3,178 件 ・ 関連度順
概要と使いどころ
Triage and prioritize a support ticket or customer issue. Use when a new ticket comes in and needs categorization, assigning P1-P4 priority, deciding which team should handle it, or checking whether it's a duplicate or known issue before routing.
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。
Fallback authoring playbook for building general-purpose personal assistant agents that do not fit a more specific archetype. Use this only after checking the other archetype skills (coding, spreadsheet, research, customer-support, content-writer, ops-automation). Examples include summarizing emails, drafting short answers, capturing notes, or generic personal-helper agents.
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 branded Qwen Code desktop package from the Tauri desktop shell using a minimal brandId and logo. Use when the user wants a custom, white-label, or rebranded desktop client, installer, DMG/EXE/AppImage/deb, or one-click brand build on top of packages/desktop.
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, scaffold, customize, validate, and locally test Qwen Code extensions. Use when the user wants a new Qwen Code extension, needs help choosing an extension template, wants to add QWEN.md context, commands, skills, agents, MCP servers, settings, hooks, channels, or LSP servers, or asks how to link and test an extension locally. Invoke with `/extension-creator` followed by an extension path and optional template name.
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。
Implement a Table v9 plugin when built-ins and typed meta are insufficient. Covers FeatureMaps, runtime lifecycle hooks, prototypes, and a complete checked example.
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。
Comprehensive toolkit for product managers including RICE prioritization, customer interview analysis, PRD templates, discovery frameworks, and go-to-market strategies. Use when prioritizing features, synthesizing user research, writing requirement documentation, or developing product strategy.
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。
Atlassian Jira expert for creating and managing projects, planning, product discovery, JQL queries, workflows, custom fields, automation, reporting, and all Jira features. Use when setting up or configuring Jira projects, writing JQL and advanced searches, creating dashboards, designing workflows, or performing technical Jira operations.
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。
Manage local SEO for service-area businesses — appliance repair, HVAC, plumbing, cleaning, and any business that serves customers at their location. Use when the user wants to: audit Google Business Profile, generate neighborhood service area pages, check NAP consistency across directories, create LocalBusiness schema, or write review responses. Triggers: 'local SEO', 'Google Business Profile', 'GBP', 'service area page', 'NAP consistency', 'local citations', 'LocalBusiness schema', 'review responses', 'Google Maps ranking'. NOT for national SEO (use seo-audit). NOT for general schema (use schema-markup). NOT for AI answer-engine visibility (use aeo).
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 preparing roadmap narratives, release notes, changelogs, or stakeholder updates tailored for executives, engineering teams, and customers.
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 design, launch, or optimize a referral or affiliate program. Use when they mention 'referral program,' 'affiliate program,' 'word of mouth,' 'refer a friend,' 'incentive program,' 'customer referrals,' 'brand ambassador,' 'partner program,' 'referral link,' or 'growth through referrals.' Covers program mechanics, incentive design, and optimization — not just the idea of referrals but the actual system.
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 and maintain the marketing context document that all marketing skills read before starting. Use when the user mentions 'marketing context,' 'brand voice,' 'set up context,' 'target audience,' 'ICP,' 'style guide,' 'who is my customer,' 'positioning,' or wants to avoid repeating foundational information across marketing tasks. Run this at the start of any new project before using other marketing skills.
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。
Set up, audit, and debug analytics tracking implementation — GA4, Google Tag Manager, event taxonomy, conversion tracking, and data quality. Use when building a tracking plan from scratch, auditing existing analytics for gaps or errors, debugging missing events, or setting up GTM. Trigger keywords: GA4 setup, Google Tag Manager, GTM, event tracking, analytics implementation, conversion tracking, tracking plan, event taxonomy, custom dimensions, UTM tracking, analytics audit, missing events, tracking broken. NOT for analyzing marketing campaign data — use campaign-analytics for that. NOT for BI dashboards — use product-analytics for in-product event analysis.
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。
Converts long-form markdown (specs, RFCs, reports, plans, explainers) into a single-file, lightly-interactive HTML document with sticky TOC, scrollspy, search filter, code-copy buttons, and design-system-driven brand tokens. Triggers when the markdown-html-orchestrator classifies an input as DOCUMENT, or when invoked directly via /cs:md-document. Reads the design-system config via config_loader.py and inlines the user's 12 derived CSS custom properties; refuses to render if onboarding hasn't run. Single-file output — Google Fonts + Prism.js CDN are the only externals; no framework runtime, no build step. Use after orchestrator routing or after design-system onboarding is confirmed.
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 operational runbooks from a service name — deployment, incident response, maintenance, and rollback workflows. Templated structure customizable per environment. Use when documenting on-call procedures for a new service, standardizing incident response across teams, or producing runbooks before launching to production.
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。
SaaS financial health advisor. Use when a user shares revenue or customer numbers, or mentions ARR, MRR, churn, LTV, CAC, NRR, or asks how their SaaS business is doing.
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。
Computer vision engineering skill for object detection, image segmentation, and visual AI systems. Covers CNN and Vision Transformer architectures, YOLO/Faster R-CNN/DETR detection, Mask R-CNN/SAM segmentation, and production deployment with ONNX/TensorRT. Includes PyTorch, torchvision, Ultralytics, Detectron2, and MMDetection frameworks. Use when building detection pipelines, training custom models, optimizing inference, or deploying vision 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。
Production-grade Stripe integrations: subscriptions with trials and proration, one-time payments, usage-based billing, checkout sessions, idempotent webhook handlers, customer portal, and invoicing. Covers Next.js, Express, and Django patterns. Use when integrating Stripe for the first time, debugging webhook reliability issues, migrating from a different payment provider, or adding usage-based billing to an existing subscription product.
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。
/cs:ciso-review <plan> — Risk-paranoid interrogation of any plan that touches data, compliance, or production access. Use when launching features that handle customer data, before a SOC 2 / ISO audit, or after any incident or near-miss.
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。
/cs:gc-review <plan> — General Counsel interrogation of contracts, IP, regulatory, term sheets, and employment-law surface. Use when reviewing a term sheet before signing, redlining a customer MSA, or checking IP assignment and regulatory exposure on a new product.
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。
/cs:office-hours <topic> — YC-style 6-question founder interrogation before any advice. Forces clarity on problem, customer, distribution, defensibility, capital, and founder fit. Use when a founder question is too vague to route — e.g. 'should we grow faster?' — or before drafting a strategy brief.
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 and maintain one coherent company story across all audiences — employees, investors, customers, candidates, and partners. Detects narrative contradictions and ensures the same truth is framed for each audience's needs. Use when preparing investor updates, all-hands presentations, board communications, recruiting narratives, crisis communications, or when user mentions company narrative, messaging consistency, storytelling, all-hands, investor update, or crisis communication.
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 reviewing a specific inbound deal before close — when sales has asked for a discount that exceeds AE authority, when the customer has redlined the MSA, when per-deal economics (margin after discount, multi-year payment shape, indemnity exposure) need to be quantified, or when discount approval needs to be routed to a named human approver (Sales Director, VP Sales, CFO, CRO, General Counsel). Covers deal review, discount approval routing, per-deal margin scoring, deal exception handling, MSA redline triage, contract landmine detection (uncapped indemnity, MFN, perpetual license-back, missing DPA), and named-approver chain assembly. NEVER auto-approves — every output is a numeric scorecard plus a routing recommendation to a named human.
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 Head of People Ops, BizOps lead, or Internal Communications owner needs to draft and sequence an internal-only change-management communication — a re-org announcement, a tool rollout, a policy change, a leadership transition, a layoff, an acquisition close, or an internal product launch — and the audience is employees (not customers). Pairs Prosci ADKAR and Kotter's 8-step change model with deterministic stdlib-only Python tools to produce a sequenced touchpoint calendar, a Kotter-compliant primary announcement, an audience-segmented FAQ, and manager cascade talking points; industry-tuned via --profile {tech-startup, scaleup, enterprise, public-company, non-profit}. Triggers on "all-hands announcement", "change comms", "rollout comms", "re-org announcement", "manager talking points", "layoff comms".
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。
Analyzes RFP/RFI responses for coverage gaps, builds competitive feature comparison matrices, and plans proof-of-concept (POC) engagements for pre-sales engineering. Use when responding to RFPs, bids, or proposal requests; comparing product features against competitors; planning or scoring a customer POC or sales demo; preparing a technical proposal; or performing win/loss competitor analysis. Handles tasks described as 'RFP response', 'bid response', 'proposal response', 'competitor comparison', 'feature matrix', 'POC planning', 'sales demo prep', or 'pre-sales engineering'.
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。