[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6JCS72KEPCVTY330PVQ5CW6":3,"skill-same:01M6JCS72KEPCVTY330PVQ5CW6":71},{"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":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":49,"commitSha":50,"skillDirectory":51,"forksCount":52,"repositoryHtmlUrl":53,"repositoryHomepageUrl":54,"ownerHtmlUrl":55,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":56,"install":57,"resources":70},"01M6JCS72KEPCVTY330PVQ5CW6","connections-optimizer",null,"Reorganize the user's X and LinkedIn network with review-first pruning, add\u002Ffollow recommendations, and channel-specific warm outreach drafted in the user's real voice. Use when the user wants to clean up following lists, grow toward current priorities, or rebalance a social graph around higher-signal relationships.","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\u002Fconnections-optimizer\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"connections","connections optimizer","optimizer","relationships","graph","with","add","grow","recommendations","outreach","network","priorities","first","review","social","clean","up","use","linkedin","voice","2026-11-10 10:12:30",false,"---\nname: connections-optimizer\ndescription: Reorganize the user's X and LinkedIn network with review-first pruning, add\u002Ffollow recommendations, and channel-specific warm outreach drafted in the user's real voice. Use when the user wants to clean up following lists, grow toward current priorities, or rebalance a social graph around higher-signal relationships.\norigin: ECC\n---\n\n# Connections Optimizer\n\nReorganize the user's network instead of treating outbound as a one-way prospecting list.\n\nThis skill handles:\n\n- X following cleanup and expansion\n- LinkedIn follow and connection analysis\n- review-first prune queues\n- add and follow recommendations\n- warm-path identification\n- Apple Mail, X DM, and LinkedIn draft generation in the user's real voice\n\n## When to Activate\n\n- the user wants to prune their X following\n- the user wants to rebalance who they follow or stay connected to\n- the user says \"clean up my network\", \"who should I unfollow\", \"who should I follow\", \"who should I reconnect with\"\n- outreach quality depends on network structure, not just cold list generation\n\n## Required Inputs\n\nCollect or infer:\n\n- current priorities and active work\n- target roles, industries, geos, or ecosystems\n- platform selection: X, LinkedIn, or both\n- do-not-touch list\n- mode: `light-pass`, `default`, or `aggressive`\n\nIf the user does not specify a mode, use `default`.\n\n## Tool Requirements\n\n### Preferred\n\n- `x-api` for X graph inspection and recent activity\n- `lead-intelligence` for target discovery and warm-path ranking\n- `social-graph-ranker` when the user wants bridge value scored independently of the broader lead workflow\n- Exa \u002F deep research for person and company enrichment\n- `brand-voice` before drafting outbound\n\n### Fallbacks\n\n- browser control for LinkedIn analysis and drafting\n- browser control for X if API coverage is constrained\n- Apple Mail or Mail.app drafting via desktop automation when email is the right channel\n\n## Safety Defaults\n\n- default is review-first, never blind auto-pruning\n- X: prune only accounts the user follows, never followers\n- LinkedIn: treat 1st-degree connection removal as manual-review-first\n- do not auto-send DMs, invites, or emails\n- emit a ranked action plan and drafts before any apply step\n\n## Platform Rules\n\n### X\n\n- mutuals are stickier than one-way follows\n- non-follow-backs can be pruned more aggressively\n- heavily inactive or disappeared accounts should surface quickly\n- engagement, signal quality, and bridge value matter more than raw follower count\n\n### LinkedIn\n\n- API-first if the user actually has LinkedIn API access\n- browser workflow must work when API access is missing\n- distinguish outbound follows from accepted 1st-degree connections\n- outbound follows can be pruned more freely\n- accepted 1st-degree connections should default to review, not auto-remove\n\n## Modes\n\n### `light-pass`\n\n- prune only high-confidence low-value one-way follows\n- surface the rest for review\n- generate a small add\u002Ffollow list\n\n### `default`\n\n- balanced prune queue\n- balanced keep list\n- ranked add\u002Ffollow queue\n- draft warm intros or direct outreach where useful\n\n### `aggressive`\n\n- larger prune queue\n- lower tolerance for stale non-follow-backs\n- still review-gated before apply\n\n## Scoring Model\n\nUse these positive signals:\n\n- reciprocity\n- recent activity\n- alignment to current priorities\n- network bridge value\n- role relevance\n- real engagement history\n- recent presence and responsiveness\n\nUse these negative signals:\n\n- disappeared or abandoned account\n- stale one-way follow\n- off-priority topic cluster\n- low-value noise\n- repeated non-response\n- no follow-back when many better replacements exist\n\nMutuals and real warm-path bridges should be penalized less aggressively than one-way follows.\n\n## Workflow\n\n1. Capture priorities, do-not-touch constraints, and selected platforms.\n2. Pull the current following \u002F connection inventory.\n3. Score prune candidates with explicit reasons.\n4. Score keep candidates with explicit reasons.\n5. Use `lead-intelligence` plus research surfaces to rank expansion candidates.\n6. Match the right channel:\n   - X DM for warm, fast social touch points\n   - LinkedIn message for professional graph adjacency\n   - Apple Mail draft for higher-context intros or outreach\n7. Run `brand-voice` before drafting messages.\n8. Return a review pack before any apply step.\n\n## Review Pack Format\n\n```text\nCONNECTIONS OPTIMIZER REPORT\n============================\n\nMode:\nPlatforms:\nPriority Set:\n\nPrune Queue\n- handle \u002F profile\n  reason:\n  confidence:\n  action:\n\nReview Queue\n- handle \u002F profile\n  reason:\n  risk:\n\nKeep \u002F Protect\n- handle \u002F profile\n  bridge value:\n\nAdd \u002F Follow Targets\n- person\n  why now:\n  warm path:\n  preferred channel:\n\nDrafts\n- X DM:\n- LinkedIn:\n- Apple Mail:\n```\n\n## Outbound Rules\n\n- Default email path is Apple Mail \u002F Mail.app draft creation.\n- Do not send automatically.\n- Choose the channel based on warmth, relevance, and context depth.\n- Do not force a DM when an email or no outreach is the right move.\n- Drafts should sound like the user, not like automated sales copy.\n\n## Related Skills\n\n- `brand-voice` for the reusable voice profile\n- `social-graph-ranker` for the standalone bridge-scoring and warm-path math\n- `lead-intelligence` for weighted target and warm-path discovery\n- `x-api` for X graph access, drafting, and optional apply flows\n- `content-engine` when the user also wants public launch content around network moves\n","# Connections Optimizer\n\nReorganize the user's network instead of treating outbound as a one-way prospecting list.\n\nThis skill handles:\n\n- X following cleanup and expansion\n- LinkedIn follow and connection analysis\n- review-first prune queues\n- add and follow recommendations\n- warm-path identification\n- Apple Mail, X DM, and LinkedIn draft generation in the user's real voice\n\n## When to Activate\n\n- the user wants to prune their X following\n- the user wants to rebalance who they follow or stay connected to\n- the user says \"clean up my network\", \"who should I unfollow\", \"who should I follow\", \"who should I reconnect with\"\n- outreach quality depends on network structure, not just cold list generation\n\n## Required Inputs\n\nCollect or infer:\n\n- current priorities and active work\n- target roles, industries, geos, or ecosystems\n- platform selection: X, LinkedIn, or both\n- do-not-touch list\n- mode: `light-pass`, `default`, or `aggressive`\n\nIf the user does not specify a mode, use `default`.\n\n## Tool Requirements\n\n### Preferred\n\n- `x-api` for X graph inspection and recent activity\n- `lead-intelligence` for target discovery and warm-path ranking\n- `social-graph-ranker` when the user wants bridge value scored independently of the broader lead workflow\n- Exa \u002F deep research for person and company enrichment\n- `brand-voice` before drafting outbound\n\n### Fallbacks\n\n- browser control for LinkedIn analysis and drafting\n- browser control for X if API coverage is constrained\n- Apple Mail or Mail.app drafting via desktop automation when email is the right channel\n\n## Safety Defaults\n\n- default is review-first, never blind auto-pruning\n- X: prune only accounts the user follows, never followers\n- LinkedIn: treat 1st-degree connection removal as manual-review-first\n- do not auto-send DMs, invites, or emails\n- emit a ranked action plan and drafts before any apply step\n\n## Platform Rules\n\n### X\n\n- mutuals are stickier than one-way follows\n- non-follow-backs can be pruned more aggressively\n- heavily inactive or disappeared accounts should surface quickly\n- engagement, signal quality, and bridge value matter more than raw follower count\n\n### LinkedIn\n\n- API-first if the user actually has LinkedIn API access\n- browser workflow must work when API access is missing\n- distinguish outbound follows from accepted 1st-degree connections\n- outbound follows can be pruned more freely\n- accepted 1st-degree connections should default to review, not auto-remove\n\n## Modes\n\n### `light-pass`\n\n- prune only high-confidence low-value one-way follows\n- surface the rest for review\n- generate a small add\u002Ffollow list\n\n### `default`\n\n- balanced prune queue\n- balanced keep list\n- ranked add\u002Ffollow queue\n- draft warm intros or direct outreach where useful\n\n### `aggressive`\n\n- larger prune queue\n- lower tolerance for stale non-follow-backs\n- still review-gated before apply\n\n## Scoring Model\n\nUse these positive signals:\n\n- reciprocity\n- recent activity\n- alignment to current priorities\n- network bridge value\n- role relevance\n- real engagement history\n- recent presence and responsiveness\n\nUse these negative signals:\n\n- disappeared or abandoned account\n- stale one-way follow\n- off-priority topic cluster\n- low-value noise\n- repeated non-response\n- no follow-back when many better replacements exist\n\nMutuals and real warm-path bridges should be penalized less aggressively than one-way follows.\n\n## Workflow\n\n1. Capture priorities, do-not-touch constraints, and selected platforms.\n2. Pull the current following \u002F connection inventory.\n3. Score prune candidates with explicit reasons.\n4. Score keep candidates with explicit reasons.\n5. Use `lead-intelligence` plus research surfaces to rank expansion candidates.\n6. Match the right channel:\n   - X DM for warm, fast social touch points\n   - LinkedIn message for professional graph adjacency\n   - Apple Mail draft for higher-context intros or outreach\n7. Run `brand-voice` before drafting messages.\n8. Return a review pack before any apply step.\n\n## Review Pack Format\n\n```text\nCONNECTIONS OPTIMIZER REPORT\n============================\n\nMode:\nPlatforms:\nPriority Set:\n\nPrune Queue\n- handle \u002F profile\n  reason:\n  confidence:\n  action:\n\nReview Queue\n- handle \u002F profile\n  reason:\n  risk:\n\nKeep \u002F Protect\n- handle \u002F profile\n  bridge value:\n\nAdd \u002F Follow Targets\n- person\n  why now:\n  warm path:\n  preferred channel:\n\nDrafts\n- X DM:\n- LinkedIn:\n- Apple Mail:\n```\n\n## Outbound Rules\n\n- Default email path is Apple Mail \u002F Mail.app draft creation.\n- Do not send automatically.\n- Choose the channel based on warmth, relevance, and context depth.\n- Do not force a DM when an email or no outreach is the right move.\n- Drafts should sound like the user, not like automated sales copy.\n\n## Related Skills\n\n- `brand-voice` for the reusable voice profile\n- `social-graph-ranker` for the standalone bridge-scoring and warm-path math\n- `lead-intelligence` for weighted target and warm-path discovery\n- `x-api` for X graph access, drafting, and optional apply flows\n- `content-engine` when the user also wants public launch content around network moves\n",{"name":5,"origin":48,"description":7},"ECC","master","8ec37df6d71fb664ea19d32c079e7d3ff931a8e0","skills\u002Fecc\u002Fconnections-optimizer",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":58,"prompt":68,"zipAvailable":69},[59,62,65],{"tool":60,"command":61},"npx","npx skills add S3YED\u002Fappie-kit --skill connections-optimizer",{"tool":63,"command":64},"bunx","bunx skills add S3YED\u002Fappie-kit --skill connections-optimizer",{"tool":66,"command":67},"pnpm","pnpm dlx skills add S3YED\u002Fappie-kit --skill connections-optimizer","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FS3YED\u002Fappie-kit\u002Fblob\u002Fmaster\u002Fskills\u002Fecc\u002Fconnections-optimizer\u002FSKILL.md",true,[],[72,104,139,165,196,220],{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":76,"categories":77,"keywords":85,"publishedAt":43},"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",[78,81,84],{"categoryNo":79,"categoryName":80,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":82,"categoryName":83,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103],"music","ai music","humanizer","visualization","Tools","Audio","hermes","hermes agent","songwriting","suno","lyrics","prompts","heartmula","craft","generation","feature","ai music generation","music generation",{"skillNo":105,"name":106,"nameZh":6,"description":107,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":108,"categories":109,"keywords":118,"publishedAt":43},"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",[110,113],{"categoryNo":111,"categoryName":112,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":114,"categoryName":115,"taxonomyType":116,"taxonomyTypeDesc":117},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138],"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":140,"name":141,"nameZh":6,"description":142,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":143,"categories":144,"keywords":147,"publishedAt":43},"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",[145,146],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":114,"categoryName":115,"taxonomyType":116,"taxonomyTypeDesc":117},[120,148,124,149,150,151,126,152,153,154,155,156,157,158,159,160,161,162,163,164],"elements","agentic webdesign agent elements","MCP","ai agent","registry","search","streaming","open source","open","Bash","plan","subagent","edit","install","input","lock","source",{"skillNo":166,"name":167,"nameZh":6,"description":168,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":169,"categories":170,"keywords":175,"publishedAt":43},"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",[171,174],{"categoryNo":172,"categoryName":173,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195],"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":197,"name":198,"nameZh":6,"description":199,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":200,"categories":201,"keywords":203,"publishedAt":43},"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",[202],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[204,205,206,207,36,208,209,210,211,212,213,214,215,216,217,218,219],"workflows","quality","software","software quality workflows","tdd","workflow","commit","debugging","code","debugger","pre-commit","investigation","code simplification","user","simplification","for",{"skillNo":221,"name":222,"nameZh":6,"description":223,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":224,"categories":225,"keywords":233,"publishedAt":43},"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",[226,229,232],{"categoryNo":227,"categoryName":228,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":230,"categoryName":231,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[234,235,236,237,238,239,240,241,179,242,243,126,244,182,245,212,246,247,248,249],"react","component","refactoring","component refactoring","react component","react component refactoring","frontend","writing","safe","typescript","react components","modular","refactor","simplify","patterns","test-driven-development"]