Lead Qualification Skill

Lead Qualification Skill【免费下载链接】goose-skillsLibrary of Growth GTM skills data APIs for Claude Code, Codex, Cursor to run ads, social, content, lead gen, seo and data scraping项目地址: https://gitcode.com/gh_mirrors/go/goose-skillsThis skill analyzes lead data and qualifies prospects based on predefined criteria.Usage/gooseworks Use the lead-qualification skill to analyze leads from the CSV file.Parameterscriteria: JSON object defining qualification criterialead_source: Path to CSV file or API endpoint with lead datascoring_model: Optional scoring model configurationOutputA qualified leads report with scores and recommendations.### 第三步编写技能实现脚本 创建scripts/qualify_leads.py文件实现核心功能 python import csv import json import argparse def load_lead_data(file_path): Load lead data from CSV file leads [] with open(file_path, r) as csvfile: reader csv.DictReader(csvfile) for row in reader: leads.append(row) return leads def apply_scoring_model(lead, criteria): Apply scoring model to a single lead score 0 for key, weight in criteria.items(): if key in lead and lead[key].lower() in [yes, true, 1]: score weight return score def qualify_leads(lead_data, criteria): Qualify leads based on criteria qualified [] for lead in lead_data: score apply_scoring_model(lead, criteria) qualified.append({ lead: lead, score: score, qualified: score criteria.get(threshold, 70) }) return qualified def main(): parser argparse.ArgumentParser(descriptionLead Qualification Tool) parser.add_argument(--leads, requiredTrue, helpPath to CSV file with lead data) parser.add_argument(--criteria, requiredTrue, helpJSON file with qualification criteria) args parser.parse_args() # Load data leads load_lead_data(args.leads) with open(args.criteria, r) as f: criteria json.load(f) # Process leads results qualify_leads(leads, criteria) # Output results print(json.dumps(results, indent2)) if __name__ __main__: main()第四步测试与验证技能创建测试用例和验证脚本mkdir tests touch tests/test_leads.csv touch tests/test_criteria.json在tests/test_criteria.json中定义测试标准{ company_size: 20, industry_match: 30, budget_range: 25, decision_authority: 25, threshold: 70 }运行验证脚本检查技能是否符合规范node ../../../../../scripts/validate-skills.js 技能注册与发布本地测试与调试在发布技能之前先在本地进行测试npx goose-skills install ./skills/lead-generation/capabilities/lead-qualification npx goose-skills run lead-qualification --leads tests/test_leads.csv --criteria tests/test_criteria.json更新技能索引将新技能添加到技能索引node scripts/build-index.js这会更新项目根目录下的skills-index.json文件包含你的新技能信息。提交与发布流程按照项目贡献指南提交你的技能创建技能分支提交代码并推送到仓库创建Pull Request通过代码审查合并到主分支 高级技巧技能组合与自动化工作流组合现有技能创建复合技能Goose Skills支持通过组合现有技能创建更复杂的复合技能。例如创建一个lead-generation-pipeline复合技能组合以下能力apollo-lead-finder寻找潜在客户lead-enrichment丰富客户数据lead-qualification筛选高质量客户cold-email-outreach发送个性化邮件创建skills/lead-generation/composites/lead-generation-pipeline/skill.meta.json{ slug: lead-generation-pipeline, category: composites, description: End-to-end lead generation pipeline from prospect finding to outreach., tags: [lead-generation, sales, outreach], installation: { base_command: npx goose-skills install lead-generation-pipeline, supports: [claude, cursor, codex] }, requires_skills: [ apollo-lead-finder, lead-enrichment, lead-qualification, cold-email-outreach ] }【免费下载链接】goose-skillsLibrary of Growth GTM skills data APIs for Claude Code, Codex, Cursor to run ads, social, content, lead gen, seo and data scraping项目地址: https://gitcode.com/gh_mirrors/go/goose-skills创作声明：本文部分内容由AI辅助生成（AIGC），仅供参考