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Agent Skill · 研究检索

callable-lead-count(可调用的潜在客户数量)

callable-lead-count 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

callable-lead-count 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为调用外部服务;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 69,087。

总安装 220周安装 9GitHub Stars 12下载 71核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:callable-lead-count(可调用的潜在客户数量)
来源仓库:https://github.com/scientiacapital/skills
仓库路径:skills/callable-lead-count
安装命令:
npx skills add https://github.com/scientiacapital/skills --skill callable-lead-count
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/scientiacapital/skills --skill callable-lead-count

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

callable-lead-count 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。适用于每日早晨潜在客户清单检查,基于 HubSpot 数据筛选有电话的联系人。

按 ATL/BTL 层级分类并计算 runway 天数,输出健康警报与分档统计。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/callable-lead-count

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/scientiacapital/skills --skill callable-lead-c…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要调用外部服务,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

callable-lead-count 怎么安装?

在终端执行 npx skills add https://github.com/scientiacapital/skills --skill callable-lead-count。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

callable-lead-count 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

callable-lead-count 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

callable-lead-count 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

callable-lead-count 用的人多吗?

公开数据是累计安装 220 次,近一周安装 9 次,下载 71 次,GitHub 12 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

callable-lead-count 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
callable-lead-count

可调用的潜在客户数量 · 本页

22012调用外部服务未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Callable Lead Count Skill

<quick_start> Trigger: M-F 7:25 AM ET (daily, before morning-brief at 7:30 AM) Manual Trigger: "Show callable leads" or "Lead inventory check" Dependencies: Requires HubSpot portal 21530819 access Output: Callable lead count by tier, ATL Runway days, health alerts </quick_start>

<success_criteria>

  • Query HubSpot for contacts with phone!= null
  • Apply Golden Rules (exclude customers, channel, device owners, product-page engagers)
  • Classify each by ATL/BTL tier (per CLAUDE.md Decision-Maker rules)
  • Count totals: ATL, GRAY, BTL, NEVER (for awareness)
  • Calculate ATL Runway = ATL count ÷ 15 dials/day = X days of inventory
  • Calculate total Runway = total callable count ÷ 50 dials/day = X days
  • Alert if ATL < 15 (⚠️ warning)
  • Alert if total < 50 (🚨 critical)
  • Alert if NEVER ATL > 0 (🔍 review, remove from sequences)
  • Output: Summary stats + detailed table + trend vs previous day </success_criteria>

Stage 1: Query HubSpot for Callable Contacts

MCP Tool: search_crm_objects (HubSpot)

objectType: "contacts"
filterGroups: [{
  filters: [
    { propertyName: "phone", operator: "HAS_PROPERTY" }
  ]
}]
properties: [
  "firstname",
  "lastname",
  "email",
  "phone",
  "jobtitle",
  "company",
  "hs_lead_status",
  "hubspot_owner_id",
  "hs_analytics_num_page_views",
  "custom_atl_btl_tier",
  "custom_prospect_vertical",
  "lifecyclestage",
  "createdate"
]
limit: 100

Pagination: Loop through all results (may be 200+ contacts)

Output: Full contact list with all properties (no filtering yet)


Stage 2: Apply Golden Rules Filter

Golden Rules (Hard Exclusions):

Skip contacts if:

  • Email domain matches crm_customers table (customer account)
  • Contact tagged "Channel Partner" (hubspot_owner_id = channel owner)
  • Contact tagged "Device Owner" or in device-owner list
  • Contact tagged "Product Page Engager" (marketing-only engagement)
  • Job title contains BTL keywords: Technician, Support, Intern, Volunteer, Operator, Designer (non-director)
  • Job title in NEVER ATL list (Warehouse Manager, Network Manager, Systems Admin, AV Tech, Graphic Design Instructor, Program Administrator, Web Designer, Classroom Support, Lab Coordinator, Maintenance, Building Engineer, Multimedia Services Manager, Video Production Specialist, Streaming Crew)
  • Lead status in exclusion list: Unqualified, Opted Out, Bad Fit
  • Phone number invalid or duplicate

Retention Logic:

  • Keep contacts with hs_lead_status in: Subscriber, Qualified Lead, Inbound Lead, Marketing Qualified Lead, Sales Qualified Lead
  • Keep prospects created in last 90 days
  • Keep contacts with hs_analytics_num_page_views > 0 (engagement signal)

Output: Filtered callable list (typically 50-70% of input after Golden Rules)


Stage 3: Classify by ATL/BTL Tier

MCP Tool: N/A (logic-based classification using jobtitle)

Apply ATL/BTL Decision-Maker Classification (per CLAUDE.md):

ATL Tier (Always Prospect) — 10 Universal Keywords:

  • Chief, CIO, CTO, CFO, COO
  • Vice President, VP, AVP, SVP, EVP
  • President, Provost, Vice Provost
  • Superintendent, Director (IT/Tech/Facilities/Academic Tech/Procurement)
  • Dean, Court Administrator, Clerk of Court
  • City Manager, County Manager, Senior Pastor, Executive Pastor

GRAY Tier (Contextual - Budget Authority >$25K):

  • Manager (AV/Facilities/IT) — only if reports to Director+
  • Department Chair — context-dependent (small vs large institution)
  • Director of Educational Technology — depends on reporting line
  • Program Director — only if dept-level budget authority

BTL Tier (No Budget Authority):

  • Technician, Specialist, Coordinator, Support
  • Administrator (Systems/Network/Database)
  • Engineer (AV/Network/Systems), Operator
  • Instructor, Professor, Faculty, Designer, Assistant
  • Clerk (non-Court), Volunteer, Help Desk
  • Student, Resident, Intern

NEVER ATL (Automatic Exclusions):

  • Warehouse Manager, Network Manager, Systems Administrator
  • AV Technician, Graphic Design Instructor
  • Program Administrator, Web Designer
  • Classroom Support, Lab Coordinator, Maintenance
  • Building Engineer, Multimedia Services Manager
  • Video Production Specialist, Streaming Crew

Classification Algorithm:

FOR each contact IN filtered_list:
  title = contact.jobtitle.lower()

  IF title matches any NEVER_ATL keyword:
    tier = "NEVER"
    action = "REVIEW" (may need removal from sequences)

  ELIF title matches any ATL keyword:
    tier = "ATL"

  ELIF title matches any GRAY keyword:
    tier = "GRAY"
    note = "Verify budget authority >$25K via company research"

  ELSE:
    tier = "BTL"

Output: Classified callable contacts with tier assignment


Stage 4: Calculate Callable Inventory Metrics

Count by Tier:

atl_count = count(tier == "ATL")
gray_count = count(tier == "GRAY")
btl_count = count(tier == "BTL")
never_count = count(tier == "NEVER")
total_callable = atl_count + gray_count + btl_count

ATL Runway Metric:

atl_runway_days = ROUND(atl_count / 15, 1)
# Tim's target: 15 ATL dials/day
# Example: 45 ATL contacts = 3 days of ATL inventory

Total Runway Metric:

total_runway_days = ROUND(total_callable / 50, 1)
# Tim's target: 50+ dials/day (mix of ATL, GRAY, BTL)
# Example: 200 callable = 4 days of total inventory

Lead Age Metrics:

avg_days_in_system = AVERAGE(TODAY() - contact.createdate)
recent_engagers = count(hs_analytics_num_page_views > 0)

Stage 5: Generate Health Alerts

Alert Thresholds:

MetricThresholdAlert LevelAction
ATL count< 15⚠️ Warning"Increase ATL prospecting (prospect-enrich + prospect-refresh)"
Total callable< 50🚨 Critical"Insufficient inventory for 50 dials/day—escalate prospecting"
NEVER ATL count> 0🔍 Review"X contacts in NEVER ATL tier—remove from sequences"
ATL Runway< 2 days⚠️ Warning"Less than 2 days of ATL dials—restock urgently"

Alert Output:

✓ ATL inventory: 42 contacts (2.8 days runway)
✓ Total callable: 185 contacts (3.7 days runway)
🔍 NEVER ATL: 2 contacts (remove from sequences)
✓ Health: GREEN (acceptable inventory)

Stage 6: Output Summary Report

Format: Markdown summary + detailed table

Summary Section:

# Daily Callable Lead Inventory — 2026-03-19

## Health Status
✓ ATL Inventory: 42 contacts (2.8 days)
✓ Total Callable: 185 contacts (3.7 days)
🔍 NEVER ATL: 2 contacts (review)

## Detailed Breakdown
| Tier | Count | % of Total | Runway Days | Status |
|------|-------|-----------|-------------|--------|
| ATL | 42 | 22.7% | 2.8 | ✓ Good |
| GRAY | 65 | 35.1% | 1.3 | ⚠️ Monitor |
| BTL | 78 | 42.2% | 1.6 | ✓ OK |
| **TOTAL** | **185** | **100%** | **3.7** | **✓ GREEN** |

## Trend vs Yesterday
- ATL: +3 (was 39)
- GRAY: -2 (was 67)
- BTL: +5 (was 73)
- Total: +6 (was 179)

## Top Companies by Leads
1. Acme Corp: 12 contacts (5 ATL)
2. State University: 8 contacts (6 ATL)
3. County Courts: 6 contacts (5 ATL)

## Activity Signal
- Recent engagers (>5 page views): 45 contacts
- Avg days in system: 23 days
- New adds (last 7 days): 18 contacts

Detailed Callable Table:

ContactTitleCompanyVerticalPhoneTierDays in SystemEngagementSequence
Jane SmithDirector of IT ServicesAcme CorpCorp AV✓ATL3412 viewsBDR_CorporateAV_1
Bob JonesManager, IT InfrastructureState UnivHigher Ed✓GRAY453 viewsBDR_HigherEd_1
Carol WhiteAV TechnicianExample IncHealthcare✓NEVER121 viewREMOVE

Stage 7: Integration with Morning Brief

This skill feeds into morning-brief (7:30 AM):

  • Pass ATL/GRAY/BTL breakdown to morning-brief
  • Pass alert flags (⚠️ warning, 🚨 critical, 🔍 review)
  • Pass top 15 ATL contacts for priority dial list
  • Pass runway metrics for inventory planning

Output format for morning-brief:

{
  "atl_count": 42,
  "gray_count": 65,
  "btl_count": 78,
  "never_count": 2,
  "total_callable": 185,
  "atl_runway_days": 2.8,
  "total_runway_days": 3.7,
  "alerts": ["NEVER_ATL_REVIEW"],
  "top_atl_contacts": [
    { "name": "Jane Smith", "company": "Acme", "days_in_system": 34 },
    ...
  ]
}

Stage 8: Comparison Metrics (Week-over-Week)

Optional: Track 7-day trend

DateATLGRAYBTLTotalATL RunwayStatus
2026-03-194265781852.8✓
2026-03-183967731792.6✓
2026-03-173564701692.3⚠️
2026-03-163262681622.1⚠️

Trend Analysis:

  • ATL growing 3-4/day (good)
  • Total growing 6/day (good, at target from prospect-enrich + prospect-refresh)
  • Runway stable at 2.8-3.7 days (acceptable; 2+ days is minimum)

Emit Outcome Sidecar

As the final step, write to ~/.claude/skill-analytics/last-outcome-callable-lead-count.json:

{"ts":"[UTC ISO8601]","skill":"callable-lead-count","version":"1.0.0","variant":"default",
 "status":"[success|partial|error]","runtime_ms":[estimated ms from start],
 "metrics":{"atl_count":[n],"gray_count":[n],"btl_count":[n],"total_callable":[n],"never_atl_flagged":[n],"atl_runway_days":[n]},
 "error":null,"session_id":"[YYYY-MM-DD]"}

Use status "partial" if some stages failed but results were produced. Use "error" only if no output was generated.


Skill Metadata

Version: 1.0 Last Updated: 2026-03-19 Author: Tim Kipper Status: Production Integration: HubSpot (portal 21530819) Tier: P1 (Core BDR Automation) Triggers: Scheduled (M-F 7:25 AM) + Manual ("Show callable leads") Dependencies: Feeds into morning-brief (7:30 AM)

平台分布

Codex

37.18%
按下载量换算26

Claude

29.66%
按下载量换算21

Cursor

18.05%
按下载量换算13

Gemini CLI

9.1%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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