2.7K skills · sorted by relevance
Read existing code to reconstruct system specifications, including architecture, data flows, APIs, and business rules. Produces documentation tied to code locations, separating observed behavior from inferences and questions that need clarification.
Define reliability targets for production services and calculate error budgets, the allowed amount of failure. Create monitoring rules, incident response runbooks, capacity plans, and automation scripts, with failure experiments to check recovery behavior.
Create Dockerfiles, automated build and deployment pipelines, Kubernetes configurations, and infrastructure templates. The skill also prepares deployment checks, rollback procedures, and incident response runbooks to support application releases and production operations.
Review recent changes, automated test failures, issues, and conversations to prioritize engineering work. Produce a concise report separating urgent actions, watch items, noise, and state updates, with suggested next steps and rough effort estimates.
Develop security design improvements from vulnerability reports, incident documents, source code, or scan results. Compare architectural options with before-and-after diagrams, engineering tradeoffs, and migration plans to support a concrete design decision.
Search shared game modding notes and turn session findings into reusable documentation. Record exact versions, engine behavior, verification limits, and fixes for common problems, then validate the note and prepare a public contribution for human approval.
Inspect game code, data files, live memory, and graphics captures to understand the internals needed for a mod. Choose tools for the game’s technology, document findings, and validate unknown file formats by decoding and re-encoding them.
Review feature plans before development from business strategy, user experience, and engineering perspectives. Receive scored assessments, specific gaps tied to requirements, and prioritized actions that clarify what must change before the team starts building.
Turn raw security findings into a ranked list for engineering review. Check claims against source code, merge duplicates, assess how feasible exploitation is, and assign component owners, with results in JSON and Markdown.
Turn research notes and discussions into a PRD, a product requirements document for development teams. Connect customer problems, target users, proposed solutions, and success metrics, while documenting acceptance criteria, excluded work, dependencies, and unresolved questions.
Develop security design improvements from vulnerability reports, incident documents, source code, or scan results. Compare architectural options with before-and-after diagrams, engineering tradeoffs, and migration plans to support a concrete design decision.
Apply Vercel Engineering’s performance guidelines when writing, reviewing, or refactoring React and Next.js code. Prioritize improvements to data fetching, downloaded code size, server processing, and unnecessary rendering using rules with explanations and code examples.
Design automation for sales and marketing workflows, including lead scoring, routing, CRM updates, and AI agent tasks. The skill helps choose platforms, organize shared customer context, and define monitoring and feedback loops around team needs and budget.
Write, review, and refactor React and Next.js code using performance guidelines from Vercel Engineering. Prioritize improvements to data fetching, downloaded code size, server processing, and unnecessary rendering, with rule files providing explanations and code examples.
Guide an AI agent through research, implementation, and verification with fewer interruptions. The skill emphasizes evidence, concise reporting, and saved progress notes, with three working modes that adjust how closely the user follows the work.
Turn an agreed product direction into a technical specification before coding. Create architecture and flow diagrams, define failure handling and input validation, and map tests to the behavior engineers need to implement.
Turn reference images into prompts for AI image generation by describing composition, lighting, colors, and materials. Receive a main prompt and a negative prompt that specifies unwanted features, tailored to the image’s visual medium.
Add, repair, and strengthen tests for existing code using the project's testing framework. Covers regression cases, boundary conditions, flaky tests, and coverage gaps, with validation that links requested behaviors to specific tests.
Turn a large codebase spanning multiple repositories or services into a structured knowledge base. It documents architecture, business rules, and dependencies, links conclusions to source lines, labels confidence in relationships, and supports incremental updates.
Turn a multi-step feature into an implementation plan that another engineer can follow without prior context. The plan includes exact file paths, complete code, test commands, expected results, and small, verifiable steps.
Map an existing app’s screens, user journeys, reusable interface parts, and likely data structure using public sources and your own account. Produce a research document and prioritized feature table to guide development of your own version.
Build production systems for machine learning models, from deployment to monitoring and retraining. The skill also guides LLM integration and retrieval augmented generation, which uses retrieved information to support answers, with scripts and architecture references.
Design and improve prompts and structured responses for AI applications. The skill also guides evaluation, retrieval augmented generation, which uses retrieved material to inform answers, and agent architecture, with references and scripts for production workflows.
Learn how to organize the instructions, conversation history, documents, and tool results an AI agent receives. Explains context limits and loading information as needed, helping you review agent designs and investigate behavior caused by poorly managed context.