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概要と使いどころ
Test product-demand hypotheses using public customer language, search and social signals, competitor offers, reviews, comments, creator content, and social-commerce evidence before committing to a product, launch, offer, or campaign.
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。
Monitor existing customer accounts for upsell and cross-sell signals: team growth on LinkedIn, new job postings, product usage patterns, funding announcements, and public company news. Produces a weekly expansion opportunity list with context and talk tracks. Chains web search, LinkedIn profile monitoring, and job posting detection.
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。
Aggregate customer feedback from multiple sources — support tickets, NPS comments, Slack messages, G2 reviews, call transcripts, survey responses — into a unified VoC report with theme clustering, sentiment analysis, trend detection, and actionable recommendations for product, marketing, and CS teams. Chains review-site-scraper for public review data.
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。
Pull real SEO metrics for any domain using Apify scrapers for Semrush and Ahrefs data. Gets domain authority, organic traffic estimates, keyword rankings, backlink profiles, top performing pages, and auto-discovers competitors from keyword overlap. No Semrush/Ahrefs subscription needed — uses Apify actors that scrape public 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。
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。
Turn public social videos, transcripts, posts, creator research, or the user's own source material into platform-specific LinkedIn posts, X posts and threads, short-form scripts, newsletters, blog outlines, carousel briefs, and content calendars. Use when the user wants to reuse a strong idea without copying the source.
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。
Creates a new graphics format spec (custom canvas dimensions + content rules) and publishes it to the Gooseworks library so any agent can render against it. Use when the existing community formats (carousel, story, infographic, slides, poster, chart, tweet, plus community additions) don't fit the user's canvas — e.g., LinkedIn banner 1584×396, story cover 1080×1920, event flyer 8.5×11in. Mirrors goose-graphics-create-style but for formats.
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。
Portable visual skill pack for the Agent Skills ecosystem (Claude Code, Claude Desktop, Claude Cowork, Claude Design, Goose, Cursor, Codex). Discovers community-published styles + formats via the gooseworks CLI, runs an extract-style workflow on reference images, and exports rendered PNGs via Playwright.
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。
Find warm leads by searching LinkedIn for pain-language posts — the frustrations, complaints, and operational struggles your ICP talks about publicly. Asks clarifying questions to understand your product, ICP, and their pain points, then generates pain-language search keywords, scrapes LinkedIn for posts and engagers, enriches profiles, and ICP-filters the results. Use when someone wants to "find leads who are complaining about X" or "find people discussing problems we solve" or "LinkedIn pain-based prospecting."
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。
Final QC gate for a rendered 9:16 video ad, run before it is published. One script checks what a machine can decide — exact 1080x1920 size, a hook that moves and speaks in the first second, no frozen stretches, no dead air, no black frames, the brand's real logo on the end card (never a favicon, never a redrawn or wrong logo), end-card colours near the brand palette — and builds one contact sheet (frames with the TikTok/Reels UI safe zones shaded, beside the logo, product images and a font specimen) for the checks that need eyes — font, product likeness, product consistency across scenes, safe zones. A declared silent-text profile covers silent, text-led formats such as kinetic text (no audio track needed, short blank beats between text beats allowed within fixed bounds). Exit 0 PASS / 2 FAIL / 3 ERROR. Use on every finished video master before pinning it.
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。
Take someone from a business goal to a verified, paused-then-live Meta/Facebook/Instagram campaign without making them learn Meta's hierarchy. Use automatically for requests to launch, run, create, publish, set up, or plan Meta ads; use reporting skills for analysis-only requests.
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。
Monitor competitor content across blogs, LinkedIn, and Twitter/X on a recurring basis. Surfaces new posts, trending topics, and content gaps you can own. Chains blog-feed-monitor, linkedin-profile-post-scraper, and twitter-mention-tracker. Use when you want a weekly digest of what competitors are publishing and which topics are generating engagement.
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。
Research a specific competitor across their website, reviews, ads, social presence, and pricing — then produce a structured sales battlecard with positioning traps, objection handlers, landmine questions, and win/loss themes. Chains web research, review mining, and ad intelligence. Use when sales needs competitive ammo or when entering a new market with established incumbents.
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。
Generate a personal voice guide for X (Twitter) and/or LinkedIn by scanning a user's past posts and iteratively refining with sample-and-feedback loops. Produces a structured markdown voice guide that sibling skills (create-x-content, create-linkedin-content) consume to draft in-voice posts. Different from brand-voice-extractor, which analyses company blogs/landing pages — this skill is for personal social voice.
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。
Analyze a company's published content to extract their brand voice, writing style, and tone guidelines. Reads 10-20 of their best content pieces and produces a brand voice profile covering tone, vocabulary level, sentence structure, formatting patterns, CTAs, and target persona. Useful before writing outreach, content, or campaigns that should match a client's existing voice.
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。
Image-to-video (or text-to-video) via any FAL video model (Kling, Seedance, Veo), ROUTED THROUGH THE GooseWorks fal-proxy so the call bills the Ads agent. The template recipe names the model + params; image_url inputs must be public URLs (the orchestrator hosts local frames with the MCP media_upload). Returns the result video URL and downloads it. Use for the generative base clip of any video-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。
Generate or edit an image via any FAL image model (nano-banana edit, gpt-image, flux, ...), ROUTED THROUGH THE fal-proxy so it bills the Ads agent. image_urls must be public URLs; --image puts a local file on the fal CDN first. The recipe names the model + prompt. Use for keyframes, flat-cover transforms, product hero edits. (For the OpenAI gpt-image family specifically, create-image-gpt-image-fal also exists.)
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。
Manages Power BI semantic models, including tables, columns, measures, relationships, field parameters, DAX, Import, DirectQuery and Direct Lake designs, sources, connections, permissions, refresh, and deployment. Load this skill before acting on a semantic model, unless it is part of publishing a PBIP project, which powerbi-report-cli owns. For natural-language data questions use fabriciq; for visuals use powerbi-report-cli.
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。
Pull company financials, SEC filings, and analyst consensus for a public company. Use this skill whenever the user says "10-K", "10-Q", "earnings", "revenue of", "financials for", "analyst rating for", "price target for", or provides a stock ticker. Combines SEC EDGAR for official filings with Yahoo Finance / TipRanks for analyst data. Search + scrape only; no interact needed.
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。
Add, configure, and manage faults on Simulink, Simscape, and System Composer model signals for robustness analysis and safety validation. Use when injecting faults (stuck, noise, gain, offset) onto block inports/outports, enabling fault simulation, or analyzing fault effects. Covers sensor failures, signal corruptions, actuator faults, FMEA validation, and robustness testing. Requires Simulink Fault Analyzer.
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。
Design stable, compatible public APIs using extend-only design principles. Manage API compatibility, wire compatibility, and versioning for NuGet packages and distributed systems.
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。
Understand implementation details of .NET code by decompiling assemblies. Use when you want to see how a .NET API works internally, inspect NuGet package source, view framework implementation, or understand compiled .NET binaries.
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。
Renders Hi-C contact matrices honestly and reproducibly with matplotlib, cooltools, HiCExplorer, pyGenomeTracks, FAN-C, CoolBox, and plotgardener. Covers the raw/ICE-balanced/observed-over-expected transform choice, LogNorm vs symmetric-diverging colormaps with vmax/percentile clipping, resolution-to-feature matching (compartments 100-500kb, TADs 10-40kb, loops 5-10kb), square vs rotated-triangle track-stacking, NaN/white-stripe handling, virtual 4C, APA/saddle/on-diagonal pileups, two-condition side-by-side and log2-ratio maps, and interactive (HiGlass) vs scripted-static publication figures. Use when plotting a contact matrix, choosing a normalization or color scale, building a multi-track Hi-C figure, making a virtual 4C profile, piling up loops/boundaries, or comparing two conditions.
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。
Assembles a genome de novo from Illumina short reads with SPAdes (isolate/careful/sc/meta/plasmid/rna modes), MEGAHIT (low-memory, huge datasets), Unicycler (bacterial finishing/hybrid), MaSuRCA (large hybrid), ABySS (Bloom-filter), and Platanus (heterozygous diploids), using multi-k de Bruijn graphs. Covers the repeat-resolution limit, why N50 plateaus at the genome not the depth, GenomeScope2 k-mer profiling first, the heterozygosity/haplotig trap, error-correction erasing rare alleles, GC dropout, and NG50/auN/BUSCO reporting. Use when assembling a bacterial isolate, fungal, small-eukaryotic, single-cell, or metagenome genome from Illumina reads, or when deciding whether short reads can even produce the assembly being asked for.
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。