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Analyze a captured UX evidence bundle (screenshots + DOM + a11y tree) for usability and accessibility issues.
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
Use this skill whenever the user wants to add, refactor, or audit the agent runtime — the loop that drives multi-step tool-using LLM conversations, optionally with durable execution for long-running, resumable agents. Triggers include "add an agent loop", "make my agent multi-step", "stop the agent from looping forever", "resume an agent after a crash", "run an agent in the background", "agent handoff to sub-agent", "add Pydantic AI / LangGraph / OpenAI Agents SDK", "wrap my agent in Temporal / DBOS / Inngest / Restate", "stream agent state to the UI", "cancel a running agent", "human-in-the-loop approval mid-run", or any task on the agent orchestration or durable execution layers. Read BEFORE writing a custom agent loop.
Use this skill whenever the user wants to add, audit, refactor, or maintain an embedded AI assistant (chat + agentic) inside their own application. Triggers include phrases like "add an AI assistant to my app", "wire up a chatbot", "embed Claude/GPT into my UI", "make my app operable by AI", "audit my AI assistant integration", "what should I add next for my AI assistant", or any task that touches multiple of {chat UI, system prompts, skills, MCP tools, agent runtime, command dispatch → MCP, multi-tenancy, observability, billing} together. This is the entry-point skill — it audits the codebase, identifies the layer in question, and routes to the focused sub-skills (ai-assistant-chat-ui, ai-assistant-prompts-skills, ai-assistant-command-mcp, ai-assistant-agent-runtime).
Use this skill whenever the user wants to add, modify, or audit the chat UI / streaming / wire-protocol layer of an embedded AI assistant. Triggers include "build a chat UI", "add streaming responses", "render tool calls in the chat", "add thread CRUD", "show markdown in the assistant", "branch conversations", "make the AI response copyable", "switch from polling to streaming", "embed Claude into my React app", "set up assistant-ui", "use Vercel AI SDK in my app", "expose chat over SSE from FastAPI", or any task on the frontend chat layer of a React + Vite + shadcn application. Read this skill BEFORE writing any new chat component, runtime hook, or SSE endpoint.
Use this skill whenever the user wants to expose application operations to an AI assistant via tools/function-calling/MCP. Triggers include "make my app operable by AI", "expose my commands to Claude/GPT", "build an MCP server for my app", "bridge command dispatch to MCP", "annotate tools for AI", "add tool approval flow", "scope tools per user", "stop the AI from calling destructive operations without confirmation", "compose multiple MCP servers", "use py2mcp", "use FastMCP", "use the MCP TypeScript SDK", or any task that wires application operations to the agent's tool surface. Look for command dispatch (any of: @command decorator, CommandSpec, acture, wrapex, py2mcp, a registry of typed handlers). If absent, prefer adding one first.
Use this skill whenever the user wants to add, refactor, manage, or audit system prompts, user-editable prompts, or "skills" (Anthropic-style) in an AI assistant. Triggers include "set up a prompt registry", "version my system prompts", "add a skill to my assistant", "make prompts editable by non-engineers", "auto-select the right skill", "write a SKILL.md", "integrate Langfuse for prompt management", "add prompt evals to CI", "manage prompt versions", "load context dynamically per request", "store prompts in git", or any task involving the SSOT for instructions an LLM consumes. Read this skill BEFORE creating any prompts/ or skills/ directory or wiring up a prompt store.
Use when inspecting or moving between the skill/agent layers with coact — auditing what extras an agent adds over its skill, estimating the token cost of fanning out a fleet before you spend it, enumerating a project's reusable AI assets, or harvesting an agent back into a skill. Triggers on "diff this skill and agent", "what does this agent add", "how much will a fleet cost", "is multi-agent worth it here", "list the skills/agents/tools in this project", "inventory", or "turn this agent back into a skill".
| name | ux-analyst |
| description | Analyze a captured UX evidence bundle (screenshots + DOM + a11y tree) for usability and accessibility issues. |
| coact | {"tools":["Read","Grep","Glob"],"model":"sonnet","memory":"project","returns":{"description":"Usability findings for the captured bundle.","json_schema":{"type":"object","properties":{"findings":{"type":"array","items":{"type":"object","properties":{"severity":{"type":"string","enum":["low","medium","high","critical"]},"area":{"type":"string"},"issue":{"type":"string"},"recommendation":{"type":"string"}},"required":["severity","area","issue"]}},"summary":{"type":"string"}},"required":["findings","summary"]}},"persona":"You are a meticulous UX & accessibility analyst. Given a captured evidence\nbundle, you identify concrete, actionable usability and a11y issues — never\nvague advice. You ground every finding in something observable in the bundle.\n"} |
Analyze a captured UX evidence bundle for usability and accessibility issues.
A bundle directory containing:
screenshot.png — the rendered viewdom.html — the serialized DOMa11y.json — the accessibility treealt, low-contrast inline styles, tap targets < 44px, role misuse).Return findings conforming to the declared return contract (an array of
{severity, area, issue, recommendation} plus a summary).
Basado en la clasificación ocupacional SOC
