[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6PDQYGPD0KZYV7PCBERTVRV":3,"skill-same:01M6PDQYGPD0KZYV7PCBERTVRV":67},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":41,"linkOnly":42,"markdownContent":43,"markdownBody":44,"frontMatter":45,"refName":46,"commitSha":47,"skillDirectory":48,"forksCount":49,"repositoryHtmlUrl":50,"repositoryHomepageUrl":6,"ownerHtmlUrl":51,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":52,"install":53,"resources":66},"01M6PDQYGPD0KZYV7PCBERTVRV","lead-intelligence",null,"End-to-end lead research and qualification — signal scoring, pain mapping, warm path discovery, and personalized outreach drafting. Use when prospecting, qualifying leads, or preparing outreach campaigns.","pedrol-cmd\u002Fbrain-drin","pedrol-cmd","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F269090167?v=4","01M6PDG3CXWN1PR3GXREEXK69F","01M6PDG3HHBGEWDJ1STF9Z9ER6",12,"MIT","https:\u002F\u002Fgithub.com\u002Fpedrol-cmd\u002Fbrain-drin\u002Fblob\u002Fmaster\u002F.claude\u002Fskills\u002Fdrin-lead-intelligence\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40],"lead intelligence","intelligence","outreach","lead research","signal","qualification","path","leads","mapping","end","research","campaigns","personalized outreach","signal scoring","scoring","use","discovery","prospecting","2026-11-10 10:12:30",false,"---\nname: lead-intelligence\ndescription: End-to-end lead research and qualification — signal scoring, pain mapping, warm path discovery, and personalized outreach drafting. Use when prospecting, qualifying leads, or preparing outreach campaigns.\n---\n\n# Lead Intelligence\n\nFull-cycle lead research: from raw prospect to qualified, personalized outreach-ready intelligence.\n\n## When to Use\n\n- Researching a specific prospect or company\n- Building a prospecting list for a segment\n- Qualifying inbound leads\n- Preparing personalized outreach campaigns\n\n## Process\n\n### Step 1: Prospect Research\n\nFor each prospect, gather:\n- **Role & seniority** — Title, responsibilities, decision-making power\n- **Company** — Size, industry, growth stage, tech stack, recent news\n- **Activity** — LinkedIn posts, articles, podcast appearances, conference talks\n- **Network** — Mutual connections, shared groups, common interests\n- **Pain signals** — Hiring for roles that suggest our problem area, complaints in posts, industry-specific challenges\n\nUse WebSearch extensively. Check LinkedIn, company website, press releases, Glassdoor.\n\n### Step 2: Signal Scoring\n\nScore each prospect (0-100):\n\n| Signal | Weight | Scoring |\n|--------|--------|---------|\n| Role fit | 30% | Decision maker: 100. Influencer: 70. User: 40. Unrelated: 0. |\n| Industry match | 25% | ICP industry: 100. Adjacent: 60. Unrelated: 0. |\n| Activity signals | 20% | Active on LinkedIn\u002Fcontent: 100. Moderate: 50. Ghost: 10. |\n| Company size fit | 15% | Sweet spot: 100. Too big\u002Fsmall but viable: 50. Wrong: 0. |\n| Timing signals | 10% | Hiring, funding, expansion: 100. Stable: 50. Downsizing: 10. |\n\n**Threshold**: Score ≥65 = qualified. 40-64 = nurture. \u003C40 = skip.\n\n### Step 3: Warm Path Discovery\n\nFor qualified prospects, identify:\n- Mutual connections who could introduce\n- Content they've engaged with (comment on it first)\n- Events they're attending\n- Communities they're active in\n- Any existing relationship touchpoints\n\n### Step 4: Pain Mapping\n\nMap prospect's likely pains to our value:\n```\nPain: [specific problem they likely have]\nEvidence: [what suggests this — hiring posts, complaints, industry trend]\nOur angle: [how we address this specifically]\nProof: [case study, metric, or example from similar company]\n```\n\n### Step 5: Outreach Draft\n\nGenerate personalized outreach:\n- **LinkedIn connection request** (1 sentence, references something specific about them)\n- **First email** (value-first, references their specific pain, under 80 words)\n- **Comment strategy** (2-3 thoughtful comments on their recent posts before reaching out)\n\n## Output Template\n\n```markdown\n# Lead Intelligence: [Prospect Name]\n\n## Profile\n- Role: [title at company]\n- Company: [name, size, industry]\n- Signal Score: [X\u002F100] — [QUALIFIED \u002F NURTURE \u002F SKIP]\n\n## Score Breakdown\n| Signal | Score | Evidence |\n|--------|-------|----------|\n| Role fit | X\u002F30 | [why] |\n| Industry | X\u002F25 | [why] |\n| Activity | X\u002F20 | [why] |\n| Company size | X\u002F15 | [why] |\n| Timing | X\u002F10 | [why] |\n\n## Pain Map\n1. [Pain → Our angle → Proof]\n2. [Pain → Our angle → Proof]\n\n## Warm Paths\n- [Path 1: mutual connection, shared group, etc.]\n- [Path 2]\n\n## Recommended Outreach\n### LinkedIn Request\n[text]\n\n### First Email\nSubject: [subject]\n[body]\n\n### Pre-Outreach Engagement\n- Comment on: [specific post] — suggested comment: [text]\n```\n\n## Notes\n\n- This skill is most powerful when combined with the sales-automator agent for full sequence generation\n- Always verify information is current — LinkedIn profiles change, companies pivot\n- Quality over quantity: 10 deeply researched prospects > 100 spray-and-pray\n","# Lead Intelligence\n\nFull-cycle lead research: from raw prospect to qualified, personalized outreach-ready intelligence.\n\n## When to Use\n\n- Researching a specific prospect or company\n- Building a prospecting list for a segment\n- Qualifying inbound leads\n- Preparing personalized outreach campaigns\n\n## Process\n\n### Step 1: Prospect Research\n\nFor each prospect, gather:\n- **Role & seniority** — Title, responsibilities, decision-making power\n- **Company** — Size, industry, growth stage, tech stack, recent news\n- **Activity** — LinkedIn posts, articles, podcast appearances, conference talks\n- **Network** — Mutual connections, shared groups, common interests\n- **Pain signals** — Hiring for roles that suggest our problem area, complaints in posts, industry-specific challenges\n\nUse WebSearch extensively. Check LinkedIn, company website, press releases, Glassdoor.\n\n### Step 2: Signal Scoring\n\nScore each prospect (0-100):\n\n| Signal | Weight | Scoring |\n|--------|--------|---------|\n| Role fit | 30% | Decision maker: 100. Influencer: 70. User: 40. Unrelated: 0. |\n| Industry match | 25% | ICP industry: 100. Adjacent: 60. Unrelated: 0. |\n| Activity signals | 20% | Active on LinkedIn\u002Fcontent: 100. Moderate: 50. Ghost: 10. |\n| Company size fit | 15% | Sweet spot: 100. Too big\u002Fsmall but viable: 50. Wrong: 0. |\n| Timing signals | 10% | Hiring, funding, expansion: 100. Stable: 50. Downsizing: 10. |\n\n**Threshold**: Score ≥65 = qualified. 40-64 = nurture. \u003C40 = skip.\n\n### Step 3: Warm Path Discovery\n\nFor qualified prospects, identify:\n- Mutual connections who could introduce\n- Content they've engaged with (comment on it first)\n- Events they're attending\n- Communities they're active in\n- Any existing relationship touchpoints\n\n### Step 4: Pain Mapping\n\nMap prospect's likely pains to our value:\n```\nPain: [specific problem they likely have]\nEvidence: [what suggests this — hiring posts, complaints, industry trend]\nOur angle: [how we address this specifically]\nProof: [case study, metric, or example from similar company]\n```\n\n### Step 5: Outreach Draft\n\nGenerate personalized outreach:\n- **LinkedIn connection request** (1 sentence, references something specific about them)\n- **First email** (value-first, references their specific pain, under 80 words)\n- **Comment strategy** (2-3 thoughtful comments on their recent posts before reaching out)\n\n## Output Template\n\n```markdown\n# Lead Intelligence: [Prospect Name]\n\n## Profile\n- Role: [title at company]\n- Company: [name, size, industry]\n- Signal Score: [X\u002F100] — [QUALIFIED \u002F NURTURE \u002F SKIP]\n\n## Score Breakdown\n| Signal | Score | Evidence |\n|--------|-------|----------|\n| Role fit | X\u002F30 | [why] |\n| Industry | X\u002F25 | [why] |\n| Activity | X\u002F20 | [why] |\n| Company size | X\u002F15 | [why] |\n| Timing | X\u002F10 | [why] |\n\n## Pain Map\n1. [Pain → Our angle → Proof]\n2. [Pain → Our angle → Proof]\n\n## Warm Paths\n- [Path 1: mutual connection, shared group, etc.]\n- [Path 2]\n\n## Recommended Outreach\n### LinkedIn Request\n[text]\n\n### First Email\nSubject: [subject]\n[body]\n\n### Pre-Outreach Engagement\n- Comment on: [specific post] — suggested comment: [text]\n```\n\n## Notes\n\n- This skill is most powerful when combined with the sales-automator agent for full sequence generation\n- Always verify information is current — LinkedIn profiles change, companies pivot\n- Quality over quantity: 10 deeply researched prospects > 100 spray-and-pray\n",{"name":5,"description":7},"master","49f9153042a67a088b119042fa3aae8ede872e4d",".claude\u002Fskills\u002Fdrin-lead-intelligence",1,"https:\u002F\u002Fgithub.com\u002Fpedrol-cmd\u002Fbrain-drin","https:\u002F\u002Fgithub.com\u002Fpedrol-cmd","2026-04-11 06:42:15",{"source":8,"skillName":5,"commands":54,"prompt":64,"zipAvailable":65},[55,58,61],{"tool":56,"command":57},"npx","npx skills add 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intimacy.","https:\u002F\u002Fgithub.com\u002Fpedrol-cmd\u002Fbrain-drin\u002Fblob\u002Fmaster\u002F.claude\u002Fskills\u002Fdrin-whatsapp-outbound-engine\u002FSKILL.md",[],[75,76,77,78,79,80,81,82,83,84,85,86,87],"engine","whatsapp","drin whatsapp outbound engine","outbound","speed","intimacy","for","Production","via","workflow","engagement","sales","specialized",{"skillNo":89,"name":90,"nameZh":6,"description":91,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":92,"categories":93,"keywords":97,"publishedAt":41},"01M6PE0NJQNWJATM9B1AJRAV1P","drin-vector-database-sync","Specialized workflow for managing, syncing, and optimizing data in Vector DBs for RAG 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products.","https:\u002F\u002Fgithub.com\u002Fpedrol-cmd\u002Fbrain-drin\u002Fblob\u002Fmaster\u002F.claude\u002Fskills\u002Fdrin-ux-audit-framework\u002FSKILL.md",[136],{"categoryNo":114,"categoryName":115,"taxonomyType":116,"taxonomyTypeDesc":117},[138,119,139,140,141,142,143,144,82,145,81],"ux audit","framework","audit","drin ux audit framework","evaluation framework","issues","Evaluation","usability",{"skillNo":147,"name":148,"nameZh":6,"description":149,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":150,"categories":151,"keywords":155,"publishedAt":41},"01M6PE07XV8WYQCKJM8S9KZ7SE","drin-user-story-generator","Automated generator for well-structured user stories following the INVEST principles.","https:\u002F\u002Fgithub.com\u002Fpedrol-cmd\u002Fbrain-drin\u002Fblob\u002Fmaster\u002F.claude\u002Fskills\u002Fdrin-user-story-generator\u002FSKILL.md",[152],{"categoryNo":153,"categoryName":154,"taxonomyType":116,"taxonomyTypeDesc":117},"01JBSEEDCAT000000000000031","产品经理",[156,157,158,159,160,161,162,82,163,164,165,81],"generator","drin user story generator","Story","user story generator","user","user story","stories","structured","principles","user stories",{"skillNo":167,"name":168,"nameZh":6,"description":169,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":170,"categories":171,"keywords":172,"publishedAt":41},"01M6PE01VXR0BN5SBYQRP6AJ5W","drin-user-feedback-synthesizer","Workflow for aggregating and summarizing user feedback from support tickets, social media, and interviews.","https:\u002F\u002Fgithub.com\u002Fpedrol-cmd\u002Fbrain-drin\u002Fblob\u002Fmaster\u002F.claude\u002Fskills\u002Fdrin-user-feedback-synthesizer\u002FSKILL.md",[],[127,160,173,174,175,176,82,177,178,179,180,181,81,84],"feedback synthesizer","synthesizer","drin user feedback synthesizer","support","Media","social media","social","tickets","interviews"]