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Expert SWIFT Customer Security Programme (CSP) advisor covering the Customer Security Controls Framework (CSCF v2026). Use this skill whenever a user asks about SWIFT CSP, CSCF controls, SWIFT security attestation, KYC-SA portal, SWIFT architecture types (A1/A2/A3/A4/B), mandatory vs advisory controls, independent assessment, SWIFT secure zone, secure flow zone, MFA for operators, SWIFT messaging security, payment fraud prevention on SWIFT, gap analysis for CSCF, or compliance with SWIFT's 32 controls (26 mandatory, 6 advisory in v2026) across the three objectives: Secure Your Environment, Know and Limit Access, Detect and Respond. Control 2.4 (Back-Office Data Flow Security) is now mandatory in v2026. v2026 attestation window is July 1– December 31, 2026. Trigger for any SWIFT CSP or CSCF compliance question.

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。

Translate one internal roadmap into audience-appropriate formats with confidence-band discipline. Use when preparing a roadmap readout, tailoring it for an exec, customer, eng, or sales audience, or auditing over-promise risk.

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。

Expert SWIFT Customer Security Programme (CSP) advisor covering the Customer Security Controls Framework (CSCF v2026). Use this skill whenever a user asks about SWIFT CSP, CSCF controls, SWIFT security attestation, KYC-SA portal, SWIFT architecture types (A1/A2/A3/A4/B), mandatory vs advisory controls, independent assessment, SWIFT secure zone, secure flow zone, MFA for operators, SWIFT messaging security, payment fraud prevention on SWIFT, gap analysis for CSCF, or compliance with SWIFT's 32 controls (25 mandatory, 7 advisory in v2026) across the three objectives: Secure Your Environment, Know and Limit Access, Detect and Respond. Control 2.4 (Back-Office Data Flow Security) is now mandatory in v2026. v2026 attestation window is July 1– December 31, 2026. Trigger for any SWIFT CSP or CSCF compliance question.

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。

Draft GDPR/DSGVO-compliant privacy notices as .docx for any EU/EEA jurisdiction and audience. Use when user asks to create a privacy policy/notice, mentions "Datenschutzerklärung", "politique de confidentialité", "privacy notice", needs Art. 13/14 disclosures, AI Act transparency, cookie policy, or notices for applicants ("Bewerber-Datenschutz"), employees ("Beschäftigten-Datenschutz"), B2B partners, or B2C customers. Covers DE (DSGVO+BDSG+TDDDG), FR (RGPD+LIL+LCEN), AT, IT, ES, NL, BE, IE, UK GDPR. Five notice types: Website/App, Applicant, Employee, Business Partner, B2C Customer.

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。

Build a near-complete company list for any ICP by learning how your real customers label themselves, pulling every industry and keyword they use, judging every single row with a cheap AI model (Jev or gpt-5-nano), and running a Claygent lookalike loop until it runs dry. The method from the SCULPT 2026 talk "Build the perfect company list". Use when a database filter feels too narrow or too noisy, when someone says "find every company like our customers", "our TAM looks too small", or "why is this list full of junk".

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 the people who like and comment on a source account's LinkedIn posts (a competitor, or one of the client's own customers) into ICP-filtered prospect rows, with an optional copy-ready personalization line when the campaign asks for one. Triggers on "who engages with our competitors", "likers and commenters", "LinkedIn engagement list", "people commenting on competitor posts", "who engages with our customers' posts", "warm LinkedIn audience", "competitor engagers". Outputs one clean prospect row per engager; engagement_line only on request.

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。

Turns one customer case study into the tightest list of companies that would read that case study and see themselves. Decomposes WHY the case-study company resonates into concrete attributes, translates those attributes into database filters, intersects them with a lookalike anchor, then widens with a model judge. Triggers on "find more companies like our best customer", "who would our case study land with", "lookalikes of X", "build a list around this case study", "companies like our happiest client", "expand this list, it is too small". Outputs a filter set plus one merge field naming which case study to cite.

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。

Amazon Brand Analytics interpretation and strategic insights for Brand Registry owners. Decode Search Frequency Rank (SFR) data, analyze Market Basket patterns, interpret Item Comparison reports, and extract demographic insights to optimize product strategy and advertising spend. Works with Brand Analytics data from all Amazon marketplaces. Requires Brand Registry access. Use when: (1) analyzing Search Frequency Rank data for keyword opportunities, (2) interpreting Market Basket data for cross-sell and bundling, (3) understanding Item Comparison competitive positioning, (4) extracting customer demographic insights, (5) optimizing product portfolio based on customer behavior, (6) building data-driven advertising strategies.

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Applies Geoffrey Moore's chasm-crossing strategy for B2B tech products moving from visionary early adopters to pragmatist mainstream. Use when a product has early traction but stalls before mainstream adoption, when planning a beachhead/niche strategy, when designing whole-product offerings, when positioning against established competitors, or when scaling from innovator usage to industry standard. Triggers include 'stuck between early adopters and mainstream', 'we need a beachhead', 'pragmatist customers won't buy', 'how do we go from 10 to 1000 customers'. NOT for PLG/freemium SaaS (Slack, Notion, Cursor), pure consumer apps, two-sided marketplaces, or AI-native products with bottoms-up viral adoption - their dynamics break the visionary-to-pragmatist sequence.

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Designs a deliberate sequence of offers (attraction, upsell, downsell, continuity) so a new customer generates enough cash within ~30 days to fund acquiring the next one - Alex Hormozi's client-financed acquisition. Use when customers buy once and never again, when CAC eats all the profit, when you can't afford to advertise profitably, when the business grows but cash shrinks, when you sell one product at one price, when deciding what to sell first or next, or when structuring upsells, downsells, payment plans, trials, and continuity. Covers the 30-day payback rule, attraction/upsell/downsell/continuity offer types, and cash-flow sequencing for validated markets.

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。

Applies the StoryBrand SB7 Framework from Building a StoryBrand by Donald Miller. Use when writing website copy, crafting brand messaging, creating marketing materials, building sales funnels, or designing email campaigns. The SB7 Framework positions the customer as the hero and the brand as the guide across 7 story elements. Triggers include 'how should we write our website', 'our messaging is confusing', 'customers don't understand what we do', 'how do I write a tagline', 'what should our homepage say', 'nobody reads our emails', 'how do I create a lead generator', 'our marketing isn't working but our product is good'. NOT for growth channel selection (use Traction), not for product positioning against competitors (use Obviously Awesome), not for pricing (use Monetizing Innovation).

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。

Applies the Bullseye Framework from Traction by Gabriel Weinberg and Justin Mares. Use when choosing growth channels, testing customer acquisition strategies, or deciding where to spend marketing effort. Covers all 19 traction channels with selection methodology, testing protocol, and phase-matched channel advice. Triggers include 'how do we get customers', 'which marketing channel should we use', 'we have a product but no users', 'our growth has stalled', 'should we do content marketing or paid ads', 'how do we test traction channels', 'what channels work at our stage'. NOT for product development (use Lean Startup), not for positioning/messaging (use Obviously Awesome), not for pricing (use Monetizing Innovation), not for enterprise sales methodology (use SPIN Selling).

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 the complete free-to-premium value ladder for affiliate promotions. Triggers on: "value ladder", "customer journey", "upsell path", "ascension model", "free to paid funnel", "tripwire offer", "upsell strategy", "downsell", "product ladder", "price ladder", "customer ascension", "funnel architecture", "map my funnel", "design my funnel stages", "monetization path".

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。

Use when someone outside the marketing team has to film — a customer for UGC, an employee for EGC, or a paid creator or KOC — and needs a brief: talking points, shooting guide, do and do-not list, usage rights, deliverables, and batch management. Trigger on 'UGC brief', 'creator brief', 'KOC brief', 'brief for employee videos', 'what do I tell the influencer', 'we hired a creator to film'. Also use when the user asks how to get customers to make videos for the brand. Not for — a script the team writes and shoots itself, see `04-script-video-global`; edit direction for footage already shot, see `44-video-editor-brief-global`; posting inside communities, see `38-community-seeding-global`.

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。

Use when the user needs to understand competitors — direct, indirect, and substitute tiers, positioning map, SWOT, ad and content benchmark, pricing comparison, and the market gap to attack, using SimilarWeb, Ahrefs, Semrush, and the Meta Ad Library. Trigger on 'competitor research', 'competitive analysis', 'what are competitors running', 'who else sells this', 'why do people pick them over us', 'benchmark us against the market'. Also use when the user sends a competitor link and asks for an opinion. Not for — understanding the customer rather than the rival, see `09-customer-insight-global`; turning difference into a statement, see `58-positioning-global`; paid targeting, see `51-audience-research-global`.

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。

Customer/user segmentation with actionable insights. Use when identifying distinct customer groups, analyzing segment-specific behavior, profiling high-value segments, or testing segmentation hypotheses.

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。

Deploy, update, validate, and troubleshoot the AgentKit hybrid-cloud customer-service demo in this directory. Use when a user asks an AI coding agent to follow the README, deploy or update the demo, configure OpenAPI/Runtime/Knowledge/Memory/Sandbox/MCP/Skills/A2A, run customer-view validation, or record manual platform steps and failures.

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Check refund, complaint, and sensitive customer-service actions against financial-service policy. Use before creating refund work orders, promising settlement times, exposing account data, or responding to complaints.

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Your marketing director. Turns the buyer panel and the competitor map into positioning, the channels the target customer actually uses, a 30-day launch campaign with dated posts and ads, the first ten hooks, a budget split and the numbers to watch. Every message answers an objection real or simulated buyers raised. Use when the user says "how do I market this", "marketing plan", "launch campaign", "how do I get customers", "where do I advertise", "write my ads", or "positioning".

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。

When the user wants to research customer pain points, complaints, or sentiment using review platforms like Trustpilot, G2, Capterra, or app stores. Also use when the user mentions "what are users saying", "competitor reviews", "pain points", or "voice of customer research".

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 and roll out a price increase or packaging change without triggering churn. Segments the base by price sensitivity, models revenue and churn scenarios, decides grandfathering and notice strategy, and produces the full comms kit (customer email, in-app notice, sales talk track, support FAQ, objection handling). Use when raising prices, repackaging tiers, or moving customers to new plans.

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。

Respond to Google, Yelp, and industry reviews in the owner's voice -- gracious on the 5-stars, masterful on the 1-stars. Handles the angry customer, the unfair review, the fake review, and the one that mentions a legal or health issue. Every response written for the thousand future customers reading 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。

Deep-scan any tools you connect (CRM, email, support, reviews, analytics, billing, database) to produce a data-grounded Ideal Customer Profile and a reusable persona library. Read-only by default. Use when you need to define or refresh your ICP, build buyer personas from real data instead of guesses, or generate the persona inputs that the customer-panel-of-experts and prospect-panel-simulator skills consume.

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Input your best customers and find 100+ companies that match the profile. Uses firmographic data, tech stack, growth signals, and similarity scoring to identify ideal prospects. Use when building target account lists or expanding to new markets.

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。