[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M718FZF9A92J9CVGWEEPBAZ3":3,"skill-same:01M718FZF9A92J9CVGWEEPBAZ3":70},{"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":37,"linkOnly":38,"markdownContent":39,"markdownBody":40,"frontMatter":41,"refName":42,"commitSha":43,"skillDirectory":44,"forksCount":45,"repositoryHtmlUrl":46,"repositoryHomepageUrl":6,"ownerHtmlUrl":47,"ownerBlogUrl":48,"ownerDescription":6,"updatedAt":49,"install":50,"resources":63},"01M718FZF9A92J9CVGWEEPBAZ3","contact-research",null,"Research a specific person using Common Room data. Triggers on 'who is [name]', 'look up [email]', 'research [contact]', 'is [name] a warm lead', or any contact-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\u002Fcontact-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],"contact research","research","email","up","triggers","[name]","question","common","name","person","data","look","lead","2026-11-10 10:12:30",false,"---\nname: contact-research\ndescription: \"Research a specific person using Common Room data. Triggers on 'who is [name]', 'look up [email]', 'research [contact]', 'is [name] a warm lead', or any contact-level question.\"\n---\n\n# Contact Research\n\nRetrieve a comprehensive contact profile from Common Room. Supports lookup by email, social handle, or name + company. Returns enriched data including activity history, Spark, scores, website visits, and CRM fields.\n\n## Step 1: Locate the Contact\n\nCommon Room supports multiple lookup methods — use whichever the user has provided:\n\n| What the user gives | Lookup method |\n|---------------------|--------------|\n| Email address | Look up by email (most reliable) |\n| LinkedIn, Twitter\u002FX, or GitHub handle | Look up by social handle — specify handle type explicitly |\n| Name + company | Identity resolution by name + org domain; present matches if ambiguous |\n| Name only | Search by name; if multiple matches, show a brief list and ask the user to confirm |\n\nIf no match is found, respond: \"Common Room doesn't have a record for this person.\" Do not speculate or fabricate profile data.\n\n## Step 2: Fetch Contact Fields\n\nUse the Common Room object catalog to see available field groups and their contents. For full profiles, request all 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- **Recent activity** — use `Contact Initiated` filter (last 60 days) for their actions, not your team's\n- **Website visits** — total count + specific pages (last 12 weeks)\n- **Spark** — retrieve all Sparks when tracking engagement evolution over time\n\n## Step 3: Run Spark Enrichment (If Available)\n\nIf Spark is available, use it. Spark provides:\n- Professional background and job history\n- Social presence and influence signals\n- Persona classification: Champion, Economic Buyer, Technical Evaluator, End User, or Gatekeeper\n- Inferred role in the buying process\n\nIf Spark is unavailable but real activity data exists (recent actions, website visits, community engagement), infer a persona from those signals. If neither Spark nor activity data is available, classify as Unknown — do not guess a persona from title alone.\n\nRetrieve **all Sparks** (not just the most recent) when the user wants to understand how this contact's engagement has evolved over time.\n\n## Step 4: Assess Account Context\n\nPull an abbreviated account snapshot for this contact's parent company. Note:\n- Open opportunities, expansion signals, or churn risk at the account level\n- Whether other contacts at this company are also active\n- How this person's engagement compares to their colleagues\n\n## Step 5: Identify Conversation Angles\n\nBased on activity and signals, surface the strongest 2–3 hooks:\n- A recent `Contact Initiated` activity (community post, product event, support ticket)\n- A specific web page they visited recently — especially if it signals evaluation intent\n- A job change, promotion, or company news\n- Their Spark persona and what that suggests about communication style\n- Their role in a known active deal\n\n## Output Format\n\nOnly include sections where data was actually returned. Omit sections with no data rather than filling them with guesses.\n\n**When data is rich:**\n\n```\n## [Contact Name] — Profile\n\n**Overview**\n[2 sentences: who they are, their role, and relationship status]\n\n**Details**\n- Title: [title]\n- Company: [company]\n- Email: [email]\n- LinkedIn: [URL]\n- Other profiles: [Twitter\u002FX, GitHub, CRM link if available]\n\n**Scores** [If scores returned]\n[All scores as raw values or percentiles]\n\n**Recent Activity** (last 60 days) [If activity returned]\n[3–5 bullets with dates]\n\n**Website Visits** (last 12 weeks) [If visit data exists]\n[Total visit count + list of pages visited]\n\n**Spark Profile** [If Spark data is non-null]\n[Persona type, background summary, influence signals]\n\n**Segments** [If segments returned]\n[List of segment names this contact belongs to]\n\n**Account Context**\n[1–2 sentences on their company's status]\n\n**Conversation Starters**\n[2–3 specific, signal-backed openers]\n```\n\n**When data is sparse (e.g., only name, title, email, tags returned; sparkSummary is null):**\n\n```\n## [Contact Name] — Profile (Limited Data)\n\n**Data available:** [List exactly what Common Room returned]\n\n[Present only the returned fields]\n\n**Web Search**\n[Any findings from searching their name + company]\n\n**Note:** Common Room has limited data on this contact. No activity history, scores, or Spark profile available. I can run deeper web searches or look up their company for additional context.\n```\n\nDo not generate conversation starters, persona inferences, or engagement assessments from sparse data. These require real signals.\n\n## Quality Standards\n\n- Lookup must use the correct method for the input type — don't guess on email vs. handle\n- Scores as raw\u002Fpercentile only — never labels\n- `Contact Initiated` activity (last 60 days) is the primary engagement signal — lead with it\n- If Spark is unavailable, say so — don't fabricate a persona from title alone\n- Flag any contact where the most recent activity is older than 30 days\n\n## Reference Files\n\n- **`references\u002Fcontact-signals-guide.md`** — full field descriptions, Spark persona guide, and conversation starter principles\n","# Contact Research\n\nRetrieve a comprehensive contact profile from Common Room. Supports lookup by email, social handle, or name + company. Returns enriched data including activity history, Spark, scores, website visits, and CRM fields.\n\n## Step 1: Locate the Contact\n\nCommon Room supports multiple lookup methods — use whichever the user has provided:\n\n| What the user gives | Lookup method |\n|---------------------|--------------|\n| Email address | Look up by email (most reliable) |\n| LinkedIn, Twitter\u002FX, or GitHub handle | Look up by social handle — specify handle type explicitly |\n| Name + company | Identity resolution by name + org domain; present matches if ambiguous |\n| Name only | Search by name; if multiple matches, show a brief list and ask the user to confirm |\n\nIf no match is found, respond: \"Common Room doesn't have a record for this person.\" Do not speculate or fabricate profile data.\n\n## Step 2: Fetch Contact Fields\n\nUse the Common Room object catalog to see available field groups and their contents. For full profiles, request all 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- **Recent activity** — use `Contact Initiated` filter (last 60 days) for their actions, not your team's\n- **Website visits** — total count + specific pages (last 12 weeks)\n- **Spark** — retrieve all Sparks when tracking engagement evolution over time\n\n## Step 3: Run Spark Enrichment (If Available)\n\nIf Spark is available, use it. Spark provides:\n- Professional background and job history\n- Social presence and influence signals\n- Persona classification: Champion, Economic Buyer, Technical Evaluator, End User, or Gatekeeper\n- Inferred role in the buying process\n\nIf Spark is unavailable but real activity data exists (recent actions, website visits, community engagement), infer a persona from those signals. If neither Spark nor activity data is available, classify as Unknown — do not guess a persona from title alone.\n\nRetrieve **all Sparks** (not just the most recent) when the user wants to understand how this contact's engagement has evolved over time.\n\n## Step 4: Assess Account Context\n\nPull an abbreviated account snapshot for this contact's parent company. Note:\n- Open opportunities, expansion signals, or churn risk at the account level\n- Whether other contacts at this company are also active\n- How this person's engagement compares to their colleagues\n\n## Step 5: Identify Conversation Angles\n\nBased on activity and signals, surface the strongest 2–3 hooks:\n- A recent `Contact Initiated` activity (community post, product event, support ticket)\n- A specific web page they visited recently — especially if it signals evaluation intent\n- A job change, promotion, or company news\n- Their Spark persona and what that suggests about communication style\n- Their role in a known active deal\n\n## Output Format\n\nOnly include sections where data was actually returned. Omit sections with no data rather than filling them with guesses.\n\n**When data is rich:**\n\n```\n## [Contact Name] — Profile\n\n**Overview**\n[2 sentences: who they are, their role, and relationship status]\n\n**Details**\n- Title: [title]\n- Company: [company]\n- Email: [email]\n- LinkedIn: [URL]\n- Other profiles: [Twitter\u002FX, GitHub, CRM link if available]\n\n**Scores** [If scores returned]\n[All scores as raw values or percentiles]\n\n**Recent Activity** (last 60 days) [If activity returned]\n[3–5 bullets with dates]\n\n**Website Visits** (last 12 weeks) [If visit data exists]\n[Total visit count + list of pages visited]\n\n**Spark Profile** [If Spark data is non-null]\n[Persona type, background summary, influence signals]\n\n**Segments** [If segments returned]\n[List of segment names this contact belongs to]\n\n**Account Context**\n[1–2 sentences on their company's status]\n\n**Conversation Starters**\n[2–3 specific, signal-backed openers]\n```\n\n**When data is sparse (e.g., only name, title, email, tags returned; sparkSummary is null):**\n\n```\n## [Contact Name] — Profile (Limited Data)\n\n**Data available:** [List exactly what Common Room returned]\n\n[Present only the returned fields]\n\n**Web Search**\n[Any findings from searching their name + company]\n\n**Note:** Common Room has limited data on this contact. No activity history, scores, or Spark profile available. I can run deeper web searches or look up their company for additional context.\n```\n\nDo not generate conversation starters, persona inferences, or engagement assessments from sparse data. These require real signals.\n\n## Quality Standards\n\n- Lookup must use the correct method for the input type — don't guess on email vs. handle\n- Scores as raw\u002Fpercentile only — never labels\n- `Contact Initiated` activity (last 60 days) is the primary engagement signal — lead with it\n- If Spark is unavailable, say so — don't fabricate a persona from title alone\n- Flag any contact where the most recent activity is older than 30 days\n\n## Reference Files\n\n- **`references\u002Fcontact-signals-guide.md`** — full field descriptions, Spark persona guide, and conversation starter principles\n",{"name":5,"description":7},"main","e605d8ddbfb688da57dfaaceebd93f12a64ef1d3","partner-built\u002Fcommon-room\u002Fskills\u002Fcontact-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":51,"prompt":61,"zipAvailable":62},[52,55,58],{"tool":53,"command":54},"npx","npx skills add anthropics\u002Fknowledge-work-plugins --skill contact-research",{"tool":56,"command":57},"bunx","bunx skills add anthropics\u002Fknowledge-work-plugins --skill contact-research",{"tool":59,"command":60},"pnpm","pnpm dlx skills add anthropics\u002Fknowledge-work-plugins --skill contact-research","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fpartner-built\u002Fcommon-room\u002Fskills\u002Fcontact-research\u002FSKILL.md",true,[64],{"path":65,"resourceType":66,"resourceTypeDesc":67,"sizeBytes":68,"sourceHtmlUrl":69},"references\u002Fcontact-signals-guide.md","REFERENCE","参考文档",2396,"https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Fblob\u002Fmain\u002Fpartner-built\u002Fcommon-room\u002Fskills\u002Fcontact-research\u002Freferences\u002Fcontact-signals-guide.md",[71,97,122,144,172,198],{"skillNo":72,"name":73,"nameZh":6,"description":74,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":75,"categories":76,"keywords":77,"publishedAt":37},"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",[],[78,79,80,27,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96],"ticket","ticket deflector","mode","payments","open","writing","when","workflow","ask","Connector","text","mail","window","next","customer","support","crm","past","read",{"skillNo":98,"name":99,"nameZh":6,"description":100,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":101,"categories":102,"keywords":103,"publishedAt":37},"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",[],[104,105,106,107,108,109,110,111,112,113,84,114,115,116,117,118,96,119,120,121],"tax season organizer","tax","us federal","which","netsuite","paid","end","usd","only","now","books","xero","scanning","run","request","flags","list","stripe",{"skillNo":123,"name":124,"nameZh":6,"description":125,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":126,"categories":127,"keywords":128,"publishedAt":37},"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",[],[129,105,130,131,106,132,133,108,134,135,136,137,138,139,140,110,141,30,142,143],"tax prep","prep","SET","as","ready","never","zoho books","get","chain","profit","for","trigger","material","paypal","business",{"skillNo":145,"name":146,"nameZh":6,"description":147,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":148,"categories":149,"keywords":155,"publishedAt":37},"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",[150],{"categoryNo":151,"categoryName":152,"taxonomyType":153,"taxonomyTypeDesc":154},"01JBSEEDCAT000000000000034","行政与办公人员","PROFESSION","适用职业",[156,36,157,158,159,160,134,161,162,163,164,165,166,167,84,168,169,87,170,171],"speed to lead","speed","shared","back","cold","time","inbox","send","respond","new","web forms","people","schedule","logs","sure","human",{"skillNo":173,"name":174,"nameZh":6,"description":175,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":176,"categories":177,"keywords":181,"publishedAt":37},"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",[178],{"categoryNo":179,"categoryName":180,"taxonomyType":153,"taxonomyTypeDesc":154},"01JBSEEDCAT000000000000036","内容创作者",[182,183,184,185,186,187,131,188,189,190,191,27,192,84,193,194,195,96,196,197],"social","social content engine","engine","content engine","social content","content","brief","posts","Captions","prices","campaign","me","product","calendar","write","drafts",{"skillNo":199,"name":200,"nameZh":6,"description":201,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":202,"categories":203,"keywords":204,"publishedAt":37},"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",[],[205,206,207,132,208,139,140,209,82,210,113,143,211,212,213,214,215,193,216,91],"router","smb","smb router","do","front","single","you","vague","can","with","plugin","focus"]