[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6JD830FB63CWHCJQKG0WXRB":3,"skill-same:01M6JD830FB63CWHCJQKG0WXRB":92},{"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":25,"publishedAt":46,"linkOnly":47,"markdownContent":48,"markdownBody":49,"frontMatter":50,"refName":52,"commitSha":53,"skillDirectory":54,"forksCount":55,"repositoryHtmlUrl":56,"repositoryHomepageUrl":57,"ownerHtmlUrl":58,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":59,"install":60,"resources":73},"01M6JD830FB63CWHCJQKG0WXRB","lead-intelligence",null,"AI-native lead intelligence and outreach pipeline. Replaces Apollo, Clay, and ZoomInfo with agent-powered signal scoring, mutual ranking, warm path discovery, source-derived voice modeling, and channel-specific outreach across email, LinkedIn, and X. Use when the user wants to find, qualify, and reach high-value contacts.","S3YED\u002Fappie-kit","S3YED","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F62743867?v=4","01M6JBKBRRVP6KMS0VWZ27T3R6","01M6JBKBX0KG30284PW1DBYJT1",14,"MIT","https:\u002F\u002Fgithub.com\u002FS3YED\u002Fappie-kit\u002Fblob\u002Fmaster\u002Fskills\u002Fecc\u002Flead-intelligence\u002FSKILL.md",[17,22],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000005","邮件与沟通","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45],"lead intelligence","intelligence","across","source","path","zoominfo","contacts","native","qualify","ranking","modeling","email","signal scoring","clay","scoring","ai-native","use","linkedin","discovery","pipeline","2026-11-10 10:12:30",false,"---\nname: lead-intelligence\ndescription: AI-native lead intelligence and outreach pipeline. Replaces Apollo, Clay, and ZoomInfo with agent-powered signal scoring, mutual ranking, warm path discovery, source-derived voice modeling, and channel-specific outreach across email, LinkedIn, and X. Use when the user wants to find, qualify, and reach high-value contacts.\norigin: ECC\n---\n\n# Lead Intelligence\n\nAgent-powered lead intelligence pipeline that finds, scores, and reaches high-value contacts through social graph analysis and warm path discovery.\n\n## When to Activate\n\n- User wants to find leads or prospects in a specific industry\n- Building an outreach list for partnerships, sales, or fundraising\n- Researching who to reach out to and the best path to reach them\n- User says \"find leads\", \"outreach list\", \"who should I reach out to\", \"warm intros\"\n- Needs to score or rank a list of contacts by relevance\n- Wants to map mutual connections to find warm introduction paths\n\n## Tool Requirements\n\n### Required\n- **Exa MCP** — Deep web search for people, companies, and signals (`web_search_exa`)\n- **X API** — Follower\u002Ffollowing graph, mutual analysis, recent activity (`X_BEARER_TOKEN`, plus write-context credentials such as `X_CONSUMER_KEY`, `X_CONSUMER_SECRET`, `X_ACCESS_TOKEN`, `X_ACCESS_TOKEN_SECRET`)\n\n### Optional (enhance results)\n- **LinkedIn** — Direct API if available, otherwise browser control for search, profile inspection, and drafting\n- **Apollo\u002FClay API** — For enrichment cross-reference if user has access\n- **GitHub MCP** — For developer-centric lead qualification\n- **Apple Mail \u002F Mail.app** — Draft cold or warm email without sending automatically\n- **Browser control** — For LinkedIn and X when API coverage is missing or constrained\n\n## Pipeline Overview\n\n```\n┌─────────────┐     ┌──────────────┐     ┌─────────────────┐     ┌──────────────┐     ┌─────────────────┐\n│ 1. Signal   │────>│ 2. Mutual    │────>│ 3. Warm Path    │────>│ 4. Enrich    │────>│ 5. Outreach     │\n│    Scoring  │     │    Ranking   │     │    Discovery    │     │              │     │    Draft        │\n└─────────────┘     └──────────────┘     └─────────────────┘     └──────────────┘     └─────────────────┘\n```\n\n## Voice Before Outreach\n\nDo not draft outbound from generic sales copy.\n\nRun `brand-voice` first whenever the user's voice matters. Reuse its `VOICE PROFILE` instead of re-deriving style ad hoc inside this skill.\n\nIf live X access is available, pull recent original posts before drafting. If not, use supplied examples or the best repo\u002Fsite material available.\n\n## Stage 1: Signal Scoring\n\nSearch for high-signal people in target verticals. Assign a weight to each based on:\n\n| Signal | Weight | Source |\n|--------|--------|--------|\n| Role\u002Ftitle alignment | 30% | Exa, LinkedIn |\n| Industry match | 25% | Exa company search |\n| Recent activity on topic | 20% | X API search, Exa |\n| Follower count \u002F influence | 10% | X API |\n| Location proximity | 10% | Exa, LinkedIn |\n| Engagement with your content | 5% | X API interactions |\n\n### Signal Search Approach\n\n```python\n# Step 1: Define target parameters\ntarget_verticals = [\"prediction markets\", \"AI tooling\", \"developer tools\"]\ntarget_roles = [\"founder\", \"CEO\", \"CTO\", \"VP Engineering\", \"investor\", \"partner\"]\ntarget_locations = [\"San Francisco\", \"New York\", \"London\", \"remote\"]\n\n# Step 2: Exa deep search for people\nfor vertical in target_verticals:\n    results = web_search_exa(\n        query=f\"{vertical} {role} founder CEO\",\n        category=\"company\",\n        numResults=20\n    )\n    # Score each result\n\n# Step 3: X API search for active voices\nx_search = search_recent_tweets(\n    query=\"prediction markets OR AI tooling OR developer tools\",\n    max_results=100\n)\n# Extract and score unique authors\n```\n\n## Stage 2: Mutual Ranking\n\nFor each scored target, analyze the user's social graph to find the warmest path.\n\n### Ranking Model\n\n1. Pull user's X following list and LinkedIn connections\n2. For each high-signal target, check for shared connections\n3. Apply the `social-graph-ranker` model to score bridge value\n4. Rank mutuals by:\n\n| Factor | Weight |\n|--------|--------|\n| Number of connections to targets | 40% — highest weight, most connections = highest rank |\n| Mutual's current role\u002Fcompany | 20% — decision maker vs individual contributor |\n| Mutual's location | 15% — same city = easier intro |\n| Industry alignment | 15% — same vertical = natural intro |\n| Mutual's X handle \u002F LinkedIn | 10% — identifiability for outreach |\n\nCanonical rule:\n\n```text\nUse social-graph-ranker when the user wants the graph math itself,\nthe bridge ranking as a standalone report, or explicit decay-model tuning.\n```\n\nInside this skill, use the same weighted bridge model:\n\n```text\nB(m) = Σ_{t ∈ T} w(t) · λ^(d(m,t) - 1)\nR(m) = B_ext(m) · (1 + β · engagement(m))\n```\n\nInterpretation:\n- Tier 1: high `R(m)` and direct bridge paths -> warm intro asks\n- Tier 2: medium `R(m)` and one-hop bridge paths -> conditional intro asks\n- Tier 3: no viable bridge -> direct cold outreach using the same lead record\n\n### Output Format\n\n```\n\nIf the user explicitly wants the ranking engine broken out, the math visualized, or the network scored outside the full lead workflow, run `social-graph-ranker` as a standalone pass first and feed the result back into this pipeline.\nMUTUAL RANKING REPORT\n=====================\n\n#1  @mutual_handle (Score: 92)\n    Name: Jane Smith\n    Role: Partner @ Acme Ventures\n    Location: San Francisco\n    Connections to targets: 7\n    Connected to: @target1, @target2, @target3, @target4, @target5, @target6, @target7\n    Best intro path: Jane invested in Target1's company\n\n#2  @mutual_handle2 (Score: 85)\n    ...\n```\n\n## Stage 3: Warm Path Discovery\n\nFor each target, find the shortest introduction chain:\n\n```\nYou ──[follows]──> Mutual A ──[invested in]──> Target Company\nYou ──[follows]──> Mutual B ──[co-founded with]──> Target Person\nYou ──[met at]──> Event ──[also attended]──> Target Person\n```\n\n### Path Types (ordered by warmth)\n1. **Direct mutual** — You both follow\u002Fknow the same person\n2. **Portfolio connection** — Mutual invested in or advises target's company\n3. **Co-worker\u002Falumni** — Mutual worked at same company or attended same school\n4. **Event overlap** — Both attended same conference\u002Fprogram\n5. **Content engagement** — Target engaged with mutual's content or vice versa\n\n## Stage 4: Enrichment\n\nFor each qualified lead, pull:\n\n- Full name, current title, company\n- Company size, funding stage, recent news\n- Recent X posts (last 30 days) — topics, tone, interests\n- Mutual interests with user (shared follows, similar content)\n- Recent company events (product launch, funding round, hiring)\n\n### Enrichment Sources\n- Exa: company data, news, blog posts\n- X API: recent tweets, bio, followers\n- GitHub: open source contributions (for developer-centric leads)\n- LinkedIn (via browser-use): full profile, experience, education\n\n## Stage 5: Outreach Draft\n\nGenerate personalized outreach for each lead. The draft should match the source-derived voice profile and the target channel.\n\n### Channel Rules\n\n#### Email\n\n- Use for the highest-value cold outreach, warm intros, investor outreach, and partnership asks\n- Default to drafting in Apple Mail \u002F Mail.app when local desktop control is available\n- Create drafts first, do not send automatically unless the user explicitly asks\n- Subject line should be plain and specific, not clever\n\n#### LinkedIn\n\n- Use when the target is active there, when mutual graph context is stronger on LinkedIn, or when email confidence is low\n- Prefer API access if available\n- Otherwise use browser control to inspect profiles, recent activity, and draft the message\n- Keep it shorter than email and avoid fake professional warmth\n\n#### X\n\n- Use for high-context operator, builder, or investor outreach where public posting behavior matters\n- Prefer API access for search, timeline, and engagement analysis\n- Fall back to browser control when needed\n- DMs and public replies should be much tighter than email and should reference something real from the target's timeline\n\n#### Channel Selection Heuristic\n\nPick one primary channel in this order:\n\n1. warm intro by email\n2. direct email\n3. LinkedIn DM\n4. X DM or reply\n\nUse multi-channel only when there is a strong reason and the cadence will not feel spammy.\n\n### Warm Intro Request (to mutual)\n\nGoal:\n\n- one clear ask\n- one concrete reason this intro makes sense\n- easy-to-forward blurb if needed\n\nAvoid:\n\n- overexplaining your company\n- social-proof stacking\n- sounding like a fundraiser template\n\n### Direct Cold Outreach (to target)\n\nGoal:\n\n- open from something specific and recent\n- explain why the fit is real\n- make one low-friction ask\n\nAvoid:\n\n- generic admiration\n- feature dumping\n- broad asks like \"would love to connect\"\n- forced rhetorical questions\n\n### Execution Pattern\n\nFor each target, produce:\n\n1. the recommended channel\n2. the reason that channel is best\n3. the message draft\n4. optional follow-up draft\n5. if email is the chosen channel and Apple Mail is available, create a draft instead of only returning text\n\nIf browser control is available:\n\n- LinkedIn: inspect target profile, recent activity, and mutual context, then draft or prepare the message\n- X: inspect recent posts or replies, then draft DM or public reply language\n\nIf desktop automation is available:\n\n- Apple Mail: create draft email with subject, body, and recipient\n\nDo not send messages automatically without explicit user approval.\n\n### Anti-Patterns\n\n- generic templates with no personalization\n- long paragraphs explaining your whole company\n- multiple asks in one message\n- fake familiarity without specifics\n- bulk-sent messages with visible merge fields\n- identical copy reused for email, LinkedIn, and X\n- platform-shaped slop instead of the author's actual voice\n\n## Configuration\n\nUsers should set these environment variables:\n\n```bash\n# Required\nexport X_BEARER_TOKEN=\"...\"\nexport X_ACCESS_TOKEN=\"...\"\nexport X_ACCESS_TOKEN_SECRET=\"...\"\nexport X_CONSUMER_KEY=\"...\"\nexport X_CONSUMER_SECRET=\"...\"\nexport EXA_API_KEY=\"...\"\n\n# Optional\nexport LINKEDIN_COOKIE=\"...\" # For browser-use LinkedIn access\nexport APOLLO_API_KEY=\"...\"  # For Apollo enrichment\n```\n\n## Agents\n\nThis skill includes specialized agents in the `agents\u002F` subdirectory:\n\n- **signal-scorer** — Searches and ranks prospects by relevance signals\n- **mutual-mapper** — Maps social graph connections and finds warm paths\n- **enrichment-agent** — Pulls detailed profile and company data\n- **outreach-drafter** — Generates personalized messages\n\n## Example Usage\n\n```\nUser: find me the top 20 people in prediction markets I should reach out to\n\nAgent workflow:\n1. signal-scorer searches Exa and X for prediction market leaders\n2. mutual-mapper checks user's X graph for shared connections\n3. enrichment-agent pulls company data and recent activity\n4. outreach-drafter generates personalized messages for top ranked leads\n\nOutput: Ranked list with warm paths, voice profile summary, and channel-specific outreach drafts or drafts-in-app\n```\n\n## Related Skills\n\n- `brand-voice` for canonical voice capture\n- `connections-optimizer` for review-first network pruning and expansion before outreach\n","# Lead Intelligence\n\nAgent-powered lead intelligence pipeline that finds, scores, and reaches high-value contacts through social graph analysis and warm path discovery.\n\n## When to Activate\n\n- User wants to find leads or prospects in a specific industry\n- Building an outreach list for partnerships, sales, or fundraising\n- Researching who to reach out to and the best path to reach them\n- User says \"find leads\", \"outreach list\", \"who should I reach out to\", \"warm intros\"\n- Needs to score or rank a list of contacts by relevance\n- Wants to map mutual connections to find warm introduction paths\n\n## Tool Requirements\n\n### Required\n- **Exa MCP** — Deep web search for people, companies, and signals (`web_search_exa`)\n- **X API** — Follower\u002Ffollowing graph, mutual analysis, recent activity (`X_BEARER_TOKEN`, plus write-context credentials such as `X_CONSUMER_KEY`, `X_CONSUMER_SECRET`, `X_ACCESS_TOKEN`, `X_ACCESS_TOKEN_SECRET`)\n\n### Optional (enhance results)\n- **LinkedIn** — Direct API if available, otherwise browser control for search, profile inspection, and drafting\n- **Apollo\u002FClay API** — For enrichment cross-reference if user has access\n- **GitHub MCP** — For developer-centric lead qualification\n- **Apple Mail \u002F Mail.app** — Draft cold or warm email without sending automatically\n- **Browser control** — For LinkedIn and X when API coverage is missing or constrained\n\n## Pipeline Overview\n\n```\n┌─────────────┐     ┌──────────────┐     ┌─────────────────┐     ┌──────────────┐     ┌─────────────────┐\n│ 1. Signal   │────>│ 2. Mutual    │────>│ 3. Warm Path    │────>│ 4. Enrich    │────>│ 5. Outreach     │\n│    Scoring  │     │    Ranking   │     │    Discovery    │     │              │     │    Draft        │\n└─────────────┘     └──────────────┘     └─────────────────┘     └──────────────┘     └─────────────────┘\n```\n\n## Voice Before Outreach\n\nDo not draft outbound from generic sales copy.\n\nRun `brand-voice` first whenever the user's voice matters. Reuse its `VOICE PROFILE` instead of re-deriving style ad hoc inside this skill.\n\nIf live X access is available, pull recent original posts before drafting. If not, use supplied examples or the best repo\u002Fsite material available.\n\n## Stage 1: Signal Scoring\n\nSearch for high-signal people in target verticals. Assign a weight to each based on:\n\n| Signal | Weight | Source |\n|--------|--------|--------|\n| Role\u002Ftitle alignment | 30% | Exa, LinkedIn |\n| Industry match | 25% | Exa company search |\n| Recent activity on topic | 20% | X API search, Exa |\n| Follower count \u002F influence | 10% | X API |\n| Location proximity | 10% | Exa, LinkedIn |\n| Engagement with your content | 5% | X API interactions |\n\n### Signal Search Approach\n\n```python\n# Step 1: Define target parameters\ntarget_verticals = [\"prediction markets\", \"AI tooling\", \"developer tools\"]\ntarget_roles = [\"founder\", \"CEO\", \"CTO\", \"VP Engineering\", \"investor\", \"partner\"]\ntarget_locations = [\"San Francisco\", \"New York\", \"London\", \"remote\"]\n\n# Step 2: Exa deep search for people\nfor vertical in target_verticals:\n    results = web_search_exa(\n        query=f\"{vertical} {role} founder CEO\",\n        category=\"company\",\n        numResults=20\n    )\n    # Score each result\n\n# Step 3: X API search for active voices\nx_search = search_recent_tweets(\n    query=\"prediction markets OR AI tooling OR developer tools\",\n    max_results=100\n)\n# Extract and score unique authors\n```\n\n## Stage 2: Mutual Ranking\n\nFor each scored target, analyze the user's social graph to find the warmest path.\n\n### Ranking Model\n\n1. Pull user's X following list and LinkedIn connections\n2. For each high-signal target, check for shared connections\n3. Apply the `social-graph-ranker` model to score bridge value\n4. Rank mutuals by:\n\n| Factor | Weight |\n|--------|--------|\n| Number of connections to targets | 40% — highest weight, most connections = highest rank |\n| Mutual's current role\u002Fcompany | 20% — decision maker vs individual contributor |\n| Mutual's location | 15% — same city = easier intro |\n| Industry alignment | 15% — same vertical = natural intro |\n| Mutual's X handle \u002F LinkedIn | 10% — identifiability for outreach |\n\nCanonical rule:\n\n```text\nUse social-graph-ranker when the user wants the graph math itself,\nthe bridge ranking as a standalone report, or explicit decay-model tuning.\n```\n\nInside this skill, use the same weighted bridge model:\n\n```text\nB(m) = Σ_{t ∈ T} w(t) · λ^(d(m,t) - 1)\nR(m) = B_ext(m) · (1 + β · engagement(m))\n```\n\nInterpretation:\n- Tier 1: high `R(m)` and direct bridge paths -> warm intro asks\n- Tier 2: medium `R(m)` and one-hop bridge paths -> conditional intro asks\n- Tier 3: no viable bridge -> direct cold outreach using the same lead record\n\n### Output Format\n\n```\n\nIf the user explicitly wants the ranking engine broken out, the math visualized, or the network scored outside the full lead workflow, run `social-graph-ranker` as a standalone pass first and feed the result back into this pipeline.\nMUTUAL RANKING REPORT\n=====================\n\n#1  @mutual_handle (Score: 92)\n    Name: Jane Smith\n    Role: Partner @ Acme Ventures\n    Location: San Francisco\n    Connections to targets: 7\n    Connected to: @target1, @target2, @target3, @target4, @target5, @target6, @target7\n    Best intro path: Jane invested in Target1's company\n\n#2  @mutual_handle2 (Score: 85)\n    ...\n```\n\n## Stage 3: Warm Path Discovery\n\nFor each target, find the shortest introduction chain:\n\n```\nYou ──[follows]──> Mutual A ──[invested in]──> Target Company\nYou ──[follows]──> Mutual B ──[co-founded with]──> Target Person\nYou ──[met at]──> Event ──[also attended]──> Target Person\n```\n\n### Path Types (ordered by warmth)\n1. **Direct mutual** — You both follow\u002Fknow the same person\n2. **Portfolio connection** — Mutual invested in or advises target's company\n3. **Co-worker\u002Falumni** — Mutual worked at same company or attended same school\n4. **Event overlap** — Both attended same conference\u002Fprogram\n5. **Content engagement** — Target engaged with mutual's content or vice versa\n\n## Stage 4: Enrichment\n\nFor each qualified lead, pull:\n\n- Full name, current title, company\n- Company size, funding stage, recent news\n- Recent X posts (last 30 days) — topics, tone, interests\n- Mutual interests with user (shared follows, similar content)\n- Recent company events (product launch, funding round, hiring)\n\n### Enrichment Sources\n- Exa: company data, news, blog posts\n- X API: recent tweets, bio, followers\n- GitHub: open source contributions (for developer-centric leads)\n- LinkedIn (via browser-use): full profile, experience, education\n\n## Stage 5: Outreach Draft\n\nGenerate personalized outreach for each lead. The draft should match the source-derived voice profile and the target channel.\n\n### Channel Rules\n\n#### Email\n\n- Use for the highest-value cold outreach, warm intros, investor outreach, and partnership asks\n- Default to drafting in Apple Mail \u002F Mail.app when local desktop control is available\n- Create drafts first, do not send automatically unless the user explicitly asks\n- Subject line should be plain and specific, not clever\n\n#### LinkedIn\n\n- Use when the target is active there, when mutual graph context is stronger on LinkedIn, or when email confidence is low\n- Prefer API access if available\n- Otherwise use browser control to inspect profiles, recent activity, and draft the message\n- Keep it shorter than email and avoid fake professional warmth\n\n#### X\n\n- Use for high-context operator, builder, or investor outreach where public posting behavior matters\n- Prefer API access for search, timeline, and engagement analysis\n- Fall back to browser control when needed\n- DMs and public replies should be much tighter than email and should reference something real from the target's timeline\n\n#### Channel Selection Heuristic\n\nPick one primary channel in this order:\n\n1. warm intro by email\n2. direct email\n3. LinkedIn DM\n4. X DM or reply\n\nUse multi-channel only when there is a strong reason and the cadence will not feel spammy.\n\n### Warm Intro Request (to mutual)\n\nGoal:\n\n- one clear ask\n- one concrete reason this intro makes sense\n- easy-to-forward blurb if needed\n\nAvoid:\n\n- overexplaining your company\n- social-proof stacking\n- sounding like a fundraiser template\n\n### Direct Cold Outreach (to target)\n\nGoal:\n\n- open from something specific and recent\n- explain why the fit is real\n- make one low-friction ask\n\nAvoid:\n\n- generic admiration\n- feature dumping\n- broad asks like \"would love to connect\"\n- forced rhetorical questions\n\n### Execution Pattern\n\nFor each target, produce:\n\n1. the recommended channel\n2. the reason that channel is best\n3. the message draft\n4. optional follow-up draft\n5. if email is the chosen channel and Apple Mail is available, create a draft instead of only returning text\n\nIf browser control is available:\n\n- LinkedIn: inspect target profile, recent activity, and mutual context, then draft or prepare the message\n- X: inspect recent posts or replies, then draft DM or public reply language\n\nIf desktop automation is available:\n\n- Apple Mail: create draft email with subject, body, and recipient\n\nDo not send messages automatically without explicit user approval.\n\n### Anti-Patterns\n\n- generic templates with no personalization\n- long paragraphs explaining your whole company\n- multiple asks in one message\n- fake familiarity without specifics\n- bulk-sent messages with visible merge fields\n- identical copy reused for email, LinkedIn, and X\n- platform-shaped slop instead of the author's actual voice\n\n## Configuration\n\nUsers should set these environment variables:\n\n```bash\n# Required\nexport X_BEARER_TOKEN=\"...\"\nexport X_ACCESS_TOKEN=\"...\"\nexport X_ACCESS_TOKEN_SECRET=\"...\"\nexport X_CONSUMER_KEY=\"...\"\nexport X_CONSUMER_SECRET=\"...\"\nexport EXA_API_KEY=\"...\"\n\n# Optional\nexport LINKEDIN_COOKIE=\"...\" # For browser-use LinkedIn access\nexport APOLLO_API_KEY=\"...\"  # For Apollo enrichment\n```\n\n## Agents\n\nThis skill includes specialized agents in the `agents\u002F` subdirectory:\n\n- **signal-scorer** — Searches and ranks prospects by relevance signals\n- **mutual-mapper** — Maps social graph connections and finds warm paths\n- **enrichment-agent** — Pulls detailed profile and company data\n- **outreach-drafter** — Generates personalized messages\n\n## Example Usage\n\n```\nUser: find me the top 20 people in prediction markets I should reach out to\n\nAgent workflow:\n1. signal-scorer searches Exa and X for prediction market leaders\n2. mutual-mapper checks user's X graph for shared connections\n3. enrichment-agent pulls company data and recent activity\n4. outreach-drafter generates personalized messages for top ranked leads\n\nOutput: Ranked list with warm paths, voice profile summary, and channel-specific outreach drafts or drafts-in-app\n```\n\n## Related Skills\n\n- `brand-voice` for canonical voice capture\n- `connections-optimizer` for review-first network pruning and expansion before outreach\n",{"name":5,"origin":51,"description":7},"ECC","master","8ec37df6d71fb664ea19d32c079e7d3ff931a8e0","skills\u002Fecc\u002Flead-intelligence",3,"https:\u002F\u002Fgithub.com\u002FS3YED\u002Fappie-kit","https:\u002F\u002Fweblyfe.ai","https:\u002F\u002Fgithub.com\u002FS3YED","2026-08-27 01:54:02",{"source":8,"skillName":5,"commands":61,"prompt":71,"zipAvailable":72},[62,65,68],{"tool":63,"command":64},"npx","npx skills add S3YED\u002Fappie-kit --skill lead-intelligence",{"tool":66,"command":67},"bunx","bunx skills add S3YED\u002Fappie-kit --skill lead-intelligence",{"tool":69,"command":70},"pnpm","pnpm dlx skills add S3YED\u002Fappie-kit --skill lead-intelligence","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FS3YED\u002Fappie-kit\u002Fblob\u002Fmaster\u002Fskills\u002Fecc\u002Flead-intelligence\u002FSKILL.md",true,[74,80,84,88],{"path":75,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":78,"sourceHtmlUrl":79},"agents\u002Fenrichment-agent.md","REFERENCE","参考文档",2319,"https:\u002F\u002Fgithub.com\u002FS3YED\u002Fappie-kit\u002Fblob\u002Fmaster\u002Fskills\u002Fecc\u002Flead-intelligence\u002Fagents\u002Fenrichment-agent.md",{"path":81,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":82,"sourceHtmlUrl":83},"agents\u002Fmutual-mapper.md",2549,"https:\u002F\u002Fgithub.com\u002FS3YED\u002Fappie-kit\u002Fblob\u002Fmaster\u002Fskills\u002Fecc\u002Flead-intelligence\u002Fagents\u002Fmutual-mapper.md",{"path":85,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":86,"sourceHtmlUrl":87},"agents\u002Foutreach-drafter.md",3065,"https:\u002F\u002Fgithub.com\u002FS3YED\u002Fappie-kit\u002Fblob\u002Fmaster\u002Fskills\u002Fecc\u002Flead-intelligence\u002Fagents\u002Foutreach-drafter.md",{"path":89,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":90,"sourceHtmlUrl":91},"agents\u002Fsignal-scorer.md",2028,"https:\u002F\u002Fgithub.com\u002FS3YED\u002Fappie-kit\u002Fblob\u002Fmaster\u002Fskills\u002Fecc\u002Flead-intelligence\u002Fagents\u002Fsignal-scorer.md",[93,125,160,185,216,241],{"skillNo":94,"name":95,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":97,"categories":98,"keywords":106,"publishedAt":46},"01M6JFD4M6N4DSGC7TXEJ5R3J2","ai-music","AI music generation and audio tools: HeartMuLa lyrics-to-music generation, songwriting craft (+ Suno prompts), and audio spectrogram\u002Ffeature visualization.","https:\u002F\u002Fgithub.com\u002FS3YED\u002Fappie-kit\u002Fblob\u002Fmaster\u002Fskills\u002Fwethlete\u002Fai-music\u002FSKILL.md",[99,102,105],{"categoryNo":100,"categoryName":101,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":103,"categoryName":104,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},[107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124],"music","ai music","humanizer","visualization","Tools","Audio","hermes","hermes agent","songwriting","suno","lyrics","prompts","heartmula","craft","generation","feature","ai music generation","music generation",{"skillNo":126,"name":127,"nameZh":6,"description":128,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":129,"categories":130,"keywords":139,"publishedAt":46},"01M6JFCZE4XNFQFMNZDHKHT5V5","agentic-webdesign-design-system-ingestion","Use v0 Design Systems 2.0, Bolt Design System Agents, or Lovable to generate production UI from a client's real design system components — not generic Tailwind. Use when building a client website where brand consistency matters.","https:\u002F\u002Fgithub.com\u002FS3YED\u002Fappie-kit\u002Fblob\u002Fmaster\u002Fskills\u002Fweb-design\u002Fagentic-webdesign-design-system-ingestion\u002FSKILL.md",[131,134],{"categoryNo":132,"categoryName":133,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":135,"categoryName":136,"taxonomyType":137,"taxonomyTypeDesc":138},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159],"design","agentic","design system","system","ingestion","webdesign","agentic webdesign design system ingestion","components","generate","design systems","website","ui","Tailwind","bolt","lovable","v0","design-system","wed","design system components","appie",{"skillNo":161,"name":162,"nameZh":6,"description":163,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":164,"categories":165,"keywords":168,"publishedAt":46},"01M6JFCVPX3913CFAS6QQJ33ZB","agentic-webdesign-agent-elements","Use 21st.dev Agent Elements — 25 shadcn\u002Fui components purpose-built for AI agent UIs. Tool cards (Bash, Edit, Search, Plan, Subagent, MCP), chat, input, streaming. Open source, shadcn-registry install, no runtime lock-in.","https:\u002F\u002Fgithub.com\u002FS3YED\u002Fappie-kit\u002Fblob\u002Fmaster\u002Fskills\u002Fweb-design\u002Fagentic-webdesign-agent-elements\u002FSKILL.md",[166,167],{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":135,"categoryName":136,"taxonomyType":137,"taxonomyTypeDesc":138},[141,169,145,170,171,172,147,173,174,175,176,177,178,179,180,181,182,183,184,29],"elements","agentic webdesign agent elements","MCP","ai agent","registry","search","streaming","open source","open","Bash","plan","subagent","edit","install","input","lock",{"skillNo":186,"name":187,"nameZh":6,"description":188,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":189,"categories":190,"keywords":195,"publishedAt":46},"01M6JFCQC7H65M9QJ6TRE71BS1","vercel-deployment","Deploy static sites and frontends to Vercel — handles both CLI and REST API paths, including the CLI token sanitization workaround.","https:\u002F\u002Fgithub.com\u002FS3YED\u002Fappie-kit\u002Fblob\u002Fmaster\u002Fskills\u002Fsoftware-development\u002Fvercel-deployment\u002FSKILL.md",[191,194],{"categoryNo":192,"categoryName":193,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},[196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215],"deployment","vercel deployment","vercel","macos","cli","linux","windows","deploy","vercel deploy","REST","api","site","deploy to vercel","token","rest api","host","sites","paths","sanitization","static site",{"skillNo":217,"name":218,"nameZh":6,"description":219,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":220,"categories":221,"keywords":223,"publishedAt":46},"01M6JFCM4SMTXBM5A01BK2YQG1","software-quality-workflows","Umbrella workflow for TDD, debugging, code simplification, spikes, pre-commit review, and debugger-assisted investigation.","https:\u002F\u002Fgithub.com\u002FS3YED\u002Fappie-kit\u002Fblob\u002Fmaster\u002Fskills\u002Fsoftware-development\u002Fsoftware-quality-workflows\u002FSKILL.md",[222],{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},[224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240],"workflows","quality","software","software quality workflows","review","tdd","workflow","commit","debugging","code","debugger","pre-commit","investigation","code simplification","user","simplification","for",{"skillNo":242,"name":243,"nameZh":6,"description":244,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":245,"categories":246,"keywords":254,"publishedAt":46},"01M6JFCGD0M6CEATY01W7YN6DB","react-component-refactoring","Safely refactor large React components into smaller, maintainable pieces using proven patterns","https:\u002F\u002Fgithub.com\u002FS3YED\u002Fappie-kit\u002Fblob\u002Fmaster\u002Fskills\u002Fsoftware-development\u002Freact-component-refactoring\u002FSKILL.md",[247,250,253],{"categoryNo":248,"categoryName":249,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":251,"categoryName":252,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},[255,256,257,258,259,260,261,262,199,263,264,147,265,202,266,233,267,268,269,270],"react","component","refactoring","component refactoring","react component","react component refactoring","frontend","writing","safe","typescript","react components","modular","refactor","simplify","patterns","test-driven-development"]