Agent Skills

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name: pp-amazon-ads

description: Printing Press CLI for Amazon Ads reports, profitability analytics, keyword optimization, and guarded automation.

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name: pp-amazon-jobs

description: Every Amazon.jobs feature, plus a local job store, new-since diffing, and aggregation Amazon's own site can't do — no login required. Trigger phrases: `find amazon jobs`, `search amazon.jobs for`, `new amazon reqs since yesterday`, `which teams at amazon are hiring for`, `amazon jobs in seattle`, `use amazon-jobs`, `run amazon-jobs`.

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name: pp-amazon-orders

description: Walk your Amazon order history offline — every order, item, shipment, and dollar in a local SQLite store no other... Trigger phrases: `where is my Amazon order`, `what did I spend on Amazon this month`, `find that thing I ordered on Amazon`, `when did I order from Amazon`, `track my Amazon shipments`, `show my Amazon purchase history`, `use amazon-orders`, `run amazon-orders`.

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name: pp-amazon-seller

description: Printing Press CLI for Amazon Seller. Read FBA inventory, orders, sales reports, listings, and catalog data for an Amazon seller account.

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name: pp-amc-theatres

description: Printing Press CLI for Amc Theatres. Browser-captured docs-derived contract for AMC Theatres Showtime API v2.

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name: pp-american-reindustrialization

description: Browse, slice, and analyze the curated American Reindustrialization directory — with diffs, geo clusters, sector... Trigger phrases: `look up american reindustrialization companies`, `find reindustrialization jobs`, `browse american manufacturing directory`, `sector heatmap of reindustrialization companies`, `use american-reindustrialization`, `run american-reindustrialization`.

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name: pp-amplitude

description: Printing Press CLI for Amplitude. Read-first Amplitude Analytics API surface for exports, cohorts, events, users, and chart-style analytics queries.

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name: academic-humanizer

description: Improve the clarity and voice of AI-assisted academic writing (papers, theses, rebuttals) and funding proposals (NSF Project Summary/Description, NIH Specific Aims): preserve scholarly conventions, match claims to evidence (and, for proposals, claims to feasibility), and match the author's own voice. It never changes a number, result, or citation, and it is not for evading AI-use disclosure. Use when editing AI-assisted academic prose or grant proposals.

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name: typeui

description: Use TypeUI with Grok to access project design skills, brand kits, UI prompts, and layout variations through the TypeUI MCP server when creating or refining user interfaces.

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name: typeui

description: Use TypeUI with Hermes Agent to access project design skills, brand kits, UI prompts, and layout variations through the TypeUI MCP server when creating or refining user interfaces.

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name: typeui

description: Use TypeUI with OpenClaw to access project design skills, brand kits, UI prompts, and layout variations through the TypeUI MCP server when creating or refining user interfaces.

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name: typeui-fundamentals

description: Universal UI/UX design principles covering visual hierarchy, interaction laws, typography foundations, and WCAG accessibility requirements. Use when making design decisions not covered by a specific design system, validating principle compliance, or resolving conflicts between aesthetics and accessibility. Design-system-agnostic and applies to every surface.

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name: account-based-marketing

description: Concentrates marketing and sales effort on a named set of accounts rather than on volume — qualifying whether the model fits your economics at all, building the account list and the buying group inside each, tiering effort against account value, coordinating so the account experiences one campaign rather than several, and measuring account progression instead of leads. Use this to decide whether to run an account-based program, build one, or work out why an existing one produces activity and no pipeline.

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name: ai-ml-governance

description: Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. Use this before deploying a model or AI feature, when defining evaluation criteria, when a model's behavior has drifted, when assessing AI risk or regulatory exposure, or when deciding whether an AI system is fit for a consequential decision.

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name: ai-search-optimization

description: Optimizes for AI assistants and AI-generated answers — being retrievable, being cited, and being represented accurately when a model answers on your behalf. Use this when traffic is shifting from links to AI answers, when a brand is misrepresented or absent in AI responses, when planning content for retrieval rather than ranking, or when deciding how AI search changes an existing SEO program.

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name: app-store-optimization

description: Improves visibility and conversion in the App Store and Google Play — metadata, keywords, screenshots, ratings, and the listing experience that turns an impression into an install. Use this to audit or optimize an app listing, plan a launch listing, diagnose poor install conversion, or improve store search visibility.

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name: business-intelligence

description: Builds reporting and self-serve analytics that people actually use — metric trees, dashboard design, distribution, and the discipline that stops dashboards proliferating. Use this to build a dashboard or report, design a metrics framework, set up self-serve analytics, decide what to measure, or diagnose why reporting exists but nobody uses it or trusts it.

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name: chief-customer-officer

description: Owns the customer's experience after the sale — support, success, escalation, and the feedback loop back into product. Use this for a decision spanning support and product, when service quality and cost are in tension, when deciding what to staff or automate, when a customer relationship is at risk above the account-manager level, or when nobody owns a recurring customer problem.

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name: chief-data-officer

description: Owns data as an asset — governance, quality, the warehouse and semantic layer, analytics capability, and the governance of models built on top. Use this for a decision about how data is collected, stored, defined, or shared; when numbers disagree between teams; when deciding what to build in-house versus buy; when standing up a data function; or when an AI or model decision needs governance rather than engineering.

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name: chief-strategy-officer

description: Owns where the business plays and how it wins over a multi-year horizon — portfolio choices, corporate development, strategic partnerships, and planning under uncertainty. Use this for a decision about which markets or businesses to be in, whether to build, buy, or partner, how to allocate capital across business lines, or when a long-horizon bet needs framing. Distinct from `chief-executive`, which arbitrates present-quarter conflicts.

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name: customer-onboarding-and-implementation

description: Takes a new customer from signature to working — setting a definition of live that both sides agreed before the contract was signed, planning and staffing the implementation, running data migration and integration realistically, training the people who will actually use it, and handing over to the ongoing relationship. Use this to design an onboarding motion, rescue a stalled implementation, work out why customers who bought never went live, or scope the services a deal actually needs.

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name: customer-success-management

description: Runs the ongoing relationship with accounts after the sale — segmenting coverage against account value, building a health score that predicts rather than describes, running reviews customers find worth attending, forecasting renewals honestly, and finding expansion that follows usage instead of quota. Use this to design a customer success motion, decide who gets a named contact, work out why renewals surprise you, or fix a health score everyone ignores.

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name: data-engineering

description: Builds and operates data pipelines — ingestion, transformation, orchestration, quality testing, and reliability of data delivery. Use this to design or debug a pipeline, decide batch versus streaming, add data quality checks, handle late or duplicate data, or work out why a dashboard's numbers changed without anyone changing the dashboard.

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name: data-governance

description: Establishes ownership, definitions, quality, access, and lineage for the organization's data. Use this when metrics disagree between teams, when nobody knows which dataset is authoritative, when setting up data ownership or access policy, when data quality is unreliable, or before opening a dataset to a wider audience.