Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ellmos-ai/build-your-users-mind --skill esgit clone --depth 1 https://github.com/ellmos-ai/build-your-users-mindWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ellmos-ai/build-your-users-mind/es)<a href="https://agentmods.dev/skills/ellmos-ai/build-your-users-mind/es"><img src="https://agentmods.dev/badge/skills/ellmos-ai/build-your-users-mind/es/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ellmos-ai/build-your-users-mind/es"><img src="https://agentmods.dev/badge/skills/ellmos-ai/build-your-users-mind/es.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00103 | $0.01669 |
| Opus 5 | $0.00051 | $0.00834 |
| Sonnet 5 | $0.00021 | $0.00334 |
| Haiku 4.5 | $0.00010 | $0.00167 |
Grade A, and why
build-your-users-mind scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Translation status (2026-07-15): historical pre-1.1 draft. The root English documents are authoritative; do not use this translation as the current operational or security contract.
build-your-users-mind — Módulo agnóstico de ToM (Precognición de retroalimentación)
What you mind is what you get. Una receta, no un marco de trabajo. Cada modelo de agente lo utiliza para construir un modelo de ToM de su usuario: evaluar datos propios → destilar patrones de decisión → mantener archivos de avatar → vincular a su propio archivo de memoria/reglas/system prompt.
Núcleo = feedback precognition (feedforward): Prediga la retroalimentación del usuario ANTES de que llegue; utilícela como señal de control en su ausencia; evalúe la predicción frente a la realidad a posteriori para mejorar.
Plantillas:
templates/(archivos de avatar),scripts/(pipeline),TAXONOMY.md(8 tipos),skills/swarm-operations/(enjambre de clasificación). Existe una implementación de referencia privada (basada en los registros del autor) pero no se distribuye.Base teórica: Prompt-Archaeology (método, taxonomía en
TAXONOMY.md) + investigación de ToM (ToM-SWE arXiv 2510.21903; Persistent Memory & User Profiles 2510.07925).
Principio fundamental
Los LLM nunca ven gigabytes brutos. Los scripts deterministas primero reducen los datos a un corpus limpio de instrucciones de usuario escritas por humanos; solo entonces un enjambre de clasificación trabaja semánticamente. El núcleo no es "qué instrucciones", sino "qué decisión → qué resultado → si el usuario quedó satisfecho".
Los 6 pasos
1. Identificar la fuente (Adaptador de fuente)
Encuentre sus propios registros de interacción. Difiere según el modelo → ver SOURCE-ADAPTERS.md.
Extraiga solo instrucciones genuinas escritas por humanos (sin resultados de herramientas, recordatorios del sistema, inyecciones de hooks, resúmenes de compactación de contexto). Campos: ts, project, session, text.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 65 lines · 103 tokens per session scan A 62aade1a1b9f
build-your-users-mind is a skill published in the GitHub repository ellmos-ai/build-your-users-mind (3 stars, last pushed 2d ago), licensed MIT. It adds 103 tokens to every session and 1,669 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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