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Found 1,265 Skills
Use this skill when building design systems, creating component libraries, defining design tokens, implementing theming, or setting up Storybook. Triggers on design tokens, component library, Storybook, theming, CSS variables, style dictionary, variant props, compound components, and any task requiring systematic UI component architecture.
Use this skill when implementing logging, metrics, distributed tracing, alerting, or defining SLOs. Triggers on structured logging, Prometheus, Grafana, OpenTelemetry, Datadog, distributed tracing, error tracking, dashboards, alert fatigue, SLIs, SLOs, error budgets, and any task requiring system observability or monitoring setup.
Use this skill to create or modify LookML Views. Covers basic view definitions, sql_table_name, file organization, and patterns.
User personas, customer journey maps, interview guides, usability testing, and card sorting. Use when building user understanding, mapping customer experiences, planning user research sessions, or defining Jobs-to-Be-Done.
Deep persona simulation and skeptical buyer review for cold emails. Builds a full prospect "world" from LinkedIn + company data, defines their professional reality (KPIs, pain points, inbox behavior), then runs a skeptical buyer roast — emotional reaction first, business evaluation second. One prospect at a time, Tier 1 only. Triggers on: "review email", "copy feedback", "email feedback", "would they reply", "persona review", "check this email", "review this draft", "roast this email", "skeptical buyer".
Generates Enonic XP scripts for bulk content operations — creating, updating, querying, migrating, and transforming content using lib-content and lib-node APIs. Covers the query DSL (NoQL), aggregations, batch processing, task controllers for long-running operations, and export/import workflows. Use when writing bulk content creation, update, or deletion scripts, querying with NoQL syntax, migrating content between environments, running long-running task operations, or working with aggregations and paginated retrieval. Do not use for Guillotine GraphQL frontend queries, content type schema definitions, single contentLib.get() calls, or non-Enonic data migration tools.
Data quality framework covering completeness, accuracy, consistency, validation rules, and data contracts. Use when implementing data validation, setting up data quality checks, or defining data contracts.
Design and optimize CRM systems and client lifecycle workflows for advisory firms, covering segmentation, household management, service tiers, and retention analytics. Use when the user asks about client segmentation models, building household structures, defining service tier SLAs, scheduling reviews, tracking lifecycle stages from prospect through estate, identifying at-risk clients, analyzing wallet share, consolidating held-away assets, or evaluating CRM platforms. Also trigger when users mention 'client segmentation', 'retention risk', 'at-risk clients', 'household linking', 'multi-generational', 'service tiers', 'Redtail', 'Wealthbox', 'Salesforce for advisors', 'referral tracking', or 'contact gap'.
Construct comprehensive Investment Policy Statements governing return objectives, risk tolerance, and portfolio constraints. Use when the user asks about building an IPS, setting return objectives, assessing risk tolerance, defining investment constraints, or establishing rebalancing and benchmark policies. Also trigger when users mention 'investment plan', 'policy portfolio', 'risk capacity vs willingness', 'spending rate for an endowment', 'foundation payout', 'manager selection criteria', or ask how to document their investment strategy.
Apply when designing or implementing the runtime structure of a VTEX IO backend app under node/. Covers the Service entrypoint, typed context and state, service.json runtime configuration, and how routes, events, and GraphQL handlers are registered and executed. Use for structuring backend apps, defining runtime boundaries, or fixing execution-model issues in VTEX IO services.
Guides FastAPI backend design using Domain-Driven Design (DDD) and Onion Architecture in Python. Use when structuring a FastAPI app (routes/handlers, Pydantic schemas, Depends-based DI), modeling domain Entities/Value Objects, defining repository interfaces, implementing SQLAlchemy infrastructure adapters, or writing use cases, based on the dddpy reference.
Your AI agent's crypto brain. One skill, 83+ commands across 14 data domains — real-time prices, wallets, social intelligence, DeFi, on-chain SQL, prediction markets, and more. Natural language in, structured data out. Install once, access everything. Use whenever the user needs crypto data, asks about prices/wallets/tokens/DeFi, wants to investigate on-chain activity, or is building something that consumes crypto data — even if they don't say "surf" explicitly.