[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7BFS26FQ85BCFDP233MH5KT":3,"skill-same:01M7BFS26FQ85BCFDP233MH5KT":48},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":25,"commitSha":26,"skillDirectory":27,"forksCount":28,"repositoryHtmlUrl":29,"repositoryHomepageUrl":30,"ownerHtmlUrl":31,"ownerBlogUrl":6,"ownerDescription":32,"updatedAt":33,"install":34,"resources":47},"01M7BFS26FQ85BCFDP233MH5KT","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.","YYC-Cube\u002FYYC3-Knowledge-Base","YYC-Cube","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F235006543?v=4","01M79J2PH4SWD2X0825X6PN748","yyc3","01M79J2PHVK3F6ZBYT8K2SCZCW",1,"MIT","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-AI-应用工具\u002FYYC3-AI-应用工具-Everything-claude-code\u002Fskills\u002Fconnections-optimizer\u002FSKILL.md",[],[],"2026-11-15 14:19:17",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":24,"description":7},"ECC","main","87afe6ce96220accf50a345dea46784c8c9f210e","YYC3-AI-应用工具\u002FYYC3-AI-应用工具-Everything-claude-code\u002Fskills\u002Fconnections-optimizer",0,"https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base","https:\u002F\u002Fkb.yyc3.top\u002F","https:\u002F\u002Fgithub.com\u002FYYC-Cube","万象归元于云枢 | 深栈智启新纪元","2026-11-10 10:37:32",{"source":8,"skillName":5,"commands":35,"prompt":45,"zipAvailable":46},[36,39,42],{"tool":37,"command":38},"npx","npx skills add YYC-Cube\u002FYYC3-Knowledge-Base --skill connections-optimizer",{"tool":40,"command":41},"bunx","bunx skills add YYC-Cube\u002FYYC3-Knowledge-Base --skill connections-optimizer",{"tool":43,"command":44},"pnpm","pnpm dlx skills add YYC-Cube\u002FYYC3-Knowledge-Base --skill connections-optimizer","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-AI-应用工具\u002FYYC3-AI-应用工具-Everything-claude-code\u002Fskills\u002Fconnections-optimizer\u002FSKILL.md",true,[],[49,56,63,70,77,84],{"skillNo":50,"name":51,"nameZh":6,"description":52,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":53,"categories":54,"keywords":55,"publishedAt":19},"01M7CTDWHERCXPQCP0K03A86AS","vercel-sandbox","Run agent-browser + Chrome inside Vercel Sandbox microVMs for browser automation from any Vercel-deployed app. Use when the user needs browser automation in a Vercel app (Next.js, SvelteKit, Nuxt, Remix, Astro, etc.), wants to run headless Chrome without binary size limits, needs persistent browser sessions across commands, or wants ephemeral isolated browser environments. Triggers include \"Vercel Sandbox browser\", \"microVM Chrome\", \"agent-browser in sandbox\", \"browser automation on Vercel\", or any task requiring Chrome in a Vercel Sandbox.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Fvercel-sandbox\u002FSKILL.md",[],[],{"skillNo":57,"name":58,"nameZh":6,"description":59,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":60,"categories":61,"keywords":62,"publishedAt":19},"01M7CTD8D2BNJY54S65M47HEJA","slack","Interact with Slack workspaces using browser automation. Use when the user needs to check unread channels, navigate Slack, send messages, extract data, find information, search conversations, or automate any Slack task. Triggers include \"check my Slack\", \"what channels have unreads\", \"send a message to\", \"search Slack for\", \"extract from Slack\", \"find who said\", or any task requiring programmatic Slack interaction.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Fslack\u002FSKILL.md",[],[],{"skillNo":64,"name":65,"nameZh":6,"description":66,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":67,"categories":68,"keywords":69,"publishedAt":19},"01M7CTCSSB8ZP3Q58C3YZDZK1W","electron","Automate Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify, etc.) using agent-browser via Chrome DevTools Protocol. Use when the user needs to interact with an Electron app, automate a desktop app, connect to a running app, control a native app, or test an Electron application. Triggers include \"automate Slack app\", \"control VS Code\", \"interact with Discord app\", \"test this Electron app\", \"connect to desktop app\", or any task requiring automation of a native Electron application.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Felectron\u002FSKILL.md",[],[],{"skillNo":71,"name":72,"nameZh":6,"description":73,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":74,"categories":75,"keywords":76,"publishedAt":19},"01M7CTCBJY76688V4DCZGWFHGQ","dogfood","Systematically explore and test a web application to find bugs, UX issues, and other problems. Use when asked to \"dogfood\", \"QA\", \"exploratory test\", \"find issues\", \"bug hunt\", \"test this app\u002Fsite\u002Fplatform\", or review the quality of a web application. Produces a structured report with full reproduction evidence -- step-by-step screenshots, repro videos, and detailed repro steps for every issue -- so findings can be handed directly to the responsible teams.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Fdogfood\u002FSKILL.md",[],[],{"skillNo":78,"name":79,"nameZh":6,"description":80,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":81,"categories":82,"keywords":83,"publishedAt":19},"01M7CTC4H0FFGVEAQR9PA4GA1A","agent-browser","Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to \"open a website\", \"fill out a form\", \"click a button\", \"take a screenshot\", \"scrape data from a page\", \"test this web app\", \"login to a site\", \"automate browser actions\", or any task requiring programmatic web interaction.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Fagent-browser\u002FSKILL.md",[],[],{"skillNo":85,"name":86,"nameZh":6,"description":87,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":90,"publishedAt":19},"01M7CTBKVFYBG78FDSABSDJTTQ","shadcn","Manages shadcn components and projects — adding, searching, fixing, debugging, styling, and composing UI, including chat interfaces. Provides project context, component docs, and usage examples. Applies when working with shadcn\u002Fui, component registries, presets, --preset codes, or any project with a components.json file. Also triggers for \"shadcn init\", \"create an app with --preset\", \"switch to --preset\", and for building a design system or applying a DESIGN.md or brand spec to shadcn\u002Fui.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-UI-界面组件\u002FYYC3-UI-界面组件-ui\u002Fskills\u002Fshadcn\u002FSKILL.md",[],[]]