Popular AI skills worth publishing first.

Review pull requests for correctness, regressions, security risk, missing tests, and unclear ownership.

Every team reviews code, and agent output is much better when review criteria are reusable.

Generate focused unit, integration, and regression tests from changed files, bug reports, or existing behavior.

Testing is one of the highest-repeat development workflows for AI coding agents.

Turn Sentry issues, support tickets, logs, and user reports into impact, likely cause, and next action.

It saves time before an engineer opens the codebase and gives teams consistent handoff notes.

Ask the assistant to map dependencies, identify safe increments, and produce a verification plan before edits.

Large refactors need planning discipline so AI agents do not make broad, risky changes.

Explain architecture, folders, local setup, test commands, release flow, and common development traps.

New contributors and AI agents both need durable project context.

Guide API client changes, schema validation, retries, auth handling, error states, and integration tests.

API work is frequent, repetitive, and easy to get wrong without local conventions.

Check responsive layouts, accessibility, spacing, text overflow, empty states, and production UI polish.

AI-built UI often needs a reusable product-quality checklist before shipping.

Review semantic markup, labels, keyboard flow, color contrast, headings, focus states, and screen-reader hints.

Accessibility rules are concrete enough to package and reuse across projects.

Look for leaked secrets, unsafe auth paths, exposed admin routes, injection risks, and risky dependency usage.

Public repos and AI-generated code both need explicit safety checks before release.

Summarize commits, PRs, fixed bugs, upgrade notes, known issues, and user-facing changes.

Teams repeat this every release, and the output format can be standardized.

Review page titles, descriptions, headings, structured data, canonical URLs, internal links, and search intent.

AI products need clear public docs and discoverability from day one.

Keep READMEs, changelogs, runbooks, examples, and onboarding docs aligned with code changes.

Documentation drift is constant, and skills make the expected style repeatable.

Install and document Model Context Protocol servers, prompts, resources, tool permissions, and verification steps.

MCP is becoming the common way to connect AI assistants to tools and context.

Run local web pages, inspect key flows, capture screenshots, and report visual or interaction problems.

Real browser checks catch layout and interaction bugs that code-only review misses.

Review migrations for locks, rollbacks, indexes, nullability, backfills, and production deployment order.

Database changes are high-risk and benefit from a consistent preflight checklist.

Analyze CSV/XLSX files, normalize columns, build summaries, create formulas, and prepare chart-ready outputs.

Teams use AI for lightweight analysis long before they build a formal data pipeline.

Turn notes, specs, or reports into crisp slide outlines with audience, narrative, and visual QA guidance.

Reusable presentation workflows help agents produce structured decks instead of loose summaries.

Create visual prompts, style constraints, asset briefs, and review checklists for generated images.

Design and product teams need repeatable image direction, not one-off prompt guessing.