plugin-audit

Vercel Queues guidance — durable topics with at-least-once delivery, independent consumer groups, retries, delays, and idempotency keys via @vercel/queue (JS) or vercel-queue (Python). Use when deferring background work, buffering traffic, fanning out events, or choosing between Queues and Workflows.

Vercel CLI expert guidance. Use when deploying, managing environment variables, linking projects, viewing logs, querying metrics, debugging a deployed app on Vercel (errors, 5xx responses, timeouts, slow routes, or a single failing request), managing domains, managing feature flags with vercel flags, or interacting with the Vercel platform from the command line. Use the vercel-tracing skill instead for `vercel traces` span search, trace inspection, and sampling rules, or for OpenTelemetry setup.

Vercel Agent guidance — dashboard and Slack chat, code review, production investigation, approved actions, and product installation. Use when configuring or working with Vercel's AI assistant.

Vercel Runtime Cache API guidance — ephemeral per-region key-value cache with tag-based invalidation. Shared across Functions, Routing Middleware, and Builds. Use when implementing caching strategies beyond framework-level caching.

Vercel Routing Middleware guidance — request interception before cache, rewrites, redirects, personalization. Works with any framework. Supports Edge, Node.js, and Bun runtimes. Use when intercepting requests at the platform level.

Vercel's React and Next.js performance rules (70 rules in 8 categories ordered by impact - eliminating waterfalls, bundle size, server-side performance, client-side data fetching, re-renders, rendering performance, JavaScript performance, and advanced patterns). Use when writing, reviewing, or refactoring React components, Next.js pages, or data fetching for performance. Not an accessibility or TypeScript style guide.

Guide for building, configuring, and deploying microfrontends on Vercel. Use this skill when the user mentions microfrontends, multi-zones, splitting an app across teams, independent deployments, cross-app routing, incremental migration, composing multiple frontends under one domain, microfrontends.json, @vercel/microfrontends, the microfrontends local proxy, or path-based routing between Vercel projects. Also use when the user asks about shared layouts across projects, navigation between microfrontends, fallback environments, asset prefixes, or feature flag controlled routing.

Corrects outdated LLM knowledge about the Vercel platform and introduces new products. Injected at session start.

Audit a website for WCAG 2.1 AA accessibility violations using automated scanning and Noibu traffic data, and optionally draft the fixes. Use when you want to know what's inaccessible across the store, check WCAG or ADA compliance, find contrast or heading errors, audit for screen reader support, generate an accessibility report, or fix accessibility violations with pull requests or tickets.

Manage and search AIDP audit logs — enable/disable auditing, set retention, and query audit-log entries for a DataLake. Use when the user asks about audit logging, who did what, compliance/retention of AIDP activity, or wants to search audit events. Self-contained — official `aidp audit` CLI preferred, `oci raw-request` fallback.

Read-only PREVIEW of a Snowflake environment from the operator's machine - inventory of tables and views with row counts, byte sizes and column types, plus the census of objects that are not tables or views, table-maintenance state and security posture. Use ONLY when the user wants to look at an account without migrating it - answering what is in there, how big the tables are, what the security posture looks like. This is NOT the discovery step of a migration: a migration discovers inside AIDP as a workflow (runbook S6), because a laptop-side read leaves no log and no evidence on the platform. If the user asked to migrate, route to snowflake-migrator-overview and follow S1 through S12.

First-run setup for the Snowflake to AIDP migrator. Finds or creates the one migration config file (snowmig-config.yaml), reads every field back with secrets masked, verifies Snowflake authentication by password, key-pair, programmatic access token or SSO, verifies the AIDP end, then smoke-tests the connection and reports the account, region, role and warehouse. Use the first time the migrator runs on a machine, or when any other stage fails with an authentication, connection or "no config found" error.

Read Snowflake from an AIDP notebook through the AIDP `aidataplatform` Spark format handler. Use when the user mentions Snowflake, Snowflake warehouse, Basic authentication, KeyPair authentication, private.key.file, or private.key.content. Snowflake writes are not supported in AIDP 4.0.

Diagnose Wingspan connection and permission problems. Use when a Wingspan tool returns an authentication or permission error, when the tools report nothing at all, when an answer looks like it came from the wrong company or the wrong account, or when someone asks "am I connected to Wingspan", "is my login still valid", "why can't Claude see my contractors", "why is this empty", "these are not my contractors".

Run a comprehensive, read-only health check on the user's computer and return a scored chat summary with prioritized, plain-English recommendations and safe cleanup suggestions. Use this whenever the user wants to check their computer's health, speed it up, free up / reclaim disk space, find what's eating CPU / memory / storage, check battery health and wear, audit login & startup items, see pending updates, or asks for a "tune-up", "checkup", "system report", "health check", or "is my Mac/PC/laptop healthy". Trigger even on casual phrasing like "my laptop feels slow", "why is my fan so loud", "my computer is laggy", "running out of space", or "clean up my machine". Works on macOS, Windows, and Linux. This is a Desktop Commander skill: it relies on a real local shell (start_process / interact_with_process) and is strictly read-only by default — it never needs sudo and only performs cleanups the user explicitly approves.

Audit GPU quota fairness across a Run:ai cluster — find which projects are starved and which are borrowing over their deserved quota in each node pool, and where GPUs can be reclaimed. Use when an admin asks "who is over quota in node-pool X", "which projects are starved", "is the cluster shared fairly", "where can I reclaim GPUs", "show deserved vs allocated by project or department", or when a team reports it can't get GPUs and you suspect unfair borrowing. This is the quota / fairness axis (deserved vs allocated). For idle-but-held GPUs use runai-get-utilization-overview; for a health/scheduling verdict use runai-get-cluster-health-summary.