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sequence-load

Find leads matching criteria and bulk-add them to an Apollo outreach sequence. Handles enrichment, contact creation, deduplication, and enrollment in one flow.

DeepseekModel 官方收录技能 质量 优秀 · 90 v1.0.0

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https://deepseekmodel.com/api/download.php?id=anthropics-knowledge-work-plugins-partner-built-apollo-skills-sequence-load-skill-md&format=skill
下载 .skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
name sequence-load description Find leads matching criteria and bulk-add them to an Apollo outreach sequence. Handles enrichment, contact creation, deduplication, and enrollment in one flow. user-invocable true argument-hint [targeting criteria + sequence name] Sequence Load Find, enrich, and load contacts into an outreach sequence — end to end. The user provides targeting criteria and a sequence name via "$ARGUMENTS". Examples /apollo:sequence-load add 20 VP Sales at SaaS companies to my "Q1 Outbound" sequence /apollo:sequence-load SDR managers at fintech startups → Cold Outreach v2 /apollo:sequence-load list sequences (shows all available sequences) /apollo:sequence-load directors of engineering, 500+ employees, US → Demo Follow-up /apollo:sequence-load reload 15 more leads into "Enterprise Pipeline" Step 1 — Parse Input From "$ARGUMENTS", extract: Targeting criteria: Job titles → person_titles Seniority levels → person_seniorities Industry keywords → q_organization_keyword_tags Company size → organization_num_employees_ranges Locations → person_locations or organization_locations Sequence info: Sequence name (text after "to", "into", or "→") Volume — how many contacts to add (default: 10 if not specified) If the user just says "list sequences", skip to Step 2 and show all available sequences. Step 2 — Find the Sequence Use mcp__claude_ai_Apollo_MCP__apollo_emailer_campaigns_search to find the target sequence: Set q_name to the sequence name from input If no match or multiple matches: Show all available sequences in a table: | Name | ID | Status | Ask the user to pick one Step 3 — Get Email Account Use mcp__claude_ai_Apollo_MCP__apollo_email_accounts_index to list linked email accounts. If one account → use automatically If multiple → show them and ask which to send from Step 4 — Find Matching People Use mcp__claude_ai_Apollo_MCP__apollo_mixed_people_api_search with the targeting criteria. Set per_page to the requested volume (or 10 by default) Present the candidates in a preview table: # Name Title Company Location Ask: "Add these [N] contacts to [Sequence Name]? This will consume [N] Apollo credits for enrichment." Wait for confirmation before proceeding. Step 5 — Enrich and Create Contacts For each approved lead: Enrich — Use mcp__claude_ai_Apollo_MCP__apollo_people_bulk_match (batch up to 10 per call) with: first_name , last_name , domain for each person reveal_personal_emails set to true Create contacts — For each enriched person, use mcp__claude_ai_Apollo_MCP__apollo_contacts_create with: first_name , last_name , email , title , organization_name direct_phone or mobile_phone if available run_dedupe set to true Collect all created contact IDs. Step 6 — Add to Sequence Use mcp__claude_ai_Apollo_MCP__apollo_emailer_campaigns_add_contact_ids with: id : the sequence ID emailer_campaign_id : same sequence ID contact_ids : array of created contact IDs send_email_from_email_account_id : the chosen email account ID sequence_active_in_other_campaigns : false (safe default) Step 7 — Confirm Enrollment Show a summary: Sequence loaded successfully Field Value Sequence [Name] Contacts added [count] Sending from [email address] Credits used [count] Contacts enrolled: Name Title Company Email Step 8 — Offer Next Actions Ask the user: Load more — Find and add another batch of leads Review sequence — Show sequence details and all enrolled contacts Remove a contact — Use mcp__claude_ai_Apollo_MCP__apollo_emailer_campaigns_remove_or_stop_contact_ids to remove specific contacts Pause a contact — Re-add with status: "paused" and an auto_unpause_at date
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下载的 .skill 包内含以下字段。
字段 说明
format格式标识(skill/v1)
skill_id技能唯一 ID
name技能名称
version版本号
description技能描述
category所属分类(数组)
trigger_words触发词列表
tags标签列表
source来源标识
source_url来源链接(本页地址)
exported_at导出时间(每次下载生成)
system_prompt系统提示词正文
model_config模型参数:provider / model / temperature / max_tokens / top_p
examples示例
install_guide各平台导入说明(Coze / Dify / Claude / 自定义框架)
同一份技能可按不同平台格式导出。
.skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用 下载
.skillpro 增强格式,额外含脚本 / 工具 / 依赖 / 钩子占位 下载
.json 纯 JSON 导出,只含 system_prompt 与模型参数 下载
Coze 带 frontmatter 的 Markdown,Coze 平台导入用 下载
Dify Dify DSL,创建应用后直接导入 下载

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