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概要と使いどころ
"Map and analyze the customer journey across Awareness, Consideration, Decision, Usage, and Advocacy stages. Use this skill when the user needs to understand the customer experience end-to-end, identify drop-off points, optimize touchpoints, or improve conversion — even if they say 'where are we losing customers', 'what's our funnel look like', or 'map the user experience'.".
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 multi-objective e-commerce product ranking combining relevance, conversion, and business metrics. Use this skill when the user needs to build a product ranking system beyond text relevance, balance relevance with commercial objectives, or implement learning-to-rank — even if they say 'product sorting', 'search result ranking', or 'how to rank products'.".
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 sales pipeline, historical revenue, conversion rates, and assumptions to produce forecast scenarios. Use for sales reviews, RevOps planning, and founder revenue forecasting.
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 rigorous A/B test plans with hypothesis, sample size calculation, Minimum Detectable Effect (MDE), randomization strategy, and decision rules. Includes guardrail metrics and rollout playbook. Use when planning product experiments, conversion optimization, or data-driven feature decisions.
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。
Identify and exploit OS Command Injection vulnerabilities where web applications insecurely pass user input into system shell commands. Use this skill when applications feature ping utilities, file conversions, network diagnostics, or PDF generators to execute arbitrary system commands and achieve Remote Code Execution (RCE).
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。
Create high-converting, visually distinctive landing pages. Use when building marketing pages, product launches, SaaS homepages, or any single-page conversion-focused website. Guides section-by-section composition with anti-AI-slop principles.
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。
Full instrumentation from day one. PostHog consolidates product analytics + feature flags + error tracking (one platform, one bill). GA4 via GTM (14-step automation, custom dimensions over events, server-side tagging). Sentry (deep error tracking + performance). Stripe (webhook-first with idempotent processing). Listmonk on Coolify (newsletters via Amazon SES SMTP relay). PLG 7-layer framework. Programmatic SEO (5 page types). Incident auto-remediation via Sentry→Inngest pipeline. AI search (GEO) awareness. Local business conversions (phone_click, direction_click, form_submit, booking_click) with CRO patterns for both SaaS and local.
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。
Client-side image handling - preview generation, Canvas API resizing, compression, EXIF orientation, format conversion, memory management with object URL cleanup
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, analyze, and document A/B tests for conversion, onboarding, pricing, lifecycle, and product experiments. Use when the user asks for `/ab-test-setup`, experiment design, sample size, statistical significance, A/B test analysis, ICE-scored test backlogs, or avoiding common testing mistakes.
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。
OpenUI generative UI with Anthropic Claude SDK backend. Stream conversion to OpenAI NDJSON 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。
Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.
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。
Product marketing and go-to-market strategy for B2B SaaS launches and positioning. Use this skill when planning a product launch, developing positioning and messaging, analyzing competitive landscape, building customer advocacy programs, designing a customer referral program (who may accept the reward, how the introduction is made), designing a win/loss interview program, working out why we lose deals, designing GTM strategy, creating category positioning, planning analyst briefings, or deciding which country to expand into and how far to localize. Also use it for an early-stage startup where the founder is still doing the selling — running founder-led sales, setting up a design-partner program, judging whether you have product-market fit, deciding what not to do yet, deciding when to hire the first AE or first marketer, and moving upmarket to larger buyers. Also use it when your company has just acquired another (or is about to), to plan the marketing integration: what you may and may not do between signing and close, the first letter to the acquired customers, whether to keep, endorse, fold in or retire the acquired brand, merging the two marketing databases without mailing people who opted out, when to start cross-selling, and announcing a product sunset. Also use it to design a demand-generation campaign and the channel plan it runs on: one campaign with one job, the offer for buyers who are and are not in market, what each channel is for, the kill and scale rules set before launch, the follow-up sales agrees to, the yearly calendar of always-on programs and tentpole campaigns, and which channels to keep, test or stop. Also use it for brand marketing and demand creation — how much to spend on brand versus performance, what to measure when a brand campaign produces no conversion event, and how to answer "we cannot attribute it". Also triggers on: founder-led sales, founder-led GTM, design partners, design partner agreement, first customers, first 10 customers, do we have product-market fit, PMF, Sean Ellis test, very disappointed survey, pre-seed, seed stage, when to hire our first AE, first sales hire, first marketing hire, moving upmarket, going upmarket, enterprise readiness, first enterprise deal, what do enterprise buyers need before they sign, positioning, product launch, beta program, early access program, design partners, private preview, public preview, general availability, GA launch, when can we call it GA, can sales sell a beta feature, selling the roadmap, launch tier, competitive intelligence, messaging, customer advocacy, win/loss analysis, win/loss interviews, why we lose deals, closed-lost analysis, loss reason, close-reason field, competitive displacement, displace an incumbent, rip and replace, competitive takeout, switch from a competitor, win deals from a competitor, incumbent contract, renewal window, auto-renewal notice period, switching costs, EU Data Act, cloud switching rights, why do we lose to no decision, GTM, category design, ideal customer profile, ICP, what is our real ICP, who actually buys from us, refresh our ICP from closed-won deals, ICP refresh before planning, analyst briefing, G2 review, international expansion, market entry, expand into Europe, which country should we launch in next, localization, translate our site, in-country go-to-market, regional GTM, is cold email legal in Germany, brand marketing, brand awareness, brand vs performance, brand versus demand gen, demand creation, demand capture, 95-5 rule, out of market buyers, category entry points, distinctive brand assets, mental availability, brand tracking, brand health, brand lift, share of search, how do we measure brand, justify brand spend, brand budget. Also triggers on public-sector marketing: sell to government, government marketing, public sector go-to-market, federal marketing, state and local government, SLED, FedRAMP, FedRAMP marketing claims, can we say FedRAMP ready, GovRAMP, StateRAMP, VPAT, accessibility confor
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 a task needs the judgment of an Electronics Engineer working on RF, analog, and mixed-signal hardware — closing an RF link budget for a wireless telemetry radio, sizing an op-amp or instrumentation-amplifier front end against a gain-bandwidth constraint, designing an anti-alias or IF filter to a cutoff and Q, pre-checking a design against FCC/IEC EMC radiated-emissions limits before formal compliance testing, or picking between a superheterodyne and direct-conversion receiver architecture.
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 a task needs the judgment of a Brewmaster — setting or correcting mash pH before conversion, calibrating yeast pitch rates from wort gravity, deciding whether a diacetyl (VDK) reading clears a batch for cold crash or requires a diacetyl rest, investigating a contamination or off-flavor signal and scoping a recall, or specifying dissolved-oxygen and pasteurization controls to hit a TTB-compliant ABV label.
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 a task needs the judgment of a Biofuels Processing Technician — adjusting catalyst dosing for transesterification based on actual measured feedstock free fatty acid content rather than a standard fixed dose, verifying conversion completeness via actual glyceride content testing rather than visual inspection, actively monitoring fermentation temperature and pH throughout the process rather than checking only at the end, and managing byproduct quality as a distinct economic consideration alongside primary fuel yield.
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 a task needs the judgment of a Biofuels/Biodiesel Technology and Product Development Manager — deciding whether a lab/bench-scale process is ready to scale up, evaluating feedstock flexibility tradeoffs in process design, pricing a technology's value against regulatory credit pathways (RFS RIN, LCFS CI score), or managing IP strategy for a novel conversion process. Distinct from biofuels-production-manager, which runs an existing operating facility — this role owns R&D and commercialization of new or improved process technology.
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 a task needs the judgment of an Animal Scientist — formulating a least-cost livestock ration against nutrient requirements, evaluating breeding stock using EPDs or a selection index, diagnosing a feed-conversion-ratio decline, or designing a growth/reproduction performance-monitoring plan for a livestock operation.
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。
Query the Ensembl REST API for regulatory feature annotations, variant effect prediction (VEP), coordinate liftover, gene lookups, and cross-references. Use when the user needs to annotate variants with VEP (consequence, CADD, REVEL, SpliceAI), check Ensembl Regulatory Build overlap for ENCODE regions, convert coordinates between GRCh37 and GRCh38, resolve gene IDs (Ensembl ↔ symbol ↔ RefSeq), look up gene phenotype associations, or cross-reference ENCODE targets with Ensembl annotations. Also use when the user mentions Ensembl, VEP, variant effect predictor, liftover, assembly conversion, regulatory build, gene lookup, or cross-references between databases.
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 SEO-optimized competitor comparison and alternatives pages. Covers "X vs Y" layouts, "alternatives to X" pages, feature matrices, schema markup, and conversion optimization. Use when user says "comparison page", "vs page", "alternatives page", "competitor comparison", "X vs Y", "alternatives page", "versus page", "comparison matrix", or "feature comparison".
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。
Convert simplified Chinese to Traditional Chinese (zh-TW), covering characters, Taiwan vocabulary, and phrasing. Use when the user asks to convert 簡體 to 正體/繁體, localise mainland Chinese text for Taiwan, fix simplified characters left in a document or repository, check whether content is written in proper zh-TW, or normalise variant forms such as 着/裏/爲 to the Taiwan standard. Handles both files edited in place and text pasted into the conversation. Runs on Python 3 standard library alone, with no OpenCC and no installed packages.
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。
Convert an AWS CDK application to Pulumi. This skill MUST be loaded whenever a user requests migration or conversion of a CDK application to Pulumi.
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。
Convert an AWS CloudFormation stack or template to Pulumi. This skill MUST be loaded whenever a user requests migration or conversion of CloudFormation to Pulumi.
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。
Convert or migrate Azure ARM (Azure Resource Manager) templates, Bicep templates, or code to Pulumi, including importing existing Azure resources. This skill MUST be loaded whenever a user requests migration, conversion, or import of ARM templates, Bicep templates, ARM code, Bicep code, or Azure resources to Pulumi.
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。
Rules for numeric type conversions in Luma. Use when writing or reviewing code that casts between integer or float types, especially across different widths.
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。