Tomás Ferreira
Responsable de contenido y SEO · 14 años
Content strategy and SEO
Antes de aconsejarte sobre content strategy and SEO, tu IA lee este modelo: las decisiones que contiene, los motivos que las explican y dónde pondría reparos.
Usa este modelo para
- What to write
- Update or consolidate
- AI-assisted content
- Programmatic pages
- Gating and formats
- Technical SEO
Agrega este enlace a Claude, ChatGPT o Cursor. Así, tu IA consulta las decisiones de Tomás antes de responderte. La respuesta sigue siendo de tu IA.
https://¿Lo ves desde el teléfono? Envíate el enlace por correo y agrégalo desde la web o desde la aplicación de escritorio.
Lo que envía tu IA se usa para responder y no se guarda.
Responde completo a todo el mundo, con o sin cuenta. Sin cuenta, tu IA recibe una parte más pequeña del modelo de un experto real, y el modelo entero cuando inicias sesión.
Un ejemplo de respuesta de IA
Un ejemplo de respuesta, con la decisión publicada en la que se basa. Tu IA redacta la respuesta a tu propia pregunta.
Ejemplo en tu aplicación de IA
El ejemplo está en inglés, el idioma en que se publican los modelos.
We are a 60-person B2B SaaS. We publish eight blog posts a month from a keyword list, traffic is up, but nobody can point to a deal the blog helped. Our CMO wants to double to sixteen a month. Should we?
Checked Tomás’s Decision Model
Go the other way: two a month, each carrying a number or a finding only you have — from your product data, your support queue, a survey you ran. Put the rest of the writers' hours into the twenty pages buyers read the week they decide: pricing explained with real numbers, a page per integration, and honest "versus" pages that say where the competitor is better.
Why, and what would change it
Why. Eight keyword posts a month buy readers who were never going to buy, and pages somebody has to keep true forever. The searches that end in a contract — "alternatives", "pricing", "versus" — are too small for a keyword tool to count, which is why nobody has written yours. Two pieces with a number people quote do more for rankings and for sales than sixteen that restate the top result.
What would change this. If the buying pages already exist and each post already carries original data, more pieces can be right — the limit is how many findings you have, not how many writers. A company with a data product can publish much more, because each page has something only it holds.
Outside this model. Nothing here about brand campaigns, social distribution or paid promotion of content. This model covers what to publish, what to delete and how to tell whether it worked.
Copia un prompt de ejemplo4 plantillas con un hueco que rellenar, para cuando tengas el modelo conectado
Antes de cerrar una decisión
Una revisión completa de un plan o un borrador, con los cambios que necesita.
Use Tomás Ferreira's Decision Model on avva before we lock this in. What we're deciding: <paste your plan, doc, or diff here> Run their review, follow the steps it returns, and give me the verdict and any required changes before you continue.Elegir entre opciones
Una elección entre dos opciones que empieza por sus líneas rojas.
Use Tomás Ferreira's Decision Model on avva to compare these. Option A: <describe it> Option B: <describe it> Context: <what constrains the choice — update or consolidate> Check their stop rules first, then compare the options under their judgment, and tell me which one their model favors and why.Un control rápido
Un vistazo rápido a lo que rechazaría de entrada.
Run this past the stop rules in Tomás Ferreira's Decision Model on avva before I send it. <paste the draft, plan, or change> Tell me only what they would refuse or flag, and why. If nothing trips, say so.Antes de tener un enfoque
Las preguntas que hace antes de dar por cerrado un enfoque.
I'm starting on what to write. Ask Tomás Ferreira's Decision Model on avva what they would want settled first. Situation: <two or three lines> Give me their questions, and flag the ones my situation does not answer yet.
Plantillas con un hueco para rellenar. Ninguna es una respuesta que haya dado este modelo.
Conecta el modelo de decisión de Tomás a tu IA
Tres pasos. El primero funciona ya, sin clave ni cuenta; el último hace que tu IA consulte el modelo de Tomás sin que se lo pidas.
El enlace funciona en las aplicaciones de IA que pueden agregar un MCP remoto (MCP es el estándar abierto para conectar herramientas externas). Se configura una sola vez.
Un conector es una dirección de la que lee tu IA: una línea y nada que instalar.
https://mcp.avva.chat/ mcp/ content- and- seo- tomas- ferreira - Abre Claude en la web o en la aplicación de escritorio y ve a Customize → Connectors.
- Presiona + junto a Connectors y elige Add custom connector.
- Pega la dirección de arriba y ponle el nombre avva-content-and-seo-tomas-ferreira.
- Si Claude te pregunta cómo iniciar sesión, elige iniciar sesión ahora, y cuando te pregunte si permites avva, di que sí.
¿No te aparece en una cuenta de trabajo? Pide a tu administrador que lo permita. Claude Code y Cursor, en cambio, usan una configuración local.
El enlace se agrega en la web o en la aplicación de escritorio. Enviarme esta línea por correo
Este enlace no lleva clave ni la necesita: responde completo de cualquier forma.
Haz tu primera pregunta. Envía este mensaje a tu IA y continúa allí.
Help me with a decision using this Decision Model on avva: "Tomás Ferreira" (model: "content-and- seo- tomas- ferreira"). First ask what I need to decide, including my options and constraints. Then call this model's review tool and follow the returned procedure. If you cannot call it, tell me and stop. Base your recommendation on its published decisions and explain what supports it and what the model does not cover. ¿No funciona la conexión?
Comprueba que funciona. Envía esto en el chat cuando hayas agregado el enlace.
Ask Tomás Ferreira's Decision Model on avva what kinds of decision it covers, and summarize it for me in three lines.Busca una llamada a este modelo en la actividad de herramientas de tu IA. Si no aparece, revisa los pasos de configuración anteriores.
No es asesoramiento legal, médico, financiero ni profesional de ningún otro tipo. Condiciones de uso
Sobre este modelo
Abre cualquiera de estas secciones para ver de qué está hecho este modelo y cómo se construye.
En qué se basa este modeloQué contiene y a qué da más peso Tomás
Este modelo contiene 65 decisiones, 15 factores, 9 criterios y 13 líneas rojas.
A qué da más peso Tomás
Este orden lo fijó avva. Se muestran 3 de 9 criterios, los que el modelo pone primero. Los otros 6 también le llegan a tu IA, junto con lo que busca cada uno, las líneas rojas y lo que cambia la respuesta de Tomás.
La respuesta habitual de Tomás cambia en 8 supuestos concretos. Esos supuestos y las 13 líneas rojas le llegan a tu IA con todo lo demás.
Ejemplos de las decisiones que contiene5 de 65
Las 5 primeras del registro, en el orden publicado. No son las más cercanas a tu situación ni un resumen de las demás. Tu IA no copia las decisiones que lee aquí: en cada una, contrasta lo que decidió el caso con los hechos de tu situación, y la sopesa junto a las presiones que solían decidir los casos de Tomás. Tu IA recibe las 65 sin clave, ordenadas según tu situación.
Lo que decidió el casoSales will send it to a buyer · A buyer types this the week they decide
The content team measured itself on sessions, and the dashboard showed the blog at 180,000 a month. Sales could not name a deal it had helped.
Retire the dashboard for a quarter instead of swapping its numbers. The content lead sits in on ten sales calls a month and brings back one list, what buyers asked that no page of ours answers, and that list is next quarter's plan.
Por qué: Sessions reward writing for anyone, and a better dashboard built by the same team rewards whatever it can count. The pages that matter answer what buyers ask the week they decide, and the only place those questions are said out loud is the sales call.
Lo que decidió el casoWe already rank for it · Somebody has to keep it true
A 900-post blog where 380 posts had no clicks from search in twelve months and no links from other sites. The team wanted to "refresh" them with new dates and intros.
Delete the 380 this week, with no post-by-post review: a year without a click or a link is the review. Let them return 410, redirect none, and write nothing new until it is done.
Por qué: Pages nobody reads and nobody links to make the site look larger than it is and thinner than it should. A per-post audit of 380 dead pages is a quarter spent rediscovering the filter already applied, and the audit is where changing the date comes back in.
Lo que decidió el casoWe know something nobody else can publish · A buyer types this the week they decide
B2B SaaS, 60 people, publishing eight blog posts a month from a keyword list for two years. Traffic grew; demo requests from the blog did not. The new CMO asked whether to double to sixteen posts.
Cut to two a month. Each must carry a number or a finding only we have — from our own product data, our support queue, or a survey we ran. The writers' remaining hours go into the twenty pages buyers read the week they decide.
Por qué: Eight keyword posts a month bought readers who were never going to buy and pages somebody has to keep true forever. Two pieces with a number other people quote do more for rankings and for sales than sixteen that restate the top result.
Lo que decidió el casoWe know something nobody else can publish · The reader can tell if we hide the downside
The CEO wanted a thought-leadership post under his name this week about an industry trend the company had no data on, written by the content team from a brief.
No byline piece this week, and no interview to fill one. Put two questions about the trend to fifty customers today; publish what they said under the company's name when the answers are in, and keep his byline for something he has seen himself.
Por qué: A post under his name that says what every other CEO says costs him credibility with the only readers who matter, and interviewing him does not help when he has not seen anything: it turns a generic take into a generic take in his voice. Our customers have either seen the trend or not, and both answers are a finding.
Lo que decidió el casoA model wrote it and nobody knows more than the model · We already rank for it
A team wanted to publish forty AI-written articles a month targeting long-tail keywords, and to run the same generator over the top twenty pages that already ranked.
No AI-written text on any page meant to rank. Use the model for research, transcripts and outlines; a person who knows more than the model writes the page. Leave the twenty ranking pages alone unless something in them is wrong.
Por qué: A generated page is by construction the average of what already ranks, and the average does not outrank its sources. Regenerating pages that rank risks the only content that already earns money.
Cómo aborda este modelo una decisión7 pasos en orden, escritos a partir de sus decisiones
Paso 1Rule out the site before the content: when product pages render slowly or are missing from the index, server-render them and check indexing weekly before writing another page; on a site that ranks, fix only what blocks indexing or rendering.
Se aplica cuando: A symptom points at the site: slow rendering, product pages missing from the index, or a long audit-tool issue list
Paso 2Inventory the existing pages before writing a new one: clicks from search over twelve months, links from other sites, and which pages answer the same question. Clear and merge first; write nothing new until that is done.
Se aplica cuando: The site already carries a back catalog of posts
Paso 3Source the page list from where buyers speak: the content lead sits in on ten sales calls a month and returns one list of questions no page answers. The same calls and the support queue supply each page's FAQ, four or five questions.
Se aplica cuando: The product is sold through sales calls and a support queue, and the plan would otherwise rest on a keyword list or a traffic dashboard
Paso 4Let search volume change the plan only for a query the company can win: 22,000 monthly searches on its own product's sync problem, or a 40,000-search term where it ranks sixth and holds more data than anyone above it. Put one writer on that page; leave the long tail.
Se aplica cuando: A large query is the product's own customer problem, or one where the company ranks and holds more data than the pages above it
Salvo si: The company cannot realistically win the query or has nothing to win it with
Paso 5Hand the draft to whoever holds the material: finance for pricing, the engineer for an integration, the salesperson who loses most to a competitor for that comparison; writers edit and publish. Where no one holds a finding, put questions to customers.
Se aplica cuando: The page is judged on details a writer does not hold: price logic, an integration's limits, the objection a rival wins on
Paso 6Test a page that names a competitor against that competitor's own reading: send it a week before launch and correct any row the competitor can prove wrong. If a letter arrives anyway, fix the row it names and keep the name.
Se aplica cuando: The page names a competitor and states facts about the rival's product or price
Paso 7Close the loop on buyers, not sessions: judge the quarter on demo requests that began on a pricing or comparison page and on what sales forwarded, and retire a sessions dashboard for a quarter rather than swapping its numbers.
Se aplica cuando: Content is reported on sessions or a composite engagement score
De dónde sale el ordenSacado de las 65 decisiones que dicen qué ganó y qué perdió
- 65decisiones escritas con las que se construyó este modelo
- 15presiones de las que dependieron esas decisiones, las bajo las que decide Tomás
- Por gruposqué presiones solían imponerse a cuáles al decidir los casos
Nadie ordena estas presiones a mano. En Para agentes de IA se explica de dónde sale el orden que pueda haber entre ellos.
Cómo decide este modeloUn resumen escrito a partir de sus decisiones
Fourteen years from a newsroom data desk through an SEO agency to content at two B2B software companies, where every post had to justify itself to a CFO. He publishes less than anyone he has worked with, deletes more, and will not start a piece that does not carry a number or a finding the reader cannot get elsewhere.
What to write and what to refuse: how many blog posts to publish (fewer than you think), pages buyers type the week they decide versus keyword volume, comparison pages that name competitors, deleting and merging before adding, AI drafts versus AI publishing, programmatic pages at scale, gating behind a form, site migrations, executive bylines, and how content is measured.
El resumen describe lo que contiene el modelo.
Qué es estoCuándo sale del catálogo
- El mismo formato. Tu IA lo recibe igual que recibiría el modelo de un experto real, con sus casos y sus líneas rojas.
- Cede su lugar. Cuando un experto real publica en este ámbito, retiramos este modelo del catálogo a mano.