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Как превратить Cursor в долговременную память проекта: LLM Wiki, QMD и Cursor CLI

> Практический кейс интеграции LLM Wiki и локального поиска в рабочий процесс Cursor. Ссылка на исходные конфигурации и skills — в конце статьи.

Большой контекст в Cursor помогает ровно до момента, когда проект начинает жить дольше одного чата. В одной сессии обсуждается исходная постановка задачи, в другой — устройство витрины, в третьей внезапно выясняется, что показатель должен считаться немного иначе, а ещё через неделю приходится заново объяснять агенту, почему первое очевидное решение уже было отвергнуто.

С кодом ситуация хотя бы относительно понятна: он лежит в репозитории, его можно перечитать. С требованиями, договорённостями и результатами исследований всё сложнее. Они распределяются между транскриптами встреч, сообщениями в чате, SQL‑запросами, заметками и памятью участников проекта.

Ранее эту проблему я рассматривал со стороны встреч в AudioToText: запись превращается в структурированный транскрипт, затем поверх него можно выделять сущности и сопоставлять позиции участников. В текущем проекте возник следующий вопрос: как довести этот материал до coding agent так, чтобы он не просто находил отдельный фрагмент по запросу, а постепенно получал поддерживаемую картину проекта?

Отправной точкой стала концепция LLM Wiki Андрея Карпати. В ней между сырыми источниками и ответами модели появляется отдельный слой — связанная Markdown‑wiki, которую агент дополняет по мере появления новых материалов. Сырые документы остаются неизменными, а производное знание постепенно накапливается в тематических страницах, индексе и журнале изменений.

На бумаге всё выглядело достаточно просто. На практике путь прошёл через настройку QMD, попытку вынести ingest в DeepSeek Harness, локальную модель, отдельный MCP‑контур и, наконец, возврат к более скучной, но устойчивой схеме: Cursor, набор project skills и вызов Cursor CLI для фоновой обработки.

В этой статье разберу именно этот путь — включая решения, от которых пришлось отказаться. Это не ещё один гайд по созданию raw/, wiki/ и index.md: основной предмет здесь — граница между LLM‑инструкцией и детерминированной оркестрацией, то есть что разумно оставить внутри skill, что вынести во внешний скрипт и в какой момент отдельный agent harness оказывается лишним.

Что хотелось получить

Задача появилась не из желания завести ещё одну папку с Markdown‑файлами. Нужен был рабочий контур для задач, в которых контекст складывается постепенно.

Типичный сценарий выглядел так:

  1. На установочной встрече заказчик формулирует исходные требования.

  2. Запись проходит через AudioToText и превращается в транскрипт.

  3. Во время анализа модели данных появляются SQL‑фрагменты, ограничения и гипотезы.

  4. В Cursor уточняется реализация конкретного блока.

  5. Часть решений меняется после проверки на реальных данных.

  6. Через несколько сессий агенту снова требуется весь этот контекст.

Просто прикладывать к каждому чату все документы можно, но это плохо масштабируется. Модель каждый раз повторно разбирает один и тот же материал, значительная часть контекстного окна уходит на уже известные вещи, а противоречащие друг другу формулировки приходится разрешать заново.

Архитектурная основа взята в готовом виде из концепции LLM Wiki Карпати: неизменяемый слой сырых источников, отдельный слой синтезированного знания и обязательная привязка каждого утверждения к первоисточнику. Чтобы превратить эту схему из концепта в повседневный рабочий инструмент внутри Cursor, к ней добавились прикладные инженерные требования:

  • из текущего чата в Cursor должно быть легко зафиксировать полезный фрагмент диалога и отправить его в сырые источники;

  • фоновый ingest хотелось вынести на локальные модели — на старте задача казалась достаточно прямолинейной, чтобы не тратить на неё облачные лимиты;

  • поиск по накопленной базе должен работать локально и быстро;

  • все операции должны запускаться детерминированно — одними и теми же командами, а не зависеть от того, насколько удачно сформулирован очередной промпт.

Последний пункт оказался важнее, чем казалось в начале.

Базовая структура wiki

В проекте используется каноническая структура LLM Wiki: неизменяемый слой raw/ для первоисточников, синтезируемый слой wiki/ (с разбивкой на sources, entities, concepts и analyses), а также SCHEMA.md и AGENTS.md с правилами валидации и инструкциями для агента. На случай роста базы в исходной заметке Карпати в качестве опционального локального поисковика упоминается QMD — он также сразу вошёл в нашу схему:

PharmacyCategories/
├── raw/
│   ├── assets/
│   └── 2026-10-17--initial-audit.md
├── wiki/
│   ├── sources/
│   ├── entities/
│   ├── concepts/
│   ├── analyses/
│   ├── index.md
│   ├── overview.md
│   └── log.md
├── AGENTS.md
└── SCHEMA.md

PharmacyCategories/ — отдельная база знаний внутри проекта. Такая организация позволяет разделять знания по предметным областям, то есть самостоятельным бизнес‑контурам. Например, сначала хранилище может содержать только данные о продажах, а позднее в него добавляется логистика, которую необходимо связать с продажами для сквозной аналитики. Для логистики можно создать соседнюю базу с такой же структурой, сохранив связи с сущностями из блока продаж. Каталог wiki/ каждой базы регистрируется в QMD как отдельная коллекция, поэтому при поиске можно обращаться к нужной предметной области. Исходники из raw/, код и остальные файлы проекта в поисковую выдачу не попадают. К материалам из raw/ агент обращается напрямую только во время ingest и проверки источников.

Поскольку базовая структура каталогов и назначение операций ingest, query и lint уже описаны в материалах о LLM Wiki, повторять их обоснование нет смысла. Дальше речь пойдёт о конкретной реализации этой схемы в Cursor: настройке QMD, выборе модели для ingest, оформлении операций в skills и их запуске через Cursor CLI.

Почему одного Markdown мало

Пока в wiki несколько десятков страниц, достаточно открыть index.md и пройти по ссылкам. Но хотелось заранее проверить сценарий, в котором источников и связанных страниц становится заметно больше.

QMD — локальный поисковый движок для Markdown, который сочетает полнотекстовый BM25-поиск, векторный поиск, query expansion и reranking. Его базовый workflow состоит из двух этапов: сначала найти кандидатов, затем получить полные документы через get или multi_get.

Коллекция описывалась в конфигурации QMD примерно так:

collections:
  pharmacy-categories:
    path: /Users/user/Sensu/PharmacyCategories/wiki
    pattern: "**/*.md"
    context:
      "": "База знаний блока категорий аптек: sources, entities, concepts, analyses."
      concepts: "Бизнес-термины, правила и алгоритмы расчёта (LFL, What-If моделирование)."
      entities: "Компоненты системы: Oracle, DWH, OLAP-куб, витрины и дашборды."

После настройки коллекции индекс обновляется командами qmd update и qmd embed -c pharmacy-categories, а сам поиск выполняется в два шага: сначала отбор кандидатов (search, vsearch или гибридный query), затем чтение нужных карточек по docid через get или multi-get.

Примеры команд поиска и чтения карточек в QMD
# точный термин или имя таблицы (BM25)
qmd search "T_BI_CAT_MODELING" -c pharmacy-categories -n 5

# семантически близкие страницы (векторный поиск)
qmd vsearch "сопоставление категорий между периодами" -c pharmacy-categories -n 5

# гибридный поиск с query expansion и reranking
qmd query "как работает Like-for-Like для категорий" -c pharmacy-categories -n 5

# чтение выбранных документов по docid
qmd get "#a1b2c3"
qmd multi-get "#a1b2c3,#d4e5f6"

Такое разделение оказалось полезным не только ради скорости. В контекст агента сначала попадает короткий список кандидатов, а уже затем — содержимое нескольких действительно релевантных страниц. QMD поддерживает поиск по docid, пакетное чтение и получение диапазона строк, что позволяет не затягивать в prompt целый большой документ.

Здесь важно уточнить: в этой реализации QMD используется не как самостоятельная RAG‑система поверх сырых документов, а как retrieval‑компонент поверх уже синтезированных страниц wiki/. В классическом RAG первичные источники обычно нарезаются на чанки, а найденные top‑k фрагменты автоматически подмешиваются в prompt перед генерацией ответа. QMD в нашей схеме решает более узкую навигационную задачу: он ранжирует поддерживаемые Markdown‑карточки с помощью полнотекстового и семантического поиска, но не подменяет ими рабочий контекст целиком. Агент сначала видит компактную карту релевантных страниц, а затем сам решает, какие карточки или диапазоны строк открыть. За счёт этого в контекст попадает не случайный набор сырых чанков, а связанное, структурированное и привязанное к источникам знание из LLM Wiki.

Отдельную роль при работе через MCP‑сервер играет поле context. В QMD его можно задать как для всей коллекции, так и для отдельных папок или конкретных файлов. При поиске через MCP QMD возвращает эти описания вместе со сниппетами — они работают как навигационная подсказка для LLM. По ним модель сразу понимает назначение документа или раздела (это первоисточник, бизнес‑правило или описание таблицы) и точнее решает, какие именно карточки стоит открывать полностью через get.

В итоге QMD был подключён к Cursor через MCP, и на этом этапе сложилась первая рабочая схема:

raw → LLM ingest → wiki → qmd update/embed → Cursor query (через QMD)

Но оставался открытый вопрос: какая модель должна выполнять ingest?

Первая попытка: DeepSeek Harness

Использовать основную модель Cursor для разбора каждого транскрипта было жалко. Ingest — это объёмная, но во многом рутинная работа: прочитать источник, классифицировать материал, найти связанные страницы, обновить frontmatter, индекс и журнал.

Логичным казалось вынести эту часть во внешний агентный контур и подключить к нему локальную модель через oMLX. На эту роль подошёл бы любой CLI‑агент — например, Qwen Code или другие доступные альтернативы, — но хотелось поэкспериментировать с DeepSeek Harness (DSH). В теории разделение ролей выглядело хорошо:

  • Cursor отвечает за интерактивную разработку и работу с кодом;

  • DSH обслуживает wiki;

  • локальная модель выполняет дешёвый ingest;

  • QMD предоставляет обоим агентам общий поисковый слой.

Для DSH был подготовлен профиль с тремя компонентами:

  1. QMD как stdio MCP‑сервер.

  2. Локальная директория со skills.

  3. OpenAI‑compatible endpoint oMLX с локальной моделью Qwen.

Упрощенный фрагмент профиля DSH
- id: qmd
  name: deepseek-ai/dsh-mcp-client
  config:
    serverName: qmd
    transport: stdio
    command: /opt/homebrew/bin/qmd
    args: [mcp]
    cwd: /Users/user/Sensu
    toolCallTimeoutMs: 180000

- id: skill-filesystem
  name: deepseek-ai/dsh-skill-filesystem
  config:
    providerName: sensu-workspace
    includeDefaultRoots: false
    customSkillDirs:
      - /Users/user/Sensu/.dsh/skills

- id: agent-default-model
  name: deepseek-ai/dsh-agent-default-model
  config:
    provider: omlx
    model: Qwen3.5-9B-MLX-8bit

Отдельный skill llm-wiki-ingest в DSH должен был:

  1. Проверить состояние коллекции QMD.

  2. Найти новый raw‑файл.

  3. Прочитать SCHEMA.md и существующий индекс.

  4. Найти связанные страницы через QMD.

  5. Создать source page.

  6. Обновить entities, concepts и analyses.

  7. Дополнить index.md и log.md.

  8. Запустить переиндексацию.

То есть DSH рассматривался не как ещё один чат, а как отдельный исполнитель хорошо определённой операции.

Почему DSH не стал финальным решением

Сразу зафиксирую главное: отказ произошёл не из‑за того, что на связке DSH и локальной модели не удалось получить качественный ingest — технически контур полностью заработал: DSH видел skill, MCP подключался к QMD, а модель читала источники и обновляла карточки по схеме.

Решающей стала эксплуатационная цена отдельного контура и слишком большая задержка для интерактивного workflow внутри IDE.

Во‑первых, появилось сразу несколько независимых контуров конфигурации:

  • правила wiki;

  • skills Cursor;

  • skills DSH;

  • профиль DSH;

  • параметры локального inference‑сервера;

  • MCP‑настройки Cursor;

  • MCP‑настройки DSH;

  • QMD‑коллекции и модели эмбеддингов.

Каждый компонент по отдельности выглядел понятным. Вместе они создавали систему, в которой сбой ingest мог быть связан с моделью, skill, профилем, cwd, MCP, таймаутом или индексом QMD.

Во‑вторых, первоначальный выбор компактной модели Qwen3.5-9B-MLX-8bit оказался приемлемым для классификации и чернового конспекта, но менее стабильным на многошаговой записи в несколько файлов. Чем больше в skill появлялось защитных инструкций, тем сильнее он начинал напоминать отдельное приложение, только реализованное в Markdown. Позже проблему качества удалось полностью закрыть переходом на mlx-community/Qwen3.8-27B-OptiQ-4bit с отключённым reasoning: в связке с DSH эта модель справляется с задачей на отлично, и сам контур остаётся полностью рабочим. Единственный минус упирается уже в физику локального инференса — время выполнения операции напрямую зависит от скорости генерации токенов (token generation), которая лимитируется пропускной способностью памяти (memory bandwidth) устройства. В моих экспериментах инференс выполнялся на Mac Studio M4 Max, где скорость token generation составила около 15 токенов в секунду.

В‑третьих, экономия токенов подписки не равна снижению общей стоимости системы. Локальный ingest добавил холодный запуск модели, ожидание генерации, контроль MCP‑процесса и диагностику профиля. Для фоновой пакетной обработки без спешки это вполне рабочий вариант. Для интерактивного режима, когда несколько raw‑файлов хочется быстро разобрать без отрыва от основной задачи в IDE — уже не всегда.

Наконец, главная проблема была организационной: рабочая точка входа всё равно оставалась в Cursor. Именно там обсуждалась задача, создавался важный контекст и принималось решение, что сохранить. Переключение в отдельный UI ради обслуживания wiki ломало сценарий.

После нескольких итераций стало понятно, что нужен не второй интерактивный агент, а единый набор операций внутри Cursor. DSH остался полезным экспериментом: он помог отделить pipeline от конкретной IDE и проверить локальную модель. Но в финальной схеме от него было решено отказаться.

Если свести всю эволюцию контура в одну таблицу, путь от первого прототипа до финальной реализации выглядел так:

Итерация

Исполнитель

Что получилось

Почему пошли дальше / результат

1. QMD + ручной ingest

Основной чат Cursor

Рабочий прототип базы и поиска

Чтение источников и запись карточек засоряют окно рабочего чата

2. DSH + Qwen3.5-9B-MLX-8bit

Отдельный harness + oMLX

Быстрый локальный разбор простых заметок

Нестабильность на многошаговой записи в несколько файлов

3. DSH + mlx-community/Qwen3.8-27B-OptiQ-4bit

Отдельный harness + oMLX

Валидация SCHEMA.md пройдена, 0 ошибок и 0 битых ссылок

~15 минут ожидания на документ и дублирование контуров конфигурации

4. Чистый SKILL.md + Cursor CLI

Основной чат управляет CLI

Быстрый ingest (~4,5 мин) в режиме --model auto

Цикл AwaitShell и пост‑обработка сжигают шаги и контекст основной модели

5. Тонкий skill + wrapper + CLI

run-ingest.sh оркестрирует, CLI меняет wiki/

Финальная схема

Ровно один вызов Shell из чата, детерминированный validate-wiki.py и автоиндексация QMD

Финальная схема: skills в Cursor

Итоговое решение получилось заметно проще:

Cursor chat
   │
   ├── /save-raw ───────→ raw/YYYY-MM-DD--slug.md
   │
   ├── /ingest-cli ─────→ run-ingest.sh
   │                         ├── Cursor Agent CLI (--model auto) → wiki/
   │                         ├── validate-wiki.py (детерминированный lint)
   │                         └── qmd update / embed / cleanup
   │
   ├── /context-cli ────→ run-context.sh → описания context в .qmd/index.yml
   │
   └── Query / Lint ────→ AGENTS.md + skill qmd → поиск, ответ с источниками и аудит

Операции были разделены между правилами хранилища (AGENTS.md) и четырьмя прикладными project skills в .cursor/skills/. Сам формат skill — это SKILL.md с YAML‑frontmatter, Markdown‑инструкциями и, при необходимости, вспомогательными скриптами; такая упаковка позволяет превратить длинный промпт или многошаговый пайплайн в повторно используемую операцию с понятным триггером.

Вместо одного универсального «обслужи wiki» появились четыре чётко разграниченных skill:

Skill

Назначение

save-raw

Сформировать аналитическую выжимку последних $N$ ответов текущего диалога и сохранить её в raw/

ingest-cli

Тонкая оболочка над run-ingest.sh: разобрать новые raw‑документы через изолированный Cursor Agent CLI, провалидировать wiki и обновить индекс QMD

context-cli

Тонкая оболочка над run-context.sh: сгенерировать и проаудировать навигационные описания context в .qmd/index.yml для папок и карточек wiki

qmd

Унифицировать работу агента с локальным поиском QMD (структурированные запросы intent/lex/vec/hyde и чтение срезов через get/multi-get)

При этом обычные исследовательские запросы к базе (Query) и смысловой аудит (Lint) не потребовали отдельных slash‑команд: они описаны напрямую в AGENTS.md и используют стандартный инструментарий чата в связке со skill qmd и MCP‑сервером.

Такое разделение оказалось принципиальным. У каждого skill есть один ожидаемый результат, а тяжёлые пакетные операции (ingest-cli и context-cli) вынесены в детерминированные обёртки со своими проверками.

Skill /save‑raw

Самой практичной частью повседневной работы стал save-raw. Он нужен в ситуации, когда полезный контекст появился прямо внутри диалога с Cursor: агент разобрал сложный SQL, нашёл ограничение модели данных, проверил прототип дашборда или сформулировал архитектурное решение.

Копировать всё вручную неудобно, а автоматически сохранять каждый ответ — значит быстро превратить raw/ в свалку. Поэтому операция запускается явно и принимает параметр N — количество последних содержательных ответов модели в текущей сессии (по умолчанию N = 1).

Пример вызова:

/save-raw 4

Skill выполняет несколько шагов:

  1. Берёт последние $N$ ответов ассистента вместе с исходными вопросами пользователя, исключая сам вызов команды (при необходимости сверяясь с транскриптом сессии).

  2. Очищает материал от диалогового шума, приветствий и служебных реплик, оставляя только фактологию, доменную логику, формулы и ссылки на объекты кода.

  3. Формирует заголовок, kebab‑case slug и раскладывает выжимку по фиксированной структуре из четырёх разделов.

  4. Сохраняет документ в raw/YYYY-MM-DD--<slug>.md и выводит краткий отчёт.

  5. Не редактирует содержимое файла после записи.

Пример создаваемого raw‑файла
# Аналитический аудит прототипа Power BI: расхождение расчёта категорий

**Дата составления:** 2026-10-16  
**Контекст:** Проверка логики расчёта категории между витриной DWH и OLAP-кубом.  
**Охват сессии:** Последние 4 ответа модели в сессии диалога Cursor  

---

## 1. Резюме и ключевые выводы
Расхождение появляется до загрузки в OLAP на этапе предрасчёта в витрине...

## 2. Детальный разбор
Для исторического периода используется версия классификатора на дату расчёта...

## 3. Технические артефакты и объекты
Таблицы `T_BI_CAT_MODELING`, процедура пересчёта, меры куба...

## 4. Открытые вопросы и дальнейшие шаги
Требуется подтвердить правило для ретроспективного пересчёта LFL...

Важно, что save-raw не пытается сразу раскладывать знания по карточкам wiki/. Строго говоря, для сессий в Cursor такой файл уже является производным артефактом модели, а не дословным логом чата: в данном workflow raw/ означает не обязательно побайтовую копию первичного канала, а неизменяемый входной снимок (snapshot) для последующего ingest. Для внешних встреч и документов таким артефактом выступает исходный транскрипт или заметка, а для диалогов в Cursor — зафиксированная в момент сохранения выжимка с указанием охвата сессии, тогда как полный первичный диалог при необходимости остаётся доступен в JSONL‑транскриптах самого IDE.

Разделение фиксации в raw/ и последующего синтеза в wiki/ защищает от смешивания двух этапов обработки: классификация, связывание с существующими сущностями и интеграция в граф выполняются отдельно на этапе ingest. Если схема wiki или логика разбора через месяц станут лучше, сохранённый в raw/ входной артефакт всегда можно пересобрать в карточки повторно.

Ingest и поддержка контекстов через Cursor CLI (/ingest‑cli и /context‑cli)

Оставался вопрос с разбором накопленных raw‑файлов. Выполнять многошаговый ingest прямо в текущем чате Cursor неудобно: операция читает много файлов, занимает контекстное окно и мешает основной задаче.

Здесь пригодился подход, который ранее уже разбирался в статье про генерацию технической документации на связке LangGraph + Cursor CLI + MCP: основной workflow запускает отдельный экземпляр Cursor Agent CLI и передаёт ему одну ограниченную задачу. В том проекте CLI‑агент вызывался итеративно для каждой меры Power BI, а управление оставалось во внешнем LangGraph‑контуре.

Почему одного SKILL.md оказалось мало

В LLM Wiki полноценный LangGraph оказался избыточным, однако и «чистая» реализация skill через один только SKILL.md с текстовой инструкцией вызвать Cursor CLI не подошла. В первой итерации /ingest-cli был написан именно так: предполагалось, что агент в чате прочитает SKILL.md, найдёт необработанные файлы, вызовет в терминале agent --model auto, а затем обновит индекс QMD. На практике выяснилось, что при такой схеме управление процессом и трассировка всех промежуточных шагов остаются за моделью основного чата, из которого вызывается команда:

  • Предварительные шаги до запуска CLI: основная модель сама выполняла Glob и чтение wiki/sources/*.md, чтобы сопоставить их с raw/ и собрать промпт для CLI (3–4 инструментальных шага);

  • Цикл ожидания фонового процесса: инструмент Shell в Cursor по умолчанию ждёт завершения команды 30 секунд (block_until_ms: 30000), после чего переводит её в фон. Поскольку разбор документа через CLI занимал около 4,5 минут, основная модель в чате каждую минуту просыпалась, вызывала AwaitShell и читала лог терминала (Read terminal), на каждом таком шаге заново прокачивая через себя весь накопленный контекст диалога;

  • Пост‑обработка после CLI: завершив вызов агента, основная модель тратила ещё несколько шагов на запуск qmd update && qmd embed, проверку ссылок и опрос MCP‑сервера.

В результате вместо экономии получалось около десятка лишних шагов тяжёлой модели основного чата, хотя вся идея заключалась в том, чтобы переложить работу на CLI в режиме auto.

Что делает run‑ingest.sh

Чтобы свести участие модели основного чата к ровно одному инструментальному вызову, всю оркестрацию пришлось вынести из SKILL.md во внешний bash/python‑wrapper run-ingest.sh. Сам SKILL.md превратился в жёсткую «тонкую оболочку»: агенту чата запрещено самостоятельно исследовать raw/, wiki/ или терминалы — его единственное действие при вызове /ingest-cli состоит в однократном запуске run-ingest.sh с увеличенным таймаутом ожидания (block_until_ms: 1800000 и required_permissions: ["all"], поддерживаются также режимы --plan, --force и передача конкретного файла).

Скрипт run-ingest.sh внутри одного процесса терминала берёт всю механику на себя:

  1. Находит в raw/ все .md и .txt файлы (кроме assets/), для которых в wiki/sources/*.md ещё нет карточки с соответствующим полем resource: raw/....

  2. Для каждого нового файла поочерёдно запускает изолированный процесс Cursor Agent CLI с моделью auto;

  3. При этом вложенному агенту явно запрещено трогать raw/, правила схемы или запускать индексацию. Его задача — только обновить карточки wiki/, каталог index.md и журнал log.md;

  4. После того как агент завершил запись, внешний скрипт сам запускает детерминированный валидатор validate-wiki.py, а затем выполняет обслуживание индекса (qmd update, qmd embed -c pharmacy-categories, qmd cleanup) и сравнивает контрольные суммы файлов до и после запуска, выводя в чат аккуратную сводку созданных и обновлённых страниц.

Пример итогового вывода run‑ingest.sh
Wiki validation passed: 4 sources, 13 indexed cards, 0 errors.

Ingest завершён успешно.
Обработанные ресурсы:
- raw/2026-10-17--powerbi-prototype-audit-against-customer-mockup.md
Изменённые wiki-файлы:
- wiki/analyses/scenarios-open-questions-and-limitations.md (updated)
- wiki/concepts/counterfactual-category-simulation.md (updated)
- wiki/concepts/demand-price-elasticity.md (created)
- wiki/concepts/like-for-like-comparison.md (updated)
- wiki/entities/olap-cube.md (updated)
- wiki/entities/oracle-dwh.md (updated)
- wiki/entities/pharmacy-category.md (updated)
- wiki/entities/power-bi-category-modeling-prototype.md (created)
- wiki/index.md (updated)
- wiki/log.md (updated)
- wiki/overview.md (updated)
- wiki/sources/2026-10-17--powerbi-prototype-audit-against-customer-mockup.md (created)
QMD: документов — 17, векторов — 17.

Локальный DSH против Cursor CLI

Отдельно стоит отметить флаг --model auto. В моём тарифе и сценарии использования этот режим оказался самым практичным компромиссом для обслуживания базы знаний: он даёт достаточное качество при разборе источников, связывании карточек и соблюдении SCHEMA.md, не расходует квоту выбранной вручную флагманской модели из основного coding‑чата и при этом работает заметно быстрее локального инференса.

Нагляднее всего разницу показал контрольный прогон на одном и том же состоянии базы (10 предметных карточек, 119 wikilinks) и одном входном документе из raw/ (~6,2 КБ, 89 строк с таблицей трассировки требований и формулами). Оба контура — локальный DSH + mlx-community/Qwen3.8-27B-OptiQ-4bit + oMLX (с отключённым reasoning и предварительно прогретым сервером на Mac Studio M4 Max) и Cursor Agent CLI с --model auto — работали по одним и тем же правилам AGENTS.md и SCHEMA.md поверх общей коллекции QMD:

Метрика

DSH + mlx-community/Qwen3.8-27B-OptiQ-4bit + oMLX

Cursor Agent CLI (--model auto)

Общее время выполнения (Wall time)

~15 мин 45 сек (LLM time: 14m 56s, Tool time: 48.3s, 33 шага агента)

~4 мин 36 сек (276.6s, включая qmd update и embed)

Выделение новых страниц

1 карточка источника (10 → 11 pages)

1 карточка источника + 2 новые карточки (entity и concept, 10 → 13 pages)

Обновление связей в графе

Точечно обновлены 3 предметные карточки (всего затронуто 7 файлов)

Сквозное обновление 6 предметных карточек + актуализация analyses/ (всего затронуто 11 файлов)

Соблюдение SCHEMA.md и целостность ссылок

validate-wiki.py: 0 ошибок; битые wikilinks не обнаружены

validate-wiki.py: 0 ошибок; битые wikilinks не обнаружены (184 ссылки в графе)

Разумеется, это не синтетический бенчмарк моделей, а одиночный контрольный замер wall time двух конкретных рабочих контуров: на результат влияют также реализация agent loop, механика tool calling и то, насколько глубоко каждая среда решила обновить смежные карточки графа. Тем не менее для повседневной работы разница оказалась наглядной — около 4,5 минут против 15 с лишним минут на локальной 27B‑модели при более полной проработке связей.

Как обновляется QMD context

По такому же принципу устроен и второй инфраструктурный skill — /context-cli (run-context.sh + context_cli.py). Как упоминалось выше, QMD возвращает вместе с результатами поиска навигационные описания context из .qmd/index.yml. Поддерживать их вручную при росте базы утомительно, а перегенерировать описания для всех страниц после каждого ingest — накладно. Поэтому context_cli.py хранит в .qmd/context-cli-state.json слепок content_sha256 и context_sha256 для каждой карточки и папки wiki/:

  • в штатном режиме (без флагов) скрипт находит только новые объекты, у которых ещё нет записи в context, и дописывает их;

  • в режиме --audit он сверяет текущие SHA-256 хеши файлов с сохранённым состоянием, помечает только реально изменившиеся карточки и их родительские директории и передаёт в read‑only вызов Cursor Agent CLI --model auto строго этот дельта‑список.

Например, когда после ingest в базе появилась карточка concepts/demand-price-elasticity.md и обновилась entities/power-bi-category-modeling-prototype.md, скрипт не заставляет модель перечитывать все 17 документов коллекции, а точечно обновляет в .qmd/index.yml только затронутые ключи:

context:
  concepts: "Слой концепций: ключевые бизнес-термины, правила и алгоритмы расчетов (LFL, сценарное What-If моделирование, регламентный перерасчет категорий)."
  concepts/demand-price-elasticity.md: "Коэффициент ε чувствительности объёма спроса к изменению цены при смене категории аптеки: в прототипе — What-If слайдер [0; 1.5] для корректировки расчётной валовой прибыли и поиска точки безубыточности перевода."
  entities/power-bi-category-modeling-prototype.md: "Интерактивный HTML/Chart.js прототип из шести экранов сценарного моделирования категорий аптек; эталон UX и метрик для целевого PBIX с покрытием макета заказчика около 98–100%."

Зачем вызывать Cursor CLI из Cursor

Причина не в рекурсии ради рекурсии. Вынос операции в отдельный процесс под управлением скрипта даёт несколько полезных свойств:

  • ingest и аудит контекстов не загрязняют контекстное окно текущего рабочего чата;

  • каждому вызову CLI‑агента передаётся ровно один raw‑файл и одна атомарная задача;

  • валидация структуры (validate-wiki.py) и переиндексация QMD гарантированно выполняются детерминированным скриптом после работы модели, а не зависят от того, не забыла ли LLM вызвать команду в конце длинного промпта;

  • одна и та же команда одинаково запускается из чата через /ingest-cli, вручную из терминала или по расписанию;

  • нет второго UI и отдельной системы профилей — используются те же AGENTS.md, SCHEMA.md и MCP‑настройки репозитория.

То есть связка run-ingest.sh + Cursor Agent CLI работает здесь как изолированный конвейер сборки знаний со встроенным контролем качества.

Что происходит внутри ingest

Если внешний скрипт run-ingest.sh отвечает за детерминированную оркестрацию, валидацию и индексацию, то задача самого CLI‑агента внутри вызова — не просто пересказать документ, а аккуратно встроить новый материал в существующий граф знаний без дублей.

Поэтому перед созданием любой новой страницы агент обязан выполнить поиск по уже накопленной базе. Например, новый материал может упоминать Like‑for‑Like, таблицу T_BI_CAT_MODELING и What‑If‑сценарий. Вместо трёх новых почти одинаковых страниц агент сначала ищет существующие карточки через QMD:

qmd query "Like-for-Like T_BI_CAT_MODELING What-If" \
  -c pharmacy-categories -n 10

После этого полные кандидаты извлекаются через qmd get или multi_get. Такой порядок — search, затем retrieve — закреплён в skill qmd и не даёт базе расползаться на дублирующиеся заметки.

Как мы уже условились в начале статьи, подробно разбирать внутреннюю механику базовых операций ingest, query и lint здесь не будем — канонические правила работы с карточками, перекрёстными ссылками и разрешением противоречий детально описаны в исходной концепции LLM Wiki и других обзорных публикациях. В нашем проекте все эти регламенты зафиксированы в SCHEMA.md и AGENTS.md:

  • Сборка и обновление графа (ingest): создание карточки источника (resource: raw/<filename>), слияние новых фактов с существующими карточками entities, concepts и analyses (с инкрементом source_count, пополнением Citations и маркировкой противоречий contested: true), а также обновление index.md и log.md;

  • Двухэтапный поиск и чтение (query): формирование комбинированного запроса (intent, lex, vec, hyde с фильтром по коллекции) по правилам qmd/SKILL.md, чтение выбранных карточек через qmd get / multi_get и синтез ответа со ссылками [[wikilinks]];

  • Контроль целостности (lint): сочетает автоматическую проверку структуры и ссылок скриптом validate-wiki.py на каждом прогоне с периодическим семантическим аудитом по регламенту AGENTS.md (поиск смысловых расхождений, осиротевших карточек и пропущенных связей).

Сквозной пример

Рассмотрим упрощённый сценарий из задачи по категориям аптечной сети.

Во время установочной встречи обсуждался отчёт по категориям и Like‑for‑Like‑сравнение. Транскрипт был получен через AudioToText и сохранён в raw/.

Первый ingest создал:

wiki/sources/2026-09-21--requirements-meeting.md

wiki/concepts/like-for-like-comparison.md

wiki/entities/tb-icat-modeling.md

wiki/analyses/category-comparison-requirements.md

Позже в Cursor разбирался SQL‑процесс. Выяснилось, что исторические данные используют другой вариант классификатора. Обсуждение оказалось важнее конкретного куска кода, поэтому последние четыре сообщения были сохранены командой:

/save-raw 4

После вызова /ingest-cli запустился изолированный ingest через Cursor CLI. Агент нашёл существующие страницы по Like‑for‑Like и T_BI_CAT_MODELING, обновил их, добавил секцию с расхождением правил, создал analysis page и дополнил log.md, а скрипт провалидировал ссылки и обновил индекс QMD.

В новой Cursor‑сессии вопрос:

Какая версия классификатора должна использоваться при ретроспективном расчёте 
Like-for-Like?

уже не требовал повторно прикладывать транскрипт и SQL. Агент через QMD нашёл две тематические страницы и вернул не только текущее предположение, но и незакрытое противоречие между постановкой и фактической реализацией.

Именно в этот момент wiki начала приносить пользу. Не тогда, когда в каталоге появились красивые Markdown‑файлы, а когда новая сессия смогла восстановить ход решения без пересказа всей истории.

Что оказалось лишним

Эксперимент помог отделить обязательные компоненты от тех, которые были добавлены скорее из архитектурного энтузиазма.

Отдельный agent harness

DSH был полезен как лабораторный стенд, но для текущего объёма источников оказался лишним эксплуатационным слоем. Если wiki будет обрабатывать сотни документов по расписанию, к нему можно вернуться. Для интерактивного проектного workflow проще использовать Cursor CLI как исполнителя.

Локальная модель для всей операции

Как уже описано выше, после смены модели вопрос качества локального ingest был снят, но осталось время ожидания. Для интерактивного цикла внутри IDE это оказалось решающим: запуск через Cursor CLI в режиме --model auto возвращает результат за минуты, а не за десятки минут. Локальный контур разумнее оставить для пакетной обработки без спешки.

Автоматическое сохранение всех диалогов

Полный журнал чатов создаёт ощущение надёжной памяти, но ухудшает соотношение сигнал/шум. Явный /save-raw N заставляет решить, действительно ли найденный контекст заслуживает долговременного хранения.

Что получилось в итоге

Финальная архитектура укладывается в четыре слоя без внешних агентных фреймворков:

┌─────────────────────────────────────────────┐
│ Cursor                                      │
│ coding, обсуждение, /save-raw, запуск skill │
└──────────────────┬──────────────────────────┘
                   │
┌──────────────────▼──────────────────────────┐
│ Wrapper-скрипты + Cursor Agent CLI          │
│ изолированный ingest, validate-wiki.py      │
└──────────────┬───────────────────┬──────────┘
               │                   │
┌──────────────▼──────────┐  ┌─────▼──────────┐
│ LLM Wiki               │  │ QMD            │
│ sources/entities/      │  │ BM25/vector/   │
│ concepts/analyses      │  │ reranking      │
└──────────────┬──────────┘  └─────┬──────────┘
               │                   │
┌──────────────▼───────────────────▼──────────┐
│ Raw sources + schema + append-only log      │
└─────────────────────────────────────────────┘

В сравнении с первоначальным вариантом исчезли отдельный UI DSH, дублирующий набор DSH‑skills и второй контур конфигурации. При этом сохранились все полезные свойства эксперимента: жёсткая граница между фиксацией источника (/save-raw) и компиляцией знания (/ingest-cli), QMD как единый retrieval‑слой и выполнение тяжёлых операций в изолированном процессе с детерминированной проверкой.

Это решение нельзя назвать бесплатной памятью. Wiki требует схемы, контроля источников и периодической проверки. Но обслуживание становится предсказуемой инженерной операцией, а не просьбой к модели «пожалуйста, ничего не забудь».

Вместо заключения

Изначальная идея была довольно прямой: подключить LLM Wiki, добавить локальный поиск и поручить дешёвой модели раскладывать документы. По пути получилась небольшая распределённая система из двух агентных сред, двух наборов skills, MCP, inference‑сервера и нескольких конфигураций.

Работать она могла. Пользоваться ею регулярно — уже другой вопрос.

Финальное решение оказалось менее эффектным: один рабочий интерфейс, четыре прикладных skills, QMD и отдельный вызов Cursor CLI для тяжёлых операций. Но именно такая схема пережила несколько реальных циклов сохранил контекст → обработал источник → продолжил задачу в новой сессии.

Главный вывод для меня здесь не в выборе конкретного инструмента. Долговременная память coding agent начинается не с векторной базы и не с размера контекстного окна — она держится на разделении ответственности между моделью и детерминированным кодом:

  • осознанная фиксация контекста: в неизменяемый raw/ попадает не весь поток чатов, а явно отобранная выжимка (/save-raw);

  • двухэтапный retrieval: поиск через QMD только отбирает кандидатов с учётом context, а ответ и обновление графа строятся после чтения конкретных страниц;

  • изоляция и жёсткая оркестрация: LLM отвечает только за смысловое слияние фактов в изолированном вызове CLI, а порядок шагов, проверку инвариантов (validate-wiki.py) и переиндексацию гарантируют обычные wrapper‑скрипты.

Когда эти правила выполняются, LLM Wiki перестаёт быть папкой с заметками. Она становится частью рабочего контура разработки — наряду с кодом, тестами и документацией.

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  • 30.07.26 00:04 Ahmed

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  • 04.08.26 11:05 Kisnoles

    Excellent analysis, thank you. I was particularly struck by how rigidly the skill sets the order: data processing and form come first, with color coming last. This really cures the habit of "first a pretty palette, then we'll figure it out." A palette validator using OKLCH + color blindness + WCAG is something most chart generators lack. And regarding the boundaries of competence, it's very clear. Where there's a self-checking loop (color), you delegate freely. Where there's a heuristic (form, data interpretation), you remain the final filter. This is a universal principle, not just for /dataviz. By the way, when you look at trading dashboards (including those of decent platforms like ExpertOption), it's immediately clear who thought about readability and who just threw in rainbow lines. Tools like this skill could greatly improve the quality of analytics. Thanks again for the detailed analysis – saved.

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  • 18.08.26 04:59 marcushenderson624

    Bitcoin Recovery Testimonial After falling victim to a cryptocurrency scam group, I lost $354,000 worth of USDT. I thought all hope was lost from the experience of losing my hard-earned money to scammers. I was devastated and believed there was no way to recover my funds. Fortunately, I started searching for help to recover my stolen funds and I came across a lot of testimonials online about Capital Crypto Recovery, an agent who helps in recovery of lost bitcoin funds, I contacted Capital Crypto Recover Service, and with their expertise, they successfully traced and recovered my stolen assets. Their team was professional, kept me updated throughout the process, and demonstrated a deep understanding of blockchain transactions and recovery protocols. They are trusted and very reliable with a 100% successful rate record Recovery bitcoin, I’m grateful for their help and highly recommend their services to anyone seeking assistance with lost crypto. Contact: [email protected] Phone WhatsApp/Text Number: +1 (336) 390-6684 Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 18.08.26 04:59 marcushenderson624

    Bitcoin Recovery Testimonial After falling victim to a cryptocurrency scam group, I lost $354,000 worth of USDT. I thought all hope was lost from the experience of losing my hard-earned money to scammers. I was devastated and believed there was no way to recover my funds. Fortunately, I started searching for help to recover my stolen funds and I came across a lot of testimonials online about Capital Crypto Recovery, an agent who helps in recovery of lost bitcoin funds, I contacted Capital Crypto Recover Service, and with their expertise, they successfully traced and recovered my stolen assets. Their team was professional, kept me updated throughout the process, and demonstrated a deep understanding of blockchain transactions and recovery protocols. They are trusted and very reliable with a 100% successful rate record Recovery bitcoin, I’m grateful for their help and highly recommend their services to anyone seeking assistance with lost crypto. Contact: [email protected] Phone WhatsApp/Text Number: +1 (336) 390-6684 Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 19.08.26 14:50 BAYER7043

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  • 20.08.26 11:32 michaeldavenport218

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  • 20.08.26 11:32 michaeldavenport218

    I was recently scammed out of $53,000 by a fraudulent Bitcoin investment scheme, which added significant stress to my already difficult health issues, as I was also facing cancer surgery expenses. Desperate to recover my funds, I spent hours researching and consulting other victims, which led me to discover the excellent reputation of Capital Crypto Recover, I came across a Google post It was only after spending many hours researching and asking other victims for advice that I discovered Capital Crypto Recovery’s stellar reputation. I decided to contact them because of their successful recovery record and encouraging client testimonials. I had no idea that this would be the pivotal moment in my fight against cryptocurrency theft. Thanks to their expert team, I was able to recover my lost cryptocurrency back. The process was intricate, but Capital Crypto Recovery's commitment to utilizing the latest technology ensured a successful outcome. I highly recommend their services to anyone who has fallen victim to cryptocurrency fraud. For assistance contact [email protected] and on Telegram OR WhatsApp Number +1 (336)390-6684 via email: [email protected] you can visit his website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 23.08.26 20:02 leslieyee

    Excellent analysis by /dataviz! I was particularly struck by how strictly the order is defined: "data meaning first, color last" and how the validator actually adjusts the palette according to OKLCH, color blindness, and WCAG standards. These rules greatly improve the quality of charts. By the way, a similar approach to clean and legible charts is highly valued in trading—for example, on the ExpertOption platform, the interface and price visualization are designed to ensure information is read instantly and without unnecessary noise.

  • 25.08.26 13:44 lydiassmith567

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  • 25.08.26 13:44 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 30.08.26 15:37 [email protected]

    Discovering I had been defrauded, I searched online for services claiming to recover stolen ETH. I contacted several companies, but none succeeded. Although some claimed they could trace assets or recover funds, I could not verify their success or recover my money. I later found SYLVESTER BRYANT Recovery through Google. After contacting him, he recovered 450,000,00. He can be reached at [email protected] or WhatsApp at +1 512 577 7957.

  • 30.08.26 15:37 [email protected]

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  • 01.09.26 11:16 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 01.09.26 11:16 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 01.09.26 17:34 Garry42

    Really crazy world. These fraudsters go at any length to steal your hard earned funds. I have been a victim of a bitcoin scam about 7 months back. A Con artist gained access to my cashapp account through a phishing scam. They stole $409,000. I was really devastated. I did everything to get back my funds by contacting the FBI but they claimed there was nothing they could do. A friend told me about a recovery expert. He helps fight against various phishing and investment scams and they were able to help trace and recover my funds even though it took over 2 days. you can reach out to him through recoverydarek@gmail. com . I can guarantee his services are still active.

  • 01.09.26 17:34 Garry42

    Really crazy world. These fraudsters go at any length to steal your hard earned funds. I have been a victim of a bitcoin scam about 7 months back. A Con artist gained access to my cashapp account through a phishing scam. They stole $409,000. I was really devastated. I did everything to get back my funds by contacting the FBI but they claimed there was nothing they could do. A friend told me about a recovery expert. He helps fight against various phishing and investment scams and they were able to help trace and recover my funds even though it took over 2 days. you can reach out to him through recoverydarek@gmail. com . I can guarantee his services are still active.

  • 02.09.26 03:43 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 02.09.26 03:43 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 04.09.26 21:51 Kovengray

    Recovering your lost investment funds as the case might be, is not what you can do alone, you’d require the service of a trained recovery specialist. A recovery specialist is a person or a group of people who are well equipped to work around the brokerage network. They have vast knowledge about the whole network and have the right software and private keys to follow any transaction. I was ripped off trading online to an investment broker, good thing I got every penny back through the help of Gavin ray he’s a genius Contact : Gavinray78 at gmail com or WhatsApp +1 352 322 2096 It is also important to be patient and really calm during the process.

  • 05.09.26 21:38 [email protected]

    I invested 45,000  Euro, and later aggreviated to 198,000 Euro.  I requested  to place ‎Withdrawal of my funds to be paid to my Bank account. But nothing happened I was subjected to pay more fee until I can get my funds on my account, i got in touch with Theodore ryan here on this platform who had helped a lot of people, I followed all instructions and he legally got back my withheld funds, I got threatened by the company that if I don’t pay they will get my account frozen, all thanks to Theodore ryan and I highly recommend him to anyone dealing with an unregulated broker company… contact him on his Gmail - theodoreryan318@  gmail .  com

  • 09.09.26 21:31 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 09.09.26 21:31 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 09.09.26 23:22 Fraddy Pual

    Hearing that these individuals are focusing on other people makes me very sad. I had a similar situation with them and lost a lot of money, but after learning about (Cruxcipherteam @ proton DoT me), whataqq:+168160-15021, telegram: @Cruxcipherteam I was able to get my money back. It's critical that we all be watchful and keep reporting these occurrences.

  • 11.09.26 03:23 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 11.09.26 03:23 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 15.09.26 03:35 elioduncan

    I fell victim to a freelance web development contract scam that ultimately cost me CAD 4,000. It all started when I came across a job posting on Indeed Canada, a popular online job portal. The position seemed perfect: a freelance web developer role for an established company looking for someone to design and build a fully functional e-commerce website. The job promised a high payment of CAD 20,000 upon successful completion, which sounded like a great opportunity for me to gain experience and earn decent pay.The employer, who introduced himself as a project manager from a "well-known" tech company, was very persuasive and professional in our initial communication. He explained that the project involved developing a user-friendly, responsive online store for their client and that they had a strict timeline to meet. He assured me that the payment would be made promptly after completing the tasks. However, before starting the work, he told me that I would need to pay an upfront fee of CAD 4,000 to cover certain software tools and licensing fees required for the project. This was supposedly a part of their company policy for freelance contractors, ensuring access to their premium resources. The idea of working on a professional project, coupled with the promise of a substantial payout, convinced me to pay the upfront fee.As soon as I made the payment, the project manager became increasingly difficult to reach. He initially responded to my emails and provided some vague instructions on what the project would entail, but as time went on, communication slowed to a complete halt. I never received the required tools or any proper project details. My emails went unanswered, and any attempts to contact the company were met with silence. After waiting for weeks, I realized that I had been scammed.Desperate to recover my money, I turned to TechY Force Cyber Retrieval, a service that specializes in helping victims of online scams. They helped me track the payment and took legal steps to pursue the fraudsters. Through their guidance, I was able to recover the full CAD 4,000. TechY Force Cyber Retrieval worked with my bank to reverse the transaction and liaised with the authorities to trace the scammer's details.This was a hard lesson, and I now know to be highly cautious about online job offers that require upfront payments. It is crucial to research companies thoroughly and avoid any job that seems too good to be true, especially when it involves paying money upfront. WhatsApp https://wa.link/2x6ktp Mail. [email protected]

  • 15.09.26 15:45 lydiassmith567

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  • 15.09.26 15:45 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 05.10.26 21:26 [email protected]

    I Invested 45,000  Euro, and later aggreviated to 198,000 Euro.  I requested  to place Withdrawal of my funds to be paid to my Bank account. But nothing happened I was subjected to pay more fee until I can get my funds on my account, i got in touch with Theodore ryan here on this platform who had helped a lot of people, I followed all instructions and he legally got back my withheld funds, I got threatened by the company that if I don’t pay they will get my account frozen, all thanks to Theodore ryan and I highly recommend him to anyone dealing with an unregulated broker company… contact him on his Gmail - theodoreryan318@  gmail .  com

  • 06.10.26 06:13 melissathomason573

    Professional Crypto Recovery Testimonial After losing a significant amount of cryptocurrency through a scam on Twitter, I was unsure whether my Bitcoin funds could ever be recovered. The experience was extremely stressful, particularly because I encountered several individuals and services claiming they could help recover my assets, only to discover that some were fraudulent themselves. A friend eventually referred me to CAPITAL CRYPTO RECOVER. Their team handled my case professionally, discreetly, and efficiently. With their assistance, I successfully recovered approximately $130,000 worth of USDT in just over a week. My experience taught me the importance of being extremely cautious when seeking cryptocurrency recovery services, as scammers often target victims a second time. I am grateful to CAPITAL CRYPTO RECOVER for the assistance I received and wanted to share my experience with others who may be facing a similar situation. If you’ve been compromised, don’t lose hope—and be careful of fraudsters posing as saviors. CAPITAL CRYPTO RECOVER are real professionals in crypto recovery. I am a living proof of their effectiveness, you can reach them through Phone (Call/WhatsApp): +1 (336) 390-6684 Email: [email protected] Alternate Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 06.10.26 06:13 melissathomason573

    Professional Crypto Recovery Testimonial After losing a significant amount of cryptocurrency through a scam on Twitter, I was unsure whether my Bitcoin funds could ever be recovered. The experience was extremely stressful, particularly because I encountered several individuals and services claiming they could help recover my assets, only to discover that some were fraudulent themselves. A friend eventually referred me to CAPITAL CRYPTO RECOVER. Their team handled my case professionally, discreetly, and efficiently. With their assistance, I successfully recovered approximately $130,000 worth of USDT in just over a week. My experience taught me the importance of being extremely cautious when seeking cryptocurrency recovery services, as scammers often target victims a second time. I am grateful to CAPITAL CRYPTO RECOVER for the assistance I received and wanted to share my experience with others who may be facing a similar situation. If you’ve been compromised, don’t lose hope—and be careful of fraudsters posing as saviors. CAPITAL CRYPTO RECOVER are real professionals in crypto recovery. I am a living proof of their effectiveness, you can reach them through Phone (Call/WhatsApp): +1 (336) 390-6684 Email: [email protected] Alternate Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 06.10.26 17:44 Darrell1717

    I fell victim of a scam I invested to total of 5thousand into. I know it could be risky but after I trusted this man I was dating online he lured me into investing with him. I know hard I worked of my money so I wanted every way possible to get it back. I really appreciate Blockrecoveryexpert for successfully recovered my funds form this scammer. This is all I can say. You can reachout if you ever need help contact blockrecoveryexpert (at) gmail (dot)com

  • 11.10.26 03:04 Patricia014

    Most recovery companies will take your money again. I personally think they’re the same set of people running all of them. It’s a sad, unending cycle, and so many people fall for cloned websites. The only recovery agent I know is Agent Rottgen Raphael. I’ve visited their physical company address, and they’ll tell you if they can handle your case instead of taking upfront payment when they know they can’t help. I highly appreciate their support in recovering my money from the scammers’ wallet within 24 hours. RottgenRaphael@Gmail. com

  • 12.10.26 19:34 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 12.10.26 19:34 lydiassmith567

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  • 13.10.26 14:14 lisalisa22

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  • 17.10.26 12:58 melissathomason573

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  • 17.10.26 12:58 melissathomason573

    Professional Crypto Recovery Testimonial After losing a significant amount of cryptocurrency through a scam on Twitter, I was unsure whether my Bitcoin funds could ever be recovered. The experience was extremely stressful, particularly because I encountered several individuals and services claiming they could help recover my assets, only to discover that some were fraudulent themselves. A friend eventually referred me to CAPITAL CRYPTO RECOVER. Their team handled my case professionally, discreetly, and efficiently. With their assistance, I successfully recovered approximately $130,000 worth of USDT in just over a week. My experience taught me the importance of being extremely cautious when seeking cryptocurrency recovery services, as scammers often target victims a second time. I am grateful to CAPITAL CRYPTO RECOVER for the assistance I received and wanted to share my experience with others who may be facing a similar situation. If you’ve been compromised, don’t lose hope—and be careful of fraudsters posing as saviors. CAPITAL CRYPTO RECOVER are real professionals in crypto recovery. I am a living proof of their effectiveness, you can reach them through Phone (Call/WhatsApp): +1 (336) 390-6684 Email: [email protected] Alternate Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

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