[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M718FR9Z4KWHTGRMRWEB3VDH":3,"skill-same:01M718FR9Z4KWHTGRMRWEB3VDH":72},{"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":23,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":44,"commitSha":45,"skillDirectory":46,"forksCount":47,"repositoryHtmlUrl":48,"repositoryHomepageUrl":6,"ownerHtmlUrl":49,"ownerBlogUrl":50,"ownerDescription":6,"updatedAt":51,"install":52,"resources":65},"01M718FR9Z4KWHTGRMRWEB3VDH","account-research",null,"Research a company using Common Room data. Triggers on 'research [company]', 'tell me about [domain]', 'pull up signals for [account]', 'what's going on with [company]', or any account-level question.","anthropics\u002Fknowledge-work-plugins","anthropics","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F76263028?v=4","01M697XGDPXJAVQ8391V2BCYTE","Anthropic","01M7188MPC48111TN566QNQE3X",35660,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fpartner-built\u002Fcommon-room\u002Fskills\u002Faccount-research\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"research","account research","pull","domain","for","company","up","triggers","question","common","with","me","signals","about","data","2026-11-10 10:12:30",false,"---\nname: account-research\ndescription: \"Research a company using Common Room data. Triggers on 'research [company]', 'tell me about [domain]', 'pull up signals for [account]', 'what's going on with [company]', or any account-level question.\"\n---\n\n# Account Research\n\nRetrieve and synthesize account information from Common Room. Handles four interaction patterns: full overviews, targeted field questions, sparse data situations, and combined MCP data + LLM reasoning.\n\n## Step 0: Load User Context (Me)\n\nBefore researching any account, fetch the `Me` object from Common Room. This provides:\n- The user's profile, title, role, and Persona in CR\n- The user's segments (\"My Segments\")\n\nDefault all queries to the user's own segments unless the user explicitly asks for a broader view. This keeps results scoped to their territory.\n\n## Step 1: Identify the Interaction Pattern\n\nDetermine what the user actually needs before deciding how much data to fetch:\n\n**Pattern 1 — Full Overview:** \"Tell me about Datadog\" \u002F \"Summarize cloudflare.com\"\n→ Fetch the full field set and produce a structured briefing.\n\n**Pattern 2 — Targeted Question:** \"Who owns the Snowflake account?\" \u002F \"Is acme.io showing buying signals?\" \u002F \"What's the employee count for notion.so?\"\n→ Fetch only the relevant field(s). Return a direct, concise answer — do not produce a full brief for a simple question.\n\n**Pattern 3 — Sparse Data:** \"Tell me about tiny-startup.io\"\n→ If Common Room has limited data for an account, say so honestly: \"There is limited information available for this account.\" Never speculate or fill gaps with generic statements.\n\n**Pattern 4 — Combined Reasoning:** Fetch structured MCP data, then layer in LLM analysis — e.g., \"Stripe has 8,000 employees and is hiring heavily for AI roles. Based on your ICP of 1k–10k fintech companies, this is a strong fit.\"\n\n## Step 2: Look Up the Account\n\nSearch Common Room for the account by domain or company name. Exact match first; if no result, try partial match and confirm with the user before proceeding.\n\n## Step 3: Fetch the Right Fields\n\nUse the Common Room object catalog to see available field groups and their contents. For full overviews, request all field groups. For targeted questions, request only what's relevant.\n\n**Key field groups to know about:**\n- **Scores** — always return as raw values or percentiles, never labels\n- **Summary research** — RoomieAI output; often the richest qualitative signal\n- **Top contacts** — sorted by score desc; use communityMemberID for full lookups\n\n**Choosing what to fetch:**\n\n| User query type | Fields to request |\n|-----------------|------------------|\n| Full account overview | All field groups |\n| \"Who owns this account?\" | Company profiles & links, CRM fields |\n| \"Is this company a good fit?\" | Key fields, scores, about |\n| \"What signals is this account showing?\" | Scores, summary research, CRM fields |\n| \"Who are the top contacts?\" | Top contacts |\n| \"What does RoomieAI say about them?\" | Summary research, all research |\n| \"Find engineers at this account\" | Prospects (with title filter) |\n\n## Step 4: Web Search (Sparse Data Only)\n\nCommon Room is the primary data source. Do not run web search when CR returns rich data.\n\nWhen CR data is sparse (Pattern 3 — few fields returned, no activity, no scores), run a targeted web search to fill gaps:\n- `\"[company name]\" news` — scoped to the last 30 days\n- Look for: funding rounds, acquisitions, product launches, executive changes, press coverage\n\nIf the user explicitly asks for external context or recent news, run web search regardless of data richness.\n\n## Step 5: Apply Reasoning (Pattern 4)\n\nWhen the user's question invites synthesis — not just data retrieval — layer in analysis:\n- Compare account data to known ICP criteria from session context\n- Identify fit signals (size, industry, tech stack, hiring patterns)\n- Note timing signals (funding, trial status, recent activity spike)\n- Frame insights as clearly derived from data, not assumed\n\nWhen the user's company context is available (see `references\u002Fmy-company-context.md`), position findings relative to the user's value proposition and ICP.\n\n## Step 6: Produce Output\n\nOnly include sections where Common Room returned actual data. Omit sections entirely rather than filling them with guesses.\n\n**Full overview (when data is rich):**\n\n```\n## [Company Name] — Account Overview\n\n**Snapshot**\n[2–3 sentences: what they do, plan\u002Fstage, relationship status]\n\n**Key Details**\n[Employee count, industry, location, domain, funding — from key fields]\n\n**CRM & Ownership** [If CRM fields returned]\n[Owner, opp stage, ARR]\n\n**Scores** [If scores returned]\n[All available scores as raw values or percentiles]\n\n**Signal Highlights** [If activity\u002Fsignals exist]\n[3–5 most important signals with dates]\n\n**Top Contacts** [If contacts returned]\n[Name | Title | Score — top 5 sorted by score desc]\n\n**RoomieAI Research** [If summary research is non-null]\n[Summary research output; list all available research topic names]\n\n**Recommended Next Steps**\n[2–3 specific, signal-backed actions]\n```\n\n**Targeted question:** 1–3 sentence direct answer. No full brief needed.\n\n**Sparse data (few fields returned, most sections would be empty):**\n\n```\n## [Company Name] — Account Overview (Limited Data)\n\n**Data available:** [List exactly what Common Room returned]\n\n[Present only the returned fields]\n\n**Web Search**\n[Findings from web search — or \"No significant recent news found\"]\n\n**Note:** Common Room has limited data on this account. The account may need enrichment in Common Room.\n```\n\n## Quality Standards\n\n- Scores must always be raw values or percentiles — never categorical labels\n- For targeted questions, answer precisely and don't over-deliver\n- Be explicit when data is missing or stale — don't speculate\n- Keep full briefings readable in 2–3 minutes\n- **Every fact must trace to a tool call** — don't include data not returned by Common Room\n\n## Reference Files\n\n- **`references\u002Fsignals-guide.md`** — signal type taxonomy and interpretation guide\n\n","# Account Research\n\nRetrieve and synthesize account information from Common Room. Handles four interaction patterns: full overviews, targeted field questions, sparse data situations, and combined MCP data + LLM reasoning.\n\n## Step 0: Load User Context (Me)\n\nBefore researching any account, fetch the `Me` object from Common Room. This provides:\n- The user's profile, title, role, and Persona in CR\n- The user's segments (\"My Segments\")\n\nDefault all queries to the user's own segments unless the user explicitly asks for a broader view. This keeps results scoped to their territory.\n\n## Step 1: Identify the Interaction Pattern\n\nDetermine what the user actually needs before deciding how much data to fetch:\n\n**Pattern 1 — Full Overview:** \"Tell me about Datadog\" \u002F \"Summarize cloudflare.com\"\n→ Fetch the full field set and produce a structured briefing.\n\n**Pattern 2 — Targeted Question:** \"Who owns the Snowflake account?\" \u002F \"Is acme.io showing buying signals?\" \u002F \"What's the employee count for notion.so?\"\n→ Fetch only the relevant field(s). Return a direct, concise answer — do not produce a full brief for a simple question.\n\n**Pattern 3 — Sparse Data:** \"Tell me about tiny-startup.io\"\n→ If Common Room has limited data for an account, say so honestly: \"There is limited information available for this account.\" Never speculate or fill gaps with generic statements.\n\n**Pattern 4 — Combined Reasoning:** Fetch structured MCP data, then layer in LLM analysis — e.g., \"Stripe has 8,000 employees and is hiring heavily for AI roles. Based on your ICP of 1k–10k fintech companies, this is a strong fit.\"\n\n## Step 2: Look Up the Account\n\nSearch Common Room for the account by domain or company name. Exact match first; if no result, try partial match and confirm with the user before proceeding.\n\n## Step 3: Fetch the Right Fields\n\nUse the Common Room object catalog to see available field groups and their contents. For full overviews, request all field groups. For targeted questions, request only what's relevant.\n\n**Key field groups to know about:**\n- **Scores** — always return as raw values or percentiles, never labels\n- **Summary research** — RoomieAI output; often the richest qualitative signal\n- **Top contacts** — sorted by score desc; use communityMemberID for full lookups\n\n**Choosing what to fetch:**\n\n| User query type | Fields to request |\n|-----------------|------------------|\n| Full account overview | All field groups |\n| \"Who owns this account?\" | Company profiles & links, CRM fields |\n| \"Is this company a good fit?\" | Key fields, scores, about |\n| \"What signals is this account showing?\" | Scores, summary research, CRM fields |\n| \"Who are the top contacts?\" | Top contacts |\n| \"What does RoomieAI say about them?\" | Summary research, all research |\n| \"Find engineers at this account\" | Prospects (with title filter) |\n\n## Step 4: Web Search (Sparse Data Only)\n\nCommon Room is the primary data source. Do not run web search when CR returns rich data.\n\nWhen CR data is sparse (Pattern 3 — few fields returned, no activity, no scores), run a targeted web search to fill gaps:\n- `\"[company name]\" news` — scoped to the last 30 days\n- Look for: funding rounds, acquisitions, product launches, executive changes, press coverage\n\nIf the user explicitly asks for external context or recent news, run web search regardless of data richness.\n\n## Step 5: Apply Reasoning (Pattern 4)\n\nWhen the user's question invites synthesis — not just data retrieval — layer in analysis:\n- Compare account data to known ICP criteria from session context\n- Identify fit signals (size, industry, tech stack, hiring patterns)\n- Note timing signals (funding, trial status, recent activity spike)\n- Frame insights as clearly derived from data, not assumed\n\nWhen the user's company context is available (see `references\u002Fmy-company-context.md`), position findings relative to the user's value proposition and ICP.\n\n## Step 6: Produce Output\n\nOnly include sections where Common Room returned actual data. Omit sections entirely rather than filling them with guesses.\n\n**Full overview (when data is rich):**\n\n```\n## [Company Name] — Account Overview\n\n**Snapshot**\n[2–3 sentences: what they do, plan\u002Fstage, relationship status]\n\n**Key Details**\n[Employee count, industry, location, domain, funding — from key fields]\n\n**CRM & Ownership** [If CRM fields returned]\n[Owner, opp stage, ARR]\n\n**Scores** [If scores returned]\n[All available scores as raw values or percentiles]\n\n**Signal Highlights** [If activity\u002Fsignals exist]\n[3–5 most important signals with dates]\n\n**Top Contacts** [If contacts returned]\n[Name | Title | Score — top 5 sorted by score desc]\n\n**RoomieAI Research** [If summary research is non-null]\n[Summary research output; list all available research topic names]\n\n**Recommended Next Steps**\n[2–3 specific, signal-backed actions]\n```\n\n**Targeted question:** 1–3 sentence direct answer. No full brief needed.\n\n**Sparse data (few fields returned, most sections would be empty):**\n\n```\n## [Company Name] — Account Overview (Limited Data)\n\n**Data available:** [List exactly what Common Room returned]\n\n[Present only the returned fields]\n\n**Web Search**\n[Findings from web search — or \"No significant recent news found\"]\n\n**Note:** Common Room has limited data on this account. The account may need enrichment in Common Room.\n```\n\n## Quality Standards\n\n- Scores must always be raw values or percentiles — never categorical labels\n- For targeted questions, answer precisely and don't over-deliver\n- Be explicit when data is missing or stale — don't speculate\n- Keep full briefings readable in 2–3 minutes\n- **Every fact must trace to a tool call** — don't include data not returned by Common Room\n\n## Reference Files\n\n- **`references\u002Fsignals-guide.md`** — signal type taxonomy and interpretation guide\n\n",{"name":5,"description":7},"main","e605d8ddbfb688da57dfaaceebd93f12a64ef1d3","partner-built\u002Fcommon-room\u002Fskills\u002Faccount-research",3944,"https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins","https:\u002F\u002Fgithub.com\u002Fanthropics","https:\u002F\u002Fanthropic.com","2026-11-14 06:21:52",{"source":8,"skillName":5,"commands":53,"prompt":63,"zipAvailable":64},[54,57,60],{"tool":55,"command":56},"npx","npx skills add anthropics\u002Fknowledge-work-plugins --skill account-research",{"tool":58,"command":59},"bunx","bunx skills add anthropics\u002Fknowledge-work-plugins --skill account-research",{"tool":61,"command":62},"pnpm","pnpm dlx skills add anthropics\u002Fknowledge-work-plugins --skill account-research","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fpartner-built\u002Fcommon-room\u002Fskills\u002Faccount-research\u002FSKILL.md",true,[66],{"path":67,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":70,"sourceHtmlUrl":71},"references\u002Fsignals-guide.md","REFERENCE","参考文档",2576,"https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fpartner-built\u002Fcommon-room\u002Fskills\u002Faccount-research\u002Freferences\u002Fsignals-guide.md",[73,99,124,145,174,199],{"skillNo":74,"name":75,"nameZh":6,"description":76,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":77,"categories":78,"keywords":79,"publishedAt":39},"01M718Y97HCGW12Q53PSJDY03W","ticket-deflector","Reads a forwarded customer email or ticket, pulls order and refund status from a payments connector (PayPal, Square, or Stripe) or Shopify, account history from the CRM, and open tickets from a support desk (Zoho Desk), drafts a tone-matched reply in the owner's writing voice, and can issue a refund through the payments connector with explicit owner approval. With Shopify connected it also runs a proactive order-triage mode that surfaces orders needing attention — unfulfilled past the promised window, payment problems, pending refunds, stuck shipments — and drafts the next action for each before the customer has to ask. Use when the user says \"draft a response,\" \"answer this customer,\" \"where's my order,\" \"I want a refund,\" \"check my orders,\" or \"anything about to blow up.\"","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fsmall-business\u002Fskills\u002Fticket-deflector\u002FSKILL.md",[],[80,81,82,30,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98],"ticket","ticket deflector","mode","payments","open","writing","when","workflow","ask","Connector","text","mail","window","next","customer","support","crm","past","read",{"skillNo":100,"name":101,"nameZh":6,"description":102,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":103,"categories":104,"keywords":105,"publishedAt":39},"01M718Y55HFAN5705ME5VEDZ9K","tax-season-organizer","Prepares tax-season materials for the owner's accountant, not tax advice. US federal tax; a non-US business gets its closed-books packet instead. Two modes: (1) quarterly estimated tax from YTD net income in the ledger (MYOB, NetSuite, QuickBooks, Xero, or Zoho Books); (2) year-end 1099 prep, scanning the ledger, PayPal, and Stripe for contractors paid over USD 600 into a 1099-NEC list with missing W-9 flags.\nAny tax request routes first to \u002Ftax-prep, which confirms the books are closed and reconciled before running this skill. Use this skill directly only when the owner says the period's books are already closed: \"books are closed, now do the 1099s,\" \"run the quarterly estimate off the closed numbers,\" or \"just the contractor W-9 list.\"","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fsmall-business\u002Fskills\u002Ftax-season-organizer\u002FSKILL.md",[],[106,107,108,109,110,111,112,113,114,115,86,116,117,118,119,120,98,121,122,123],"tax season organizer","tax","us federal","which","netsuite","paid","end","usd","only","now","books","xero","scanning","run","request","flags","list","stripe",{"skillNo":125,"name":126,"nameZh":6,"description":127,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":128,"categories":129,"keywords":130,"publishedAt":39},"01M718Y13VVPHDD9NTDBVFJ2AZ","tax-prep","Prepares tax materials as a two-link chain — month-end-prep confirms the books are closed and reconciled first, then tax-season-organizer calculates the quarterly estimated payment or builds the year-end 1099-NEC list and accountant packet from those closed numbers. Requires a ledger (MYOB, NetSuite, QuickBooks, Xero, or Zoho Books); uses Gusto, PayPal, and Stripe when connected, else CSV upload. US federal tax math; a non-US business gets the closed-books packet. Prep material for a CPA, never tax advice, nothing filed. Trigger on \"quarterly taxes,\" \"estimated tax payment,\" \"how much should I set aside,\" \"1099s,\" \"1099-NEC,\" \"W-9s,\" \"year-end tax prep,\" \"get my books ready for my accountant,\" any phrasing that suggests a tax deadline is coming, or a question about net profit or YTD income that sounds like worry about a tax bill. An owner who says the period's books are already closed routes to tax-season-organizer directly.","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fsmall-business\u002Fskills\u002Ftax-prep\u002FSKILL.md",[],[131,107,132,133,108,134,135,110,136,137,138,139,140,28,141,112,142,32,143,144],"tax prep","prep","SET","as","ready","never","zoho books","get","chain","profit","trigger","material","paypal","business",{"skillNo":146,"name":147,"nameZh":6,"description":148,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":149,"categories":150,"keywords":156,"publishedAt":39},"01M718XWTWQZ01GYSG9T8RW8TW","speed-to-lead","Makes sure no inbound lead goes unanswered. Watches web forms, the shared inbox, and the CRM for new inquiries, qualifies each one against the owner's criteria, drafts a reply in the owner's voice with real meeting times for the owner to approve and send, routes anything hot to a human right away, and logs the whole thing to the CRM. Never sends on its own. Built to run on a schedule so overnight inquiries are drafted and sorted by morning, and works from forwarded or pasted inquiries when no connector is available. Use this whenever the owner is worried about inbound leads or response time — including phrasings like \"leads are going cold,\" \"did anyone answer that inquiry,\" \"I keep missing form submissions,\" \"nothing falls through the cracks,\" \"handle my inbound,\" \"respond to this lead,\" or \"we lose people because nobody gets back fast enough.\" Reach for it for a single unanswered inquiry as readily as for a standing setup.","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fsmall-business\u002Fskills\u002Fspeed-to-lead\u002FSKILL.md",[151],{"categoryNo":152,"categoryName":153,"taxonomyType":154,"taxonomyTypeDesc":155},"01JBSEEDCAT000000000000034","行政与办公人员","PROFESSION","适用职业",[157,158,159,160,161,162,136,163,164,165,166,167,168,169,86,170,171,89,172,173],"speed to lead","lead","speed","shared","back","cold","time","inbox","send","respond","new","web forms","people","schedule","logs","sure","human",{"skillNo":175,"name":176,"nameZh":6,"description":177,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":178,"categories":179,"keywords":183,"publishedAt":39},"01M718XQYMN1KY3TPS33967FK0","social-content-engine","Runs the owner's content operation as a standing thing rather than a one-off: keeps a rolling posting calendar, learns the owner's voice, generates on-brand Canva graphics, writes captions that sound like them, repurposes one asset into every channel format, and stages posts for approval instead of publishing on its own. Pulls product photos and prices from Shopify when connected; with no connectors, drafts the calendar and asset briefs for manual posting. Use this whenever the owner talks about posting, content, or social — including \"I need to post more,\" \"we've gone quiet on Instagram,\" \"what should we post this month,\" \"make the content,\" \"give me a month of posts,\" \"write this in my voice,\" \"repurpose this,\" or \"set up our content calendar.\" Reach for it for a single post as readily as for a full quarter. One handoff: a finished, approved campaign brief goes to canva-creator; this skill owns the standing operation.","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fsmall-business\u002Fskills\u002Fsocial-content-engine\u002FSKILL.md",[180],{"categoryNo":181,"categoryName":182,"taxonomyType":154,"taxonomyTypeDesc":155},"01JBSEEDCAT000000000000036","内容创作者",[184,185,186,187,188,189,133,190,191,192,193,30,194,86,35,195,196,98,197,198],"social","social content engine","engine","content engine","social content","content","brief","posts","Captions","prices","campaign","product","calendar","write","drafts",{"skillNo":200,"name":201,"nameZh":6,"description":202,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":203,"categories":204,"keywords":205,"publishedAt":39},"01M718XMPJVH0EVRVP998BVZPC","smb-router","The front door to the Small Business plugin. Listens to what the owner needs right now — vague or specific — and routes them to the best skill or slash command for the moment. Also serves as a guide: explains what's available, suggests what to try next, and adapts recommendations based on stored business context. Trigger whenever the owner asks \"what can you do,\" \"help me with my business,\" \"what should I focus on,\" \"I don't know where to start,\" or any open-ended business request that doesn't clearly match a single skill.","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fsmall-business\u002Fskills\u002Fsmb-router\u002FSKILL.md",[],[206,207,208,134,209,28,141,210,84,211,115,144,212,213,214,34,215,35,216,93],"router","smb","smb router","do","front","single","you","vague","can","plugin","focus"]