Research LinkedIn profiles and write personalized messages for any LinkedIn message type — connection requests, InMails, DMs, message requests, post comments, and comment replies. Takes LinkedIn URLs as input, researches each person (profile data + recent posts via Apify), and generates messages tailored to each lead's background, interests, and recent activity. Exports tool-ready CSVs for Dripify, Expandi, Botdog, PhantomBuster, or generic format. No LinkedIn cookies or login required.
日本語の概要は準備中です。原文の説明を表示しています。
gooseworks-ai/goose-skills☆ 1,2522026年10月15日 更新
End-to-end news-triggered signal composite. Takes any piece of news — an article, LinkedIn post, tweet, announcement, event, trend, regulation, product launch, acquisition, layoff, expansion, or any other public event — and evaluates whether the companies or people mentioned are ICP fits. If yes, identifies the connection between the news and your product, finds the right people to contact, and drafts personalized outreach using the news as the hook. Tool-agnostic. Accepts both company-level and person-level news triggers. AUTO-TRIGGER: Load this composite whenever a user shares a URL (LinkedIn post, article, tweet, blog post) or mentions a company/person they "came across", "saw", or "found" from any external source and asks about relevance, fit, ICP match, or whether to reach out. The user does NOT need to explicitly say "outreach" — any signal evaluation request from an external source triggers this.
日本語の概要は準備中です。原文の説明を表示しています。
gooseworks-ai/goose-skills☆ 1,2522026年10月15日 更新
End-to-end leadership change signal composite. Takes any set of companies, detects recent leadership changes (new VP+, C-suite hires and promotions), evaluates relevance to your product, and drafts personalized outreach. Uses Apollo People Search (free) for fast detection + Apollo Enrichment (1 credit/person) for employment history, start dates, LinkedIn URLs, and verified emails.
日本語の概要は準備中です。原文の説明を表示しています。
gooseworks-ai/goose-skills☆ 1,2522026年10月15日 更新
Find decision-makers at a specific company using Apollo, Crustdata, Fiber, and PDL people search via Gooseworks MCP. Given a company name and target titles, returns a list of contacts with name, title, LinkedIn URL, and location.
日本語の概要は準備中です。原文の説明を表示しています。
gooseworks-ai/goose-skills☆ 1,2522026年10月15日 更新
Evaluate a website, landing page, content, or any online asset through the eyes of pre-built synthetic ICP personas. Loads personas from buyer-persona-generator output, then runs them against target URLs. Supports three modes: structured scorecard, freeform focus group, and head-to-head competitive comparison. Reusable — run against the same site after changes, or against new content anytime.
日本語の概要は準備中です。原文の説明を表示しています。
gooseworks-ai/goose-skills☆ 1,2522026年10月15日 更新
Two-phase Apollo.io prospecting: free People Search to discover ICP-matching leads, then selective enrichment to reveal emails/phones (credits per contact). Creates Apollo lists. Deduplicates against existing contacts by LinkedIn URL.
日本語の概要は準備中です。原文の説明を表示しています。
gooseworks-ai/goose-skills☆ 1,2522026年10月15日 更新
Extract commenters from LinkedIn posts via Apify. Returns commenter names, titles, LinkedIn profile URLs, and comment text. Use to find warm leads engaging with relevant discussions. No LinkedIn cookies required.
日本語の概要は準備中です。原文の説明を表示しています。
gooseworks-ai/goose-skills☆ 1,2522026年10月15日 更新
Search for job postings across LinkedIn and Indeed. Use when users want to find open roles, monitor hiring signals, identify companies hiring for specific positions, or research competitor hiring activity. Returns job title, company, location, salary, description, seniority level, and direct apply URLs. No login or cookies required.
日本語の概要は準備中です。原文の説明を表示しています。
gooseworks-ai/goose-skills☆ 1,2522026年10月15日 更新
Research a company or idea, define the Ideal Customer Profile, and route to the right next step — either mapping the TAM or finding leads/prospects directly. The entry point for any "find me leads", "map my market", or "who should I sell to" request. Auto-loads when a user provides a company URL or idea and asks for leads or market mapping.
日本語の概要は準備中です。原文の説明を表示しています。
gooseworks-ai/goose-skills☆ 1,2522026年10月15日 更新
Find leads by scraping engagers from a competitor's top LinkedIn posts. Given one or more company page URLs, scrapes recent posts, ranks by engagement, selects the top N, extracts all reactors and commenters, ICP-classifies, and exports CSV. Use when someone wants to "find leads engaging with competitor content" or "scrape people who interact with [company]'s LinkedIn posts".
日本語の概要は準備中です。原文の説明を表示しています。
gooseworks-ai/goose-skills☆ 1,2522026年10月15日 更新
Scrapes LinkedIn job postings using the JobSpy library (python-jobspy). Use this skill whenever the user wants to find jobs on LinkedIn, search for open roles, pull job listings, build a job pipeline, source job targets for GTM research, or monitor hiring signals. Even if the user just says "find me some jobs" or "what roles is [company] hiring for", use this skill. It runs a local Python script that outputs a CSV of job postings with title, company, location, salary, job type, description, and direct URLs.
日本語の概要は準備中です。原文の説明を表示しています。
gooseworks-ai/goose-skills☆ 1,2522026年10月15日 更新
Track all identified/contacted people across strategies. CSV-backed contact database with dedup by LinkedIn URL or email. Prevents duplicate outreach when running strategies on a recurring cadence.
日本語の概要は準備中です。原文の説明を表示しています。
gooseworks-ai/goose-skills☆ 1,2522026年10月15日 更新
Enrich any person or company from any identifier — email, name, LinkedIn URL, domain, company name, Twitter/X handle. Use when asked to enrich, look up, or research a lead, contact, person, or company.
日本語の概要は準備中です。原文の説明を表示しています。
gooseworks-ai/goose-skills☆ 1,2522026年10月15日 更新
Scrape competitor ads from Meta's Ad Library (Facebook, Instagram, Messenger, Threads, WhatsApp). Search by company name, Facebook Page URL, or keyword. Returns ad creatives, spend estimates, reach, impressions, and campaign details. Use for competitive ad research, messaging analysis, and creative inspiration.
日本語の概要は準備中です。原文の説明を表示しています。
gooseworks-ai/goose-skills☆ 1,2522026年10月15日 更新
Render a punchy ~12s vertical (9:16) music-only direct-response OFFER ad as a 4-beat kinetic-typography film — HEADLINE slam → real PRODUCT drop → CLAIM/proof → CTA pill — from one config of copy slots, a real product photo, a brand palette, fonts, bpm, and beat split. DETERMINISTIC + FREE (a bundled Remotion project; springs + interpolate, no AI-gen for visuals). Backgrounds are engine gradient divs off the palette, props are inline SVG, the ONLY composited bitmap is the REAL product photo (objectFit:contain, never stretched), and ALL headline/claim/CTA/URL/wordmark text is typeset in the engine — never AI-rendered (the format's credibility guard). A driver binds the config to Remotion input props, renders the 9:16 master, and derives a 1:1 center-crop with ffmpeg. Two gating checks run before render (claim verbs must match the product's physical format; the claim beat needs an edge-entry mechanism prop). Use for the motion-graphics-offer-ad format.
日本語の概要は準備中です。原文の説明を表示しています。
gooseworks-ai/goose-skills☆ 1,2522026年10月15日 更新
Extract and normalize pricing tiers from any SaaS, API, cloud, or LLM vendor's pricing page. Use this skill whenever the user says "pricing for X", "how much does X cost", "pricing tiers", "cost comparison", provides a URL ending in `/pricing` or `/plans`, or asks to monitor pricing over time. Pairs well with `exportSkill` to turn a run into a cron-friendly workflow. Scrape-driven; no interact needed for typical pricing pages.
日本語の概要は準備中です。原文の説明を表示しています。
firecrawl/web-agent☆ 1,2472026年4月20日 更新
Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.
日本語の概要は準備中です。原文の説明を表示しています。
GPTomics/bioSkills☆ 1,2232026年8月15日 更新
Bulk-query Ensembl BioMart (and other BioMart instances) for cross-database ID mapping, gene/transcript/exon coordinates, and ortholog tables. Use when batch-converting Ensembl IDs to other namespaces (HGNC, RefSeq, UniProt, Entrez), pulling gene coordinate tables for thousands of genes, building ortholog wide-tables across species, or replacing slow Ensembl REST loops with one-shot bulk export. Encodes BioMart's XML query format, R biomaRt vs Python pybiomart trade-off, mart-vs-dataset hierarchy, and the URL endpoint that's BioMart-specific (separate from rest.ensembl.org).
日本語の概要は準備中です。原文の説明を表示しています。
GPTomics/bioSkills☆ 1,2232026年8月15日 更新
Query UniProt's REST API (post-2022 endpoint at rest.uniprot.org) for protein sequences, annotations, GO terms, cross-references, ID mappings, and proteomes. Use when fetching UniProtKB entries, navigating the JSON schema, choosing between UniProtKB/UniRef/UniParc/Proteomes resources, deciding stream vs search endpoint for batch retrieval, running ID-mapping jobs with the async pattern, handling isoform suffixes, or filtering reviewed Swiss-Prot vs auto-annotated TrEMBL. Encodes the legacy URL migration (2022), the new JSON schema layout, and bulk-pull patterns.
日本語の概要は準備中です。原文の説明を表示しています。
GPTomics/bioSkills☆ 1,2232026年8月15日 更新
Use when implementing or debugging ANY network connection, API call, or socket in an Apple app. Covers URLSession, Network.framework, NetworkConnection, connection diagnostics.
日本語の概要は準備中です。原文の説明を表示しています。
CharlesWiltgen/Axiom☆ 1,2082026年10月14日 更新
Generate a high-quality deep-reading note for a single paper and write it into an Obsidian-style vault. Use when the user gives a paper title, DOI, URL, arXiv ID, Zotero item, or local PDF and wants a polished Markdown note with strong structure, evidence-based analysis, and figure placeholders.
日本語の概要は準備中です。原文の説明を表示しています。
917Dhj/DeepPaperNote☆ 1,2042026年10月13日 更新
Fetch WeatherKit current, minute, hourly, and daily forecasts; weather alerts; iOS 18+ changes, historical comparisons, summaries, and statistics; and required Apple Weather attribution. Use when integrating weather data, showing forecasts or alerts, caching WeatherKit responses, displaying attribution, or reviewing WeatherKit query limits in iOS apps.
日本語の概要は準備中です。原文の説明を表示しています。
dpearson2699/swift-ios-skills☆ 1,1992026年8月1日 更新
Formats and parses locale-aware numbers, decimals, currencies, percentages, dates, durations, measurements, names, lists, byte counts, and URLs with Foundation FormatStyle and ParseableFormatStyle. Use when replacing legacy Formatter subclasses, designing reusable format APIs, using SwiftUI Text(_:format:), correcting availability, or testing user-facing formatted output across locales.
日本語の概要は準備中です。原文の説明を表示しています。
dpearson2699/swift-ios-skills☆ 1,1992026年8月1日 更新
Implement SwiftUI navigation patterns including NavigationStack, NavigationSplitView, sheet presentation, tab-based navigation, and deep linking. Use when building push navigation, programmatic routing, multi-column layouts, modal sheets, tab bars, universal links, or custom URL scheme handling.
日本語の概要は準備中です。原文の説明を表示しています。
dpearson2699/swift-ios-skills☆ 1,1992026年8月1日 更新