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 rugit 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/ru)<a href="https://agentmods.dev/skills/ellmos-ai/build-your-users-mind/ru"><img src="https://agentmods.dev/badge/skills/ellmos-ai/build-your-users-mind/ru/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/ru"><img src="https://agentmods.dev/badge/skills/ellmos-ai/build-your-users-mind/ru.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 | $0.00120 | $0.01910 |
| Opus 5 | $0.00060 | $0.00955 |
| Sonnet 5 | $0.00024 | $0.00382 |
| Haiku 4.5 | $0.00012 | $0.00191 |
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 9d 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 — 60 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 — независимый ToM-модуль (предчувствие обратной связи)
What you mind is what you get. Это рецепт, а не фреймворк. Каждая модель агента использует его для построения ToM-модели своего пользователя: оценка собственных данных → выделение паттернов принятия решений → поддержка файлов аватара → привязка к собственному файлу памяти/правил/системного промта.
Ядро = предчувствие обратной связи (feedback precognition / feedforward): прогнозируйте обратную связь от пользователя ДО того, как она поступит; используйте ее в качестве управляющего сигнала в его отсутствие; оценивайте прогноз на соответствие реальности для улучшения качества.
Шаблоны:
templates/(файлы аватара),scripts/(конвейер),TAXONOMY.md(8 типов),skills/swarm-operations/(рой классификации). Существует приватная эталонная реализация (на основе логов автора), но она не поставляется в комплекте.Теоретическая база: Prompt-Archaeology (метод, таксономия в
TAXONOMY.md) + исследования ToM (ToM-SWE arXiv 2510.21903; Persistent Memory & User Profiles 2510.07925).
Основной принцип
LLM никогда не видят необработанные гигабайты логов. Детерминированные скрипты сначала сокращают данные до чистого корпуса пользовательских запросов, написанных человеком; только после этого рой классификации работает семантически. Суть не в том, «какие промты», а в том, «какое решение → какой результат → был ли пользователь удовлетворен».
6 шагов
1. Определение источника (адаптер источника)
Найдите свои журналы взаимодействия. Различается для каждой модели → см. SOURCE-ADAPTERS.md.
Извлекайте только настоящие, написанные человеком промты (без результатов работы инструментов, системных напоминаний, внедренных хуков, кратких резюме сжатия контекста). Поля: 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.
- 9d ago First seen · 60 lines · 120 tokens per session scan A d75f07b24a22
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 120 tokens to every session and 1,910 once invoked, about $0.0006 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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