compose-outreach
Подготовить персональное обращение к клиенту
Что получите
Письмо с темой, сценарий звонка и сообщение LinkedIn короче 300 символов, дополненные пояснениями и альтернативными подходами.
Что передать
Имя контакта или компания; при наличии — контекст вашего продукта и цель обращения.
Проверен состав файлов. Запуск не тестировался. Что проверено
Оценки пользователей
Как использовать
Помогает команде продаж написать обращение к конкретному человеку или компании на основе данных Common Room. Использует недавние действия, историю контактов и события, связанные с ценностью вашего продукта.
- Укажите адресата; если известна только компания, попросите подобрать подходящего сотрудника.
- Попросите выбрать наиболее свежие и конкретные поводы для обращения, связанные с вашим предложением.
- Получите варианты для трёх каналов и краткое объяснение выбранных поводов и предположений.
Условия работы
- Нужен доступ к Common Room через MCP; Spark используется при наличии.
- При недостатке данных Common Room нужен поиск в интернете; по прямой просьбе пользователя он выполняется независимо от полноты данных.
- Если и поиск не даёт достаточных оснований, агент запрашивает дополнительный контекст вместо создания персонализации из вымышленных событий.
Пример запроса после установки
Подготовь обращение к руководителю разработки компании «Север»: используй недавние реальные события и предложи письмо, сценарий звонка и короткое сообщение LinkedIn.
Пример составлен по инструкции автора; результат запуска не проверялся.
Основание: инструкция закреплённой версииДля установки
Node.js, npm и Git для команды выше. Подключения и инструменты для работы скилла настраиваются отдельно.
Совместимые агенты
Совместимость указана для инструкций. Запуск в каждом приложении не тестировался; для веб-чата способ подключения может отличаться.
Файлы скилла
GitHubЧитать инструкцию SKILL.md
Исходный текст закреплённой версии.
--- name: compose-outreach description: "Generate personalized outreach messages using Common Room signals. Triggers on 'draft outreach to [person]', 'write an email to [name]', 'compose a message for [contact]', or any outreach drafting request." --- # Compose Outreach Generate three personalized outreach formats — email, call script, and LinkedIn message — grounded in Common Room signals for a specific company or contact. ## Outreach Process ### Step 1: Look Up the Target Use Common Room MCP tools to find and retrieve data for the target (company and/or specific contact). Pull: - Recent product activity and engagement signals - Community activity (posts, questions, reactions) - 3rd-party intent signals (job postings, news, funding) - Relationship history (prior contact, meetings, email opens) If the user specified a person, run contact-level research. If only a company was given, identify the best contact to target based on title, engagement, and role. ### Step 2: Web Search for External Hooks (If CR Signals Are Thin) If CR returned strong signals (recent activity, engagement, product usage), those should drive personalization — skip web search. If CR signals are thin or the prospect has little CR activity, run a web search for external hooks: **What to search:** - `"[company name]" funding OR acquisition OR launch OR announcement` — last 30 days - `"[contact full name]" "[company name]"` — look for recent articles, interviews, LinkedIn posts, or conference talks **Prioritize external hooks that are:** - Very recent (< 2 weeks) — the prospect is likely still thinking about it - Publicly visible — they know you could have seen it - Change-signaling — growth, new role, new product, new market If the user explicitly asks for web search or external hooks, run it regardless of CR signal richness. ### Step 3: Spark Enrichment (If Available) If Spark is available, run enrichment on the target contact to get persona classification, background, and influence signals. Use this to calibrate tone and message angle. ### Step 4: Identify the Best Hooks From the signal data, identify the 1–3 strongest personalization hooks. Rank by: 1. **Recency** — happened in the last 7–14 days 2. **Specificity** — a concrete action they took, not a general trend 3. **Relevance** — connects directly to a value your product delivers Good hooks: posted a question in the community about X, just hired 5 engineers, recently started using [feature], company just raised Series B, trial nearing expiration, champion just changed jobs. Bad hooks: "I noticed you're a customer" or generic industry trends. ### Step 5: Generate All Three Formats Use the strongest hooks to write all three formats. Each format has different constraints and conventions — follow the format-specific guidelines in `references/outreach-formats-guide.md`. Always produce all three, clearly labeled. When the user's company context is available (see `references/my-company-context.md`), ground the value bridge and pitch in the user's specific product and positioning. ### Step 6: Annotate Your Choices After the three drafts, include a brief note (2–4 sentences) explaining: - Which signals were used and why they were chosen - Any assumptions made (e.g., inferred call objective) - Alternative angles if the primary hook doesn't land ## Output Format ``` ## Outreach for [Name / Company] ### 📧 Email **Subject:** [Subject line] [Email body — 3–5 sentences] --- ### 📞 Call Script **Opening:** [Opening line — conversational, 1–2 sentences] **Value Bridge:** [Why you're calling and why now — 2–3 sentences tied to a signal] **Ask:** [Single, low-friction ask — e.g., 15-minute call, specific question] --- ### 💼 LinkedIn Message [Under 300 characters. Warm, personal, no pitch.] --- ### Signal Notes [2–4 sentences: which signals were used, why, and any alternative angles] ``` ## When Signal Data Is Sparse If Common Room returns minimal data on the target (e.g., just name, title, tags — no activity, no scores, no Spark): 1. **Do not draft outreach from thin air.** Outreach grounded in fabricated signals is worse than no outreach. 2. **Run web search first** — this becomes your primary personalization source. Look for recent news, LinkedIn posts, conference talks, company announcements. 3. **If web search also returns little**, present what you have honestly and ask the user for context: ``` ## Outreach for [Name / Company] — Limited Data **What I found:** [Only the real data from CR and web search] **I don't have enough signal to draft personalized outreach yet.** To write something strong, I'd need: - Recent activity or engagement signals - Context you have from prior conversations - A specific reason for reaching out now Can you share any of the above? ``` ## Quality Standards - Every message must reference something specific — generic outreach is not acceptable output - Match tone to context: warm and conversational for inbound/community signals; more formal for cold/executive outreach - The LinkedIn message must be under 300 characters — no exceptions - The call script must be speakable naturally — read it aloud mentally to check rhythm - **Never fabricate signals** — only reference data retrieved from Common Room or web search ## Reference Files - **`references/outreach-formats-guide.md`** — detailed format rules, examples, and tone guidelines for each channel
Проверка и особенности
Автоматическая проверка источника и файлов
Автоматически сверены публичный репозиторий, лицензия, YAML-заголовок SKILL.md, состав папки и Git-хеш каждого файла. Исполняемые файлы и срабатывания статических правил исключены из этой партии. Ручной аудит всех инструкций и проверка работы не проводились; эта проверка не гарантирует безопасность. Команда закреплена на проверенном коммите.
Интерес к репозиторию оцениваем по звёздам и форкам GitHub. Это показатели всего репозитория, а не числа установок отдельного скилла.
- Проверка состава
- 16.09.2026
- Проверенная версия
c55597c2697c- Файлы с кодом
- Не указаны в данных проверки
- Изменения на GitHub
- Нужна повторная проверкаПоследняя проверка: 11.10.2026
Репозиторий и лицензия
Это показатели всего репозитория, а не установки скилла. Снимок требует обновления.
- Последняя активность GitHub
- 11.10.2026
- Лицензия
- Apache-2.0
- Добавлен в каталог
- 16.09.2026
Полный текст лицензии
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