint** (positive \u002F negative \u002F neutral) where derivable from text\n- **Engagement** — see table above\n- **Author \u002F handle**\n- **Date** — normalise to YYYY-MM-DD for display\n- **Permalink**\n\nExample output for a sentiment scan:\n\n```markdown\n## OSINT Scan: InPost — Last 30 days\n\n### Reddit (3 posts, 47 comments analyzed)\n| Title | Subreddit | Score | Sentiment | Date | URL |\n|---|---|---|---|---|---|\n| InPost lockers in UK getting better? | r\u002Funitedkingdom | 124 | positive | 2026-04-12 | … |\n| Anyone else missing parcels? | r\u002Fpoland | 38 | negative | 2026-04-09 | … |\n\n### Twitter\u002FX (87 tweets)\n| Tweet (truncated) | Author | Likes | Replies | Date | URL |\n|---|---|---|---|---|---|\n| FedEx-InPost rollout looks promising… | @logistics_eu | 412 | 27 | 2026-04-22 | … |\n\n### Trustpilot (50 reviews — avg 3.2 \u002F 5)\n| Rating | Title | Author | Date | URL |\n|---|---|---|---|---|\n| 5 | Good system, very efficient | Yeison S. | 2026-03-10 | … |\n| 1 | Parcel never delivered | Anna K. | 2026-04-18 | … |\n```\n\n## Per-company routing\n\n[`data\u002Fosint-targets.json`](data\u002Fosint-targets.json) maps each portfolio company → which OSINT actors to run, with pre-built input templates derived from `data\u002Fcompanies.json`. Coverage as of v1.0: 31 entries (30 portfolio + group), Reddit 27, Twitter 31, Trustpilot 5, all-three 5, Twitter-only 4. Empty-actor entries reflect verified absence (e.g., MONETA \u002F CETIN \u002F SOTIO have no Trustpilot page).\n\n## Critical gotchas (high-frequency mistakes)\n\n- **Reddit `maxPosts` default is 50000** — ALWAYS set explicitly (typical: 30–50 for scans).\n- **Reddit `maxComments` default is 50000** — set low whenever `scrapeComments: true`.\n- **Reddit `subredditKeywords` is an array**, not a string.\n- **Reddit `sort` enum has no `\"rising\"` \u002F `\"best\"`** — only relevance, hot, top, new, comments.\n- **Reddit `includeNsfw`** — lowercase \"sfw\" (not `includeNSFW`).\n- **Twitter `maxItems` is REQUIRED** — actor fails without it. Pay-per-result.\n- **Twitter `since` \u002F `until` format is `YYYY-MM-DD_HH:MM:SS_UTC`** (NOT ISO 8601).\n- **Twitter `tweetIDs` is plural array** — not `tweetId`.\n- **Twitter `lang` default is `\"en\"`** — set explicitly or omit for all languages.\n- **Trustpilot `startUrls` must be array of objects** with `url` key — NOT plain strings.\n- **Trustpilot has ONLY 2 input fields** (`startUrls`, `limit`) — older docs reference `maxItems`, `includeStatistics`, `includeCompanyDetails`, `onlyNewerThan` that DO NOT EXIST.\n- **Trustpilot `ratingValue` is a STRING** (\"1\"–\"5\"), not integer — parse before aggregating.\n- **Trustpilot has no date filter** — actor returns most recent first up to `limit`; post-filter by `date` field.\n- **Trustpilot URLs verified per company** — see \"Known Trustpilot URLs\" table in `reference\u002Fosint-actor-schemas.md` Section 3. Some companies have NO Trustpilot page (B2B holdings, biotech) — running the actor returns 0 reviews.\n\nFull per-actor schemas + 30+ example invocations: [reference\u002Fosint-actor-schemas.md](reference\u002Fosint-actor-schemas.md).\n\n## Reference\n\n- [data\u002Fosint-targets.json](data\u002Fosint-targets.json) — per-company routing + pre-built actor inputs (31 entries)\n- [reference\u002Fosint-actor-schemas.md](reference\u002Fosint-actor-schemas.md) — verbatim source: all 4 actor schemas with portfolio examples (Section 4 Economic Calendar archived; out of scope for this skill — see PLAN.md)\n- Shared portfolio data: [`${CLAUDE_PLUGIN_ROOT}\u002Fdata\u002Fcompanies.json`](..\u002F..\u002Fdata\u002Fcompanies.json)\n",{"data":38,"body":41},{"name":4,"description":6,"author":39,"author_url":40},"chocholous","https:\u002F\u002Fgithub.com\u002Fchocholous",{"type":42,"children":43},"root",[44,53,59,66,154,181,187,192,204,210,217,336,341,347,445,451,457,474,507,512,518,525,913,918,998,1004,1422,1426,1488,1494,1589,1593,1655,1695,1701,1706,1845,1850,1856,1871,2004,2014,2020,2055,2067,2073,2078,2276,2281,2330,2335,2906,2912,2933,2939,3261,3271,3277,3312],{"type":45,"tag":46,"props":47,"children":49},"element","h1",{"id":48},"financial-osint-social-listening",[50],{"type":51,"value":52},"text","Financial OSINT — Social Listening",{"type":45,"tag":54,"props":55,"children":56},"p",{},[57],{"type":51,"value":58},"Discover and quantify what the internet is saying about portfolio companies. Three verified Apify Actors only — Reddit (sentiment + threaded discussion), Twitter\u002FX (real-time mentions, crisis monitoring), Trustpilot (customer satisfaction). All actors verified against real demo data with ≥98% success rate.",{"type":45,"tag":60,"props":61,"children":63},"h2",{"id":62},"prerequisites",[64],{"type":51,"value":65},"Prerequisites",{"type":45,"tag":67,"props":68,"children":69},"ul",{},[70,100,143],{"type":45,"tag":71,"props":72,"children":73},"li",{},[74,76,82,84,90,92,98],{"type":51,"value":75},"Apify access — preferred: ",{"type":45,"tag":77,"props":78,"children":80},"code",{"className":79},[],[81],{"type":51,"value":8},{"type":51,"value":83}," CLI (",{"type":45,"tag":77,"props":85,"children":87},{"className":86},[],[88],{"type":51,"value":89},"npm install -g apify-cli && apify login",{"type":51,"value":91},"); fallback: Apify MCP connector (",{"type":45,"tag":77,"props":93,"children":95},{"className":94},[],[96],{"type":51,"value":97},"call-actor",{"type":51,"value":99}," tool). 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field.",{"type":45,"tag":71,"props":3245,"children":3246},{},[3247,3252,3254,3259],{"type":45,"tag":605,"props":3248,"children":3249},{},[3250],{"type":51,"value":3251},"Trustpilot URLs verified per company",{"type":51,"value":3253}," — see \"Known Trustpilot URLs\" table in ",{"type":45,"tag":77,"props":3255,"children":3257},{"className":3256},[],[3258],{"type":51,"value":442},{"type":51,"value":3260}," Section 3. Some companies have NO Trustpilot page (B2B holdings, biotech) — running the actor returns 0 reviews.",{"type":45,"tag":54,"props":3262,"children":3263},{},[3264,3266,3270],{"type":51,"value":3265},"Full per-actor schemas + 30+ example invocations: ",{"type":45,"tag":463,"props":3267,"children":3268},{"href":442},[3269],{"type":51,"value":442},{"type":51,"value":1142},{"type":45,"tag":60,"props":3272,"children":3274},{"id":3273},"reference",[3275],{"type":51,"value":3276},"Reference",{"type":45,"tag":67,"props":3278,"children":3279},{},[3280,3289,3298],{"type":45,"tag":71,"props":3281,"children":3282},{},[3283,3287],{"type":45,"tag":463,"props":3284,"children":3285},{"href":465},[3286],{"type":51,"value":465},{"type":51,"value":3288}," — per-company routing + pre-built actor inputs (31 entries)",{"type":45,"tag":71,"props":3290,"children":3291},{},[3292,3296],{"type":45,"tag":463,"props":3293,"children":3294},{"href":442},[3295],{"type":51,"value":442},{"type":51,"value":3297}," — verbatim source: all 4 actor schemas with portfolio examples (Section 4 Economic Calendar archived; out of scope for this skill — see PLAN.md)",{"type":45,"tag":71,"props":3299,"children":3300},{},[3301,3303],{"type":51,"value":3302},"Shared portfolio data: ",{"type":45,"tag":463,"props":3304,"children":3306},{"href":3305},"..\u002F..\u002Fdata\u002Fcompanies.json",[3307],{"type":45,"tag":77,"props":3308,"children":3310},{"className":3309},[],[3311],{"type":51,"value":110},{"type":45,"tag":3313,"props":3314,"children":3315},"style",{},[3316],{"type":51,"value":3317},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":3319,"total":2596},[3320,3337,3351,3366,3388,3398,3413,3426,3434,3447],{"slug":3321,"name":3321,"fn":3322,"description":3323,"org":3324,"tags":3325,"stars":3334,"repoUrl":3335,"updatedAt":3336},"apify-actor-development","build and deploy Apify Actors","Develop, debug, and deploy Apify Actors - serverless cloud programs for web scraping, automation, and data processing. Use when creating new Actors, modifying existing ones, or troubleshooting Actor code.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3326,3327,3330,3333],{"name":9,"slug":8,"type":15},{"name":3328,"slug":3329,"type":15},"Automation","automation",{"name":3331,"slug":3332,"type":15},"Serverless","serverless",{"name":21,"slug":22,"type":15},2231,"https:\u002F\u002Fgithub.com\u002Fapify\u002Fagent-skills","2026-04-06T18:00:48.720637",{"slug":3338,"name":3338,"fn":3339,"description":3340,"org":3341,"tags":3342,"stars":3334,"repoUrl":3335,"updatedAt":3350},"apify-actorization","convert projects into Apify Actors","Convert existing projects into Apify Actors - serverless cloud programs. Actorize JavaScript\u002FTypeScript (SDK with Actor.init\u002Fexit), Python (async context manager), or any language (CLI wrapper). Use when migrating code to Apify, wrapping CLI tools as Actors, or adding Actor SDK to existing projects.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3343,3344,3345,3348,3349],{"name":9,"slug":8,"type":15},{"name":3328,"slug":3329,"type":15},{"name":3346,"slug":3347,"type":15},"Migration","migration",{"name":3331,"slug":3332,"type":15},{"name":21,"slug":22,"type":15},"2026-04-06T18:00:46.151104",{"slug":3352,"name":3352,"fn":3353,"description":3354,"org":3355,"tags":3356,"stars":3334,"repoUrl":3335,"updatedAt":3365},"apify-generate-output-schema","generate Apify Actor output schemas","Generate output schemas (dataset_schema.json, output_schema.json, key_value_store_schema.json) for an Apify Actor by analyzing its source code. Use when creating or updating Actor output schemas.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3357,3360,3361,3364],{"name":3358,"slug":3359,"type":15},"API Development","api-development",{"name":9,"slug":8,"type":15},{"name":3362,"slug":3363,"type":15},"Data Modeling","data-modeling",{"name":21,"slug":22,"type":15},"2026-04-06T18:00:50.07596",{"slug":3367,"name":3367,"fn":3368,"description":3369,"org":3370,"tags":3371,"stars":3334,"repoUrl":3335,"updatedAt":3387},"apify-sdk-integration","integrate Apify SDK into applications","Integrate Apify into an existing JavaScript\u002FTypeScript or Python application using the apify-client package. Use when adding web scraping, automation, or data extraction capabilities to an existing app via the Apify API.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3372,3373,3374,3377,3380,3383,3386],{"name":9,"slug":8,"type":15},{"name":3328,"slug":3329,"type":15},{"name":3375,"slug":3376,"type":15},"JavaScript","javascript",{"name":3378,"slug":3379,"type":15},"Python","python",{"name":3381,"slug":3382,"type":15},"SDK","sdk",{"name":3384,"slug":3385,"type":15},"TypeScript","typescript",{"name":21,"slug":22,"type":15},"2026-05-16T05:56:15.416714",{"slug":3389,"name":3389,"fn":3390,"description":3391,"org":3392,"tags":3393,"stars":3334,"repoUrl":3335,"updatedAt":3397},"apify-ultimate-scraper","scrape data across web platforms","Universal AI-powered web scraper for any platform. Scrape data from Instagram, Facebook, TikTok, YouTube, LinkedIn, X\u002FTwitter, Google Maps, Google Search, Google Trends, Reddit, Airbnb, Yelp, and 15+ more platforms. Use for lead generation, brand monitoring, competitor analysis, influencer discovery, trend research, content analytics, audience analysis, review analysis, SEO intelligence, recruitment, or any data extraction task.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3394,3395,3396],{"name":9,"slug":8,"type":15},{"name":3328,"slug":3329,"type":15},{"name":21,"slug":22,"type":15},"2026-04-06T18:00:47.419432",{"slug":3399,"name":3399,"fn":3400,"description":3401,"org":3402,"tags":3403,"stars":3410,"repoUrl":3411,"updatedAt":3412},"apify-cli","run and manage Apify Actors via CLI","Patterns for invoking the Apify CLI (`apify`) from agents. Covers authentication, creating\u002Frunning\u002Fpushing Actors, calling Actors in the cloud, and reading results from datasets and key-value stores.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3404,3405,3406,3409],{"name":9,"slug":8,"type":15},{"name":3328,"slug":3329,"type":15},{"name":3407,"slug":3408,"type":15},"CLI","cli",{"name":21,"slug":22,"type":15},232,"https:\u002F\u002Fgithub.com\u002Fapify\u002Fapify-cli","2026-07-02T07:39:43.018333",{"slug":3414,"name":3414,"fn":3415,"description":3416,"org":3417,"tags":3418,"stars":26,"repoUrl":27,"updatedAt":3425},"apify-financial-news","extract financial news for portfolio companies","Discover and extract financial news for tracked portfolio companies across 33 verified Tier 1 sources (Bloomberg, Reuters, FT, WSJ, IntelliNews, ČTK, PAP, BTA, TASR, ING Think, ECB, EC Press Corner, ...) plus broad Google News fallback. Use when the user asks to find news about a company, get press coverage, monitor financial press, run a news scan, or check headlines for a portfolio company. Reads tracked companies from data\u002Fcompanies.json. Do NOT use for marketing\u002Fsocial-listening (use apify\u002Fawesome-skills) or for morning-briefing formatting (out of scope).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3419,3420,3423,3424],{"name":9,"slug":8,"type":15},{"name":3421,"slug":3422,"type":15},"Finance","finance",{"name":13,"slug":14,"type":15},{"name":21,"slug":22,"type":15},"2026-05-23T06:06:02.858883",{"slug":4,"name":4,"fn":5,"description":6,"org":3427,"tags":3428,"stars":26,"repoUrl":27,"updatedAt":28},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3429,3430,3431,3432,3433],{"name":9,"slug":8,"type":15},{"name":18,"slug":19,"type":15},{"name":13,"slug":14,"type":15},{"name":24,"slug":25,"type":15},{"name":21,"slug":22,"type":15},{"slug":3435,"name":3435,"fn":3436,"description":3437,"org":3438,"tags":3439,"stars":26,"repoUrl":27,"updatedAt":3446},"apify-public-registries","lookup company data from European public registries","Look up official company data from European public registries across 11 countries\u002Fregions (CZ, SK, PL, DE, UK, NL, RO, HR, SE + EU-level + ESG). Covers company registration, ownership, financial filings, VAT status, ESG data. Use when the user asks to \"look up a company\", \"check registry\", \"find company info\", \"look up IČO\u002FKRS\u002FLEI\u002FCRN\", \"company due diligence\", \"check VAT status\", \"find ownership structure\", or needs official data from European registries. Reads tracked companies from data\u002Fcompanies.json. Some lookups use Python scripts (stdlib), some fall back to Apify actors for scraping-based registries.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3440,3441,3444,3445],{"name":9,"slug":8,"type":15},{"name":3442,"slug":3443,"type":15},"Compliance","compliance",{"name":3421,"slug":3422,"type":15},{"name":13,"slug":14,"type":15},"2026-05-23T06:06:05.323553",{"slug":3448,"name":3448,"fn":3322,"description":3449,"org":3450,"tags":3451,"stars":3461,"repoUrl":3462,"updatedAt":3463},"apify-agent","Build, run, debug, and deploy Apify Actors. Use when the user wants to create an Actor, mentions apify create \u002F apify run \u002F apify push, or is debugging an Actor's input schema, storages, or deployment.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3452,3453,3454,3457,3460],{"name":9,"slug":8,"type":15},{"name":3328,"slug":3329,"type":15},{"name":3455,"slug":3456,"type":15},"Debugging","debugging",{"name":3458,"slug":3459,"type":15},"Deployment","deployment",{"name":21,"slug":22,"type":15},0,"https:\u002F\u002Fgithub.com\u002Fapify\u002Fapify-agent","2026-07-30T05:53:40.745073",{"items":3465,"total":986},[3466,3473,3481],{"slug":3414,"name":3414,"fn":3415,"description":3416,"org":3467,"tags":3468,"stars":26,"repoUrl":27,"updatedAt":3425},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3469,3470,3471,3472],{"name":9,"slug":8,"type":15},{"name":3421,"slug":3422,"type":15},{"name":13,"slug":14,"type":15},{"name":21,"slug":22,"type":15},{"slug":4,"name":4,"fn":5,"description":6,"org":3474,"tags":3475,"stars":26,"repoUrl":27,"updatedAt":28},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3476,3477,3478,3479,3480],{"name":9,"slug":8,"type":15},{"name":18,"slug":19,"type":15},{"name":13,"slug":14,"type":15},{"name":24,"slug":25,"type":15},{"name":21,"slug":22,"type":15},{"slug":3435,"name":3435,"fn":3436,"description":3437,"org":3482,"tags":3483,"stars":26,"repoUrl":27,"updatedAt":3446},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3484,3485,3486,3487],{"name":9,"slug":8,"type":15},{"name":3442,"slug":3443,"type":15},{"name":3421,"slug":3422,"type":15},{"name":13,"slug":14,"type":15}]