[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-anthropic-data-context-extractor":3,"mdc-4800p2-key":34,"related-org-anthropic-data-context-extractor":1144,"related-repo-anthropic-data-context-extractor":1331},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":23,"repoUrl":24,"updatedAt":25,"license":26,"forks":27,"topics":28,"repo":29,"sourceUrl":32,"mdContent":33},"data-context-extractor","bootstrap a company data analysis skill","Generate or improve a company-specific data analysis skill by extracting tribal knowledge from analysts.\nBOOTSTRAP MODE - Triggers: \"Create a data context skill\", \"Set up data analysis for our warehouse\", \"Help me create a skill for our database\", \"Generate a data skill for [company]\" → Discovers schemas, asks key questions, generates initial skill with reference files\nITERATION MODE - Triggers: \"Add context about [domain]\", \"The skill needs more info about [topic]\", \"Update the data skill with [metrics\u002Ftables\u002Fterminology]\", \"Improve the [domain] reference\" → Loads existing skill, asks targeted questions, appends\u002Fupdates reference files\nUse when data analysts want Claude to understand their company's specific data warehouse, terminology, metrics definitions, and common query patterns.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},"anthropic","Anthropic","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fanthropic.png","anthropics",[13,17,20],{"name":14,"slug":15,"type":16},"Documentation","documentation","tag",{"name":18,"slug":19,"type":16},"Data Analysis","data-analysis",{"name":21,"slug":22,"type":16},"Analytics","analytics",22885,"https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins","2026-04-06T17:57:20.351542",null,2736,[],{"repoUrl":24,"stars":23,"forks":27,"topics":30,"description":31},[],"Open source repository of plugins primarily intended for knowledge workers to use in Claude Cowork","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fknowledge-work-plugins\u002Ftree\u002FHEAD\u002Fdata\u002Fskills\u002Fdata-context-extractor","---\nname: data-context-extractor\ndescription: >\n  Generate or improve a company-specific data analysis skill by extracting tribal knowledge from analysts.\n\n  BOOTSTRAP MODE - Triggers: \"Create a data context skill\", \"Set up data analysis for our warehouse\",\n  \"Help me create a skill for our database\", \"Generate a data skill for [company]\"\n  → Discovers schemas, asks key questions, generates initial skill with reference files\n\n  ITERATION MODE - Triggers: \"Add context about [domain]\", \"The skill needs more info about [topic]\",\n  \"Update the data skill with [metrics\u002Ftables\u002Fterminology]\", \"Improve the [domain] reference\"\n  → Loads existing skill, asks targeted questions, appends\u002Fupdates reference files\n\n  Use when data analysts want Claude to understand their company's specific data warehouse,\n  terminology, metrics definitions, and common query patterns.\n---\n\n# Data Context Extractor\n\nA meta-skill that extracts company-specific data knowledge from analysts and generates tailored data analysis skills.\n\n## How It Works\n\nThis skill has two modes:\n\n1. **Bootstrap Mode**: Create a new data analysis skill from scratch\n2. **Iteration Mode**: Improve an existing skill by adding domain-specific reference files\n\n---\n\n## Bootstrap Mode\n\nUse when: User wants to create a new data context skill for their warehouse.\n\n### Phase 1: Database Connection & Discovery\n\n**Step 1: Identify the database type**\n\nAsk: \"What data warehouse are you using?\"\n\nCommon options:\n- **BigQuery**\n- **Snowflake**\n- **PostgreSQL\u002FRedshift**\n- **Databricks**\n\nUse `~~data warehouse` tools (query and schema) to connect. If unclear, check available MCP tools in the current session.\n\n**Step 2: Explore the schema**\n\nUse `~~data warehouse` schema tools to:\n1. List available datasets\u002Fschemas\n2. Identify the most important tables (ask user: \"Which 3-5 tables do analysts query most often?\")\n3. Pull schema details for those key tables\n\nSample exploration queries by dialect:\n```sql\n-- BigQuery: List datasets\nSELECT schema_name FROM INFORMATION_SCHEMA.SCHEMATA\n\n-- BigQuery: List tables in a dataset\nSELECT table_name FROM `project.dataset.INFORMATION_SCHEMA.TABLES`\n\n-- Snowflake: List schemas\nSHOW SCHEMAS IN DATABASE my_database\n\n-- Snowflake: List tables\nSHOW TABLES IN SCHEMA my_schema\n```\n\n### Phase 2: Core Questions (Ask These)\n\nAfter schema discovery, ask these questions conversationally (not all at once):\n\n**Entity Disambiguation (Critical)**\n> \"When people here say 'user' or 'customer', what exactly do they mean? Are there different types?\"\n\nListen for:\n- Multiple entity types (user vs account vs organization)\n- Relationships between them (1:1, 1:many, many:many)\n- Which ID fields link them together\n\n**Primary Identifiers**\n> \"What's the main identifier for a [customer\u002Fuser\u002Faccount]? Are there multiple IDs for the same entity?\"\n\nListen for:\n- Primary keys vs business keys\n- UUID vs integer IDs\n- Legacy ID systems\n\n**Key Metrics**\n> \"What are the 2-3 metrics people ask about most? How is each one calculated?\"\n\nListen for:\n- Exact formulas (ARR = monthly_revenue × 12)\n- Which tables\u002Fcolumns feed each metric\n- Time period conventions (trailing 7 days, calendar month, etc.)\n\n**Data Hygiene**\n> \"What should ALWAYS be filtered out of queries? (test data, fraud, internal users, etc.)\"\n\nListen for:\n- Standard WHERE clauses to always include\n- Flag columns that indicate exclusions (is_test, is_internal, is_fraud)\n- Specific values to exclude (status = 'deleted')\n\n**Common Gotchas**\n> \"What mistakes do new analysts typically make with this data?\"\n\nListen for:\n- Confusing column names\n- Timezone issues\n- NULL handling quirks\n- Historical vs current state tables\n\n### Phase 3: Generate the Skill\n\nCreate a skill with this structure:\n\n```\n[company]-data-analyst\u002F\n├── SKILL.md\n└── references\u002F\n    ├── entities.md          # Entity definitions and relationships\n    ├── metrics.md           # KPI calculations\n    ├── tables\u002F              # One file per domain\n    │   ├── [domain1].md\n    │   └── [domain2].md\n    └── dashboards.json      # Optional: existing dashboards catalog\n```\n\n**SKILL.md Template**: See `references\u002Fskill-template.md`\n\n**SQL Dialect Section**: See `references\u002Fsql-dialects.md` and include the appropriate dialect notes.\n\n**Reference File Template**: See `references\u002Fdomain-template.md`\n\n### Phase 4: Package and Deliver\n\n1. Create all files in the skill directory\n2. Package as a zip file\n3. Present to user with summary of what was captured\n\n---\n\n## Iteration Mode\n\nUse when: User has an existing skill but needs to add more context.\n\n### Step 1: Load Existing Skill\n\nAsk user to upload their existing skill (zip or folder), or locate it if already in the session.\n\nRead the current SKILL.md and reference files to understand what's already documented.\n\n### Step 2: Identify the Gap\n\nAsk: \"What domain or topic needs more context? What queries are failing or producing wrong results?\"\n\nCommon gaps:\n- A new data domain (marketing, finance, product, etc.)\n- Missing metric definitions\n- Undocumented table relationships\n- New terminology\n\n### Step 3: Targeted Discovery\n\nFor the identified domain:\n\n1. **Explore relevant tables**: Use `~~data warehouse` schema tools to find tables in that domain\n2. **Ask domain-specific questions**:\n   - \"What tables are used for [domain] analysis?\"\n   - \"What are the key metrics for [domain]?\"\n   - \"Any special filters or gotchas for [domain] data?\"\n\n3. **Generate new reference file**: Create `references\u002F[domain].md` using the domain template\n\n### Step 4: Update and Repackage\n\n1. Add the new reference file\n2. Update SKILL.md's \"Knowledge Base Navigation\" section to include the new domain\n3. Repackage the skill\n4. Present the updated skill to user\n\n---\n\n## Reference File Standards\n\nEach reference file should include:\n\n### For Table Documentation\n- **Location**: Full table path\n- **Description**: What this table contains, when to use it\n- **Primary Key**: How to uniquely identify rows\n- **Update Frequency**: How often data refreshes\n- **Key Columns**: Table with column name, type, description, notes\n- **Relationships**: How this table joins to others\n- **Sample Queries**: 2-3 common query patterns\n\n### For Metrics Documentation\n- **Metric Name**: Human-readable name\n- **Definition**: Plain English explanation\n- **Formula**: Exact calculation with column references\n- **Source Table(s)**: Where the data comes from\n- **Caveats**: Edge cases, exclusions, gotchas\n\n### For Entity Documentation\n- **Entity Name**: What it's called\n- **Definition**: What it represents in the business\n- **Primary Table**: Where to find this entity\n- **ID Field(s)**: How to identify it\n- **Relationships**: How it relates to other entities\n- **Common Filters**: Standard exclusions (internal, test, etc.)\n\n---\n\n## Quality Checklist\n\nBefore delivering a generated skill, verify:\n\n- [ ] SKILL.md has complete frontmatter (name, description)\n- [ ] Entity disambiguation section is clear\n- [ ] Key terminology is defined\n- [ ] Standard filters\u002Fexclusions are documented\n- [ ] At least 2-3 sample queries per domain\n- [ ] SQL uses correct dialect syntax\n- [ ] Reference files are linked from SKILL.md navigation section\n",{"data":35,"body":36},{"name":4,"description":6},{"type":37,"children":38},"root",[39,47,53,60,65,91,95,100,105,112,120,125,130,166,180,188,199,217,222,333,339,344,352,361,366,384,392,407,411,429,437,445,449,467,475,483,487,505,513,521,525,548,554,559,569,585,602,617,623,641,644,649,654,660,665,670,676,681,686,709,715,720,805,811,834,837,843,848,854,927,933,986,992,1053,1056,1062,1067,1138],{"type":40,"tag":41,"props":42,"children":43},"element","h1",{"id":4},[44],{"type":45,"value":46},"text","Data Context Extractor",{"type":40,"tag":48,"props":49,"children":50},"p",{},[51],{"type":45,"value":52},"A meta-skill that extracts company-specific data knowledge from analysts and generates tailored data analysis skills.",{"type":40,"tag":54,"props":55,"children":57},"h2",{"id":56},"how-it-works",[58],{"type":45,"value":59},"How It Works",{"type":40,"tag":48,"props":61,"children":62},{},[63],{"type":45,"value":64},"This skill has two modes:",{"type":40,"tag":66,"props":67,"children":68},"ol",{},[69,81],{"type":40,"tag":70,"props":71,"children":72},"li",{},[73,79],{"type":40,"tag":74,"props":75,"children":76},"strong",{},[77],{"type":45,"value":78},"Bootstrap Mode",{"type":45,"value":80},": Create a new data analysis skill from scratch",{"type":40,"tag":70,"props":82,"children":83},{},[84,89],{"type":40,"tag":74,"props":85,"children":86},{},[87],{"type":45,"value":88},"Iteration Mode",{"type":45,"value":90},": Improve an existing skill by adding domain-specific reference files",{"type":40,"tag":92,"props":93,"children":94},"hr",{},[],{"type":40,"tag":54,"props":96,"children":98},{"id":97},"bootstrap-mode",[99],{"type":45,"value":78},{"type":40,"tag":48,"props":101,"children":102},{},[103],{"type":45,"value":104},"Use when: User wants to create a new data context skill for their warehouse.",{"type":40,"tag":106,"props":107,"children":109},"h3",{"id":108},"phase-1-database-connection-discovery",[110],{"type":45,"value":111},"Phase 1: Database Connection & Discovery",{"type":40,"tag":48,"props":113,"children":114},{},[115],{"type":40,"tag":74,"props":116,"children":117},{},[118],{"type":45,"value":119},"Step 1: Identify the database type",{"type":40,"tag":48,"props":121,"children":122},{},[123],{"type":45,"value":124},"Ask: \"What data warehouse are you using?\"",{"type":40,"tag":48,"props":126,"children":127},{},[128],{"type":45,"value":129},"Common options:",{"type":40,"tag":131,"props":132,"children":133},"ul",{},[134,142,150,158],{"type":40,"tag":70,"props":135,"children":136},{},[137],{"type":40,"tag":74,"props":138,"children":139},{},[140],{"type":45,"value":141},"BigQuery",{"type":40,"tag":70,"props":143,"children":144},{},[145],{"type":40,"tag":74,"props":146,"children":147},{},[148],{"type":45,"value":149},"Snowflake",{"type":40,"tag":70,"props":151,"children":152},{},[153],{"type":40,"tag":74,"props":154,"children":155},{},[156],{"type":45,"value":157},"PostgreSQL\u002FRedshift",{"type":40,"tag":70,"props":159,"children":160},{},[161],{"type":40,"tag":74,"props":162,"children":163},{},[164],{"type":45,"value":165},"Databricks",{"type":40,"tag":48,"props":167,"children":168},{},[169,171,178],{"type":45,"value":170},"Use ",{"type":40,"tag":172,"props":173,"children":175},"code",{"className":174},[],[176],{"type":45,"value":177},"~~data warehouse",{"type":45,"value":179}," tools (query and schema) to connect. If unclear, check available MCP tools in the current session.",{"type":40,"tag":48,"props":181,"children":182},{},[183],{"type":40,"tag":74,"props":184,"children":185},{},[186],{"type":45,"value":187},"Step 2: Explore the schema",{"type":40,"tag":48,"props":189,"children":190},{},[191,192,197],{"type":45,"value":170},{"type":40,"tag":172,"props":193,"children":195},{"className":194},[],[196],{"type":45,"value":177},{"type":45,"value":198}," schema tools to:",{"type":40,"tag":66,"props":200,"children":201},{},[202,207,212],{"type":40,"tag":70,"props":203,"children":204},{},[205],{"type":45,"value":206},"List available datasets\u002Fschemas",{"type":40,"tag":70,"props":208,"children":209},{},[210],{"type":45,"value":211},"Identify the most important tables (ask user: \"Which 3-5 tables do analysts query most often?\")",{"type":40,"tag":70,"props":213,"children":214},{},[215],{"type":45,"value":216},"Pull schema details for those key tables",{"type":40,"tag":48,"props":218,"children":219},{},[220],{"type":45,"value":221},"Sample exploration queries by dialect:",{"type":40,"tag":223,"props":224,"children":229},"pre",{"className":225,"code":226,"language":227,"meta":228,"style":228},"language-sql shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","-- BigQuery: List datasets\nSELECT schema_name FROM INFORMATION_SCHEMA.SCHEMATA\n\n-- BigQuery: List tables in a dataset\nSELECT table_name FROM `project.dataset.INFORMATION_SCHEMA.TABLES`\n\n-- Snowflake: List schemas\nSHOW SCHEMAS IN DATABASE my_database\n\n-- Snowflake: List tables\nSHOW TABLES IN SCHEMA my_schema\n","sql","",[230],{"type":40,"tag":172,"props":231,"children":232},{"__ignoreMap":228},[233,244,253,263,272,281,289,298,307,315,324],{"type":40,"tag":234,"props":235,"children":238},"span",{"class":236,"line":237},"line",1,[239],{"type":40,"tag":234,"props":240,"children":241},{},[242],{"type":45,"value":243},"-- BigQuery: List datasets\n",{"type":40,"tag":234,"props":245,"children":247},{"class":236,"line":246},2,[248],{"type":40,"tag":234,"props":249,"children":250},{},[251],{"type":45,"value":252},"SELECT schema_name FROM INFORMATION_SCHEMA.SCHEMATA\n",{"type":40,"tag":234,"props":254,"children":256},{"class":236,"line":255},3,[257],{"type":40,"tag":234,"props":258,"children":260},{"emptyLinePlaceholder":259},true,[261],{"type":45,"value":262},"\n",{"type":40,"tag":234,"props":264,"children":266},{"class":236,"line":265},4,[267],{"type":40,"tag":234,"props":268,"children":269},{},[270],{"type":45,"value":271},"-- BigQuery: List tables in a dataset\n",{"type":40,"tag":234,"props":273,"children":275},{"class":236,"line":274},5,[276],{"type":40,"tag":234,"props":277,"children":278},{},[279],{"type":45,"value":280},"SELECT table_name FROM `project.dataset.INFORMATION_SCHEMA.TABLES`\n",{"type":40,"tag":234,"props":282,"children":284},{"class":236,"line":283},6,[285],{"type":40,"tag":234,"props":286,"children":287},{"emptyLinePlaceholder":259},[288],{"type":45,"value":262},{"type":40,"tag":234,"props":290,"children":292},{"class":236,"line":291},7,[293],{"type":40,"tag":234,"props":294,"children":295},{},[296],{"type":45,"value":297},"-- Snowflake: List schemas\n",{"type":40,"tag":234,"props":299,"children":301},{"class":236,"line":300},8,[302],{"type":40,"tag":234,"props":303,"children":304},{},[305],{"type":45,"value":306},"SHOW SCHEMAS IN DATABASE my_database\n",{"type":40,"tag":234,"props":308,"children":310},{"class":236,"line":309},9,[311],{"type":40,"tag":234,"props":312,"children":313},{"emptyLinePlaceholder":259},[314],{"type":45,"value":262},{"type":40,"tag":234,"props":316,"children":318},{"class":236,"line":317},10,[319],{"type":40,"tag":234,"props":320,"children":321},{},[322],{"type":45,"value":323},"-- Snowflake: List tables\n",{"type":40,"tag":234,"props":325,"children":327},{"class":236,"line":326},11,[328],{"type":40,"tag":234,"props":329,"children":330},{},[331],{"type":45,"value":332},"SHOW TABLES IN SCHEMA my_schema\n",{"type":40,"tag":106,"props":334,"children":336},{"id":335},"phase-2-core-questions-ask-these",[337],{"type":45,"value":338},"Phase 2: Core Questions (Ask These)",{"type":40,"tag":48,"props":340,"children":341},{},[342],{"type":45,"value":343},"After schema discovery, ask these questions conversationally (not all at once):",{"type":40,"tag":48,"props":345,"children":346},{},[347],{"type":40,"tag":74,"props":348,"children":349},{},[350],{"type":45,"value":351},"Entity Disambiguation (Critical)",{"type":40,"tag":353,"props":354,"children":355},"blockquote",{},[356],{"type":40,"tag":48,"props":357,"children":358},{},[359],{"type":45,"value":360},"\"When people here say 'user' or 'customer', what exactly do they mean? Are there different types?\"",{"type":40,"tag":48,"props":362,"children":363},{},[364],{"type":45,"value":365},"Listen for:",{"type":40,"tag":131,"props":367,"children":368},{},[369,374,379],{"type":40,"tag":70,"props":370,"children":371},{},[372],{"type":45,"value":373},"Multiple entity types (user vs account vs organization)",{"type":40,"tag":70,"props":375,"children":376},{},[377],{"type":45,"value":378},"Relationships between them (1:1, 1:many, many:many)",{"type":40,"tag":70,"props":380,"children":381},{},[382],{"type":45,"value":383},"Which ID fields link them together",{"type":40,"tag":48,"props":385,"children":386},{},[387],{"type":40,"tag":74,"props":388,"children":389},{},[390],{"type":45,"value":391},"Primary Identifiers",{"type":40,"tag":353,"props":393,"children":394},{},[395],{"type":40,"tag":48,"props":396,"children":397},{},[398,400,405],{"type":45,"value":399},"\"What's the main identifier for a ",{"type":40,"tag":234,"props":401,"children":402},{},[403],{"type":45,"value":404},"customer\u002Fuser\u002Faccount",{"type":45,"value":406},"? Are there multiple IDs for the same entity?\"",{"type":40,"tag":48,"props":408,"children":409},{},[410],{"type":45,"value":365},{"type":40,"tag":131,"props":412,"children":413},{},[414,419,424],{"type":40,"tag":70,"props":415,"children":416},{},[417],{"type":45,"value":418},"Primary keys vs business keys",{"type":40,"tag":70,"props":420,"children":421},{},[422],{"type":45,"value":423},"UUID vs integer IDs",{"type":40,"tag":70,"props":425,"children":426},{},[427],{"type":45,"value":428},"Legacy ID systems",{"type":40,"tag":48,"props":430,"children":431},{},[432],{"type":40,"tag":74,"props":433,"children":434},{},[435],{"type":45,"value":436},"Key Metrics",{"type":40,"tag":353,"props":438,"children":439},{},[440],{"type":40,"tag":48,"props":441,"children":442},{},[443],{"type":45,"value":444},"\"What are the 2-3 metrics people ask about most? 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Create original visual designs, never copying existing artists' work to avoid copyright violations.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1187,1188,1189],{"name":1153,"slug":1154,"type":16},{"name":1156,"slug":1157,"type":16},{"name":1190,"slug":1191,"type":16},"PDF","pdf","2026-04-06T17:56:03.794732",{"slug":1194,"name":1194,"fn":1195,"description":1196,"org":1197,"tags":1198,"stars":1164,"repoUrl":1165,"updatedAt":1211},"claude-api","build apps with the Claude API","Reference for the Claude API \u002F Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration.\nTRIGGER — read BEFORE opening the target file; don't skip because it \"looks like a one-liner\" — whenever: the prompt names Claude\u002FAnthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing\u002Fmodel choice\u002Flimits\u002Fcaching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent\u002FMCP\u002Ftool-definition\u002Fmulti-agent\u002FRAG\u002FLLM-judge\u002Fcomputer-use; generate\u002Fsummarize\u002Fextract\u002Fclassify\u002Frewrite\u002Fconverse over NL; debugging refusals\u002Fcutoffs\u002Fstreaming\u002Ftool-calls\u002Ftokens).\nSKIP only when another provider is being worked on (overrides all triggers): OpenAI\u002FGPT\u002FGemini\u002FLlama\u002FMistral\u002FCohere\u002FOllama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1199,1202,1203,1206,1208],{"name":1200,"slug":1201,"type":16},"Agents","agents",{"name":9,"slug":8,"type":16},{"name":1204,"slug":1205,"type":16},"Anthropic SDK","anthropic-sdk",{"name":1207,"slug":1194,"type":16},"Claude API",{"name":1209,"slug":1210,"type":16},"LLM","llm","2026-07-28T05:36:08.213335",{"slug":1213,"name":1213,"fn":1214,"description":1215,"org":1216,"tags":1217,"stars":1164,"repoUrl":1165,"updatedAt":1222},"doc-coauthoring","co-author documentation and technical specs","Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1218,1219],{"name":14,"slug":15,"type":16},{"name":1220,"slug":1221,"type":16},"Technical Writing","technical-writing","2026-04-06T17:56:14.18897",{"slug":1224,"name":1224,"fn":1225,"description":1226,"org":1227,"tags":1228,"stars":1164,"repoUrl":1165,"updatedAt":1243},"docx","create and edit Word documents","Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files) or Word templates (.dotx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', '.dotx', or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx or .dotx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1229,1232,1234,1237,1240],{"name":1230,"slug":1231,"type":16},"Documents","documents",{"name":1233,"slug":1224,"type":16},"DOCX",{"name":1235,"slug":1236,"type":16},"Office","office",{"name":1238,"slug":1239,"type":16},"Templates","templates",{"name":1241,"slug":1242,"type":16},"Word","word","2026-07-18T05:16:23.136271",{"slug":1245,"name":1245,"fn":1246,"description":1247,"org":1248,"tags":1249,"stars":1164,"repoUrl":1165,"updatedAt":1263},"frontend-design","design production-grade frontend interfaces","Guidance for distinctive, intentional visual design when building new UI or reshaping an existing one. Helps with aesthetic direction, typography, and making choices that don't read as templated defaults.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1250,1251,1254,1257,1260],{"name":1156,"slug":1157,"type":16},{"name":1252,"slug":1253,"type":16},"Frontend","frontend",{"name":1255,"slug":1256,"type":16},"React","react",{"name":1258,"slug":1259,"type":16},"Tailwind CSS","tailwind-css",{"name":1261,"slug":1262,"type":16},"UI Components","ui-components","2026-04-06T17:56:16.723469",{"slug":1265,"name":1265,"fn":1266,"description":1267,"org":1268,"tags":1269,"stars":1164,"repoUrl":1165,"updatedAt":1277},"internal-comms","write internal company communications","A set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever asked to write some sort of internal communications (status reports, leadership updates, 3P updates, company newsletters, FAQs, incident reports, project updates, etc.).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1270,1273,1274],{"name":1271,"slug":1272,"type":16},"Communications","communications",{"name":1238,"slug":1239,"type":16},{"name":1275,"slug":1276,"type":16},"Writing","writing","2026-04-06T17:56:20.695522",{"slug":1279,"name":1279,"fn":1280,"description":1281,"org":1282,"tags":1283,"stars":1164,"repoUrl":1165,"updatedAt":1292},"mcp-builder","build MCP servers","Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node\u002FTypeScript (MCP SDK).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1284,1285,1288,1289],{"name":1200,"slug":1201,"type":16},{"name":1286,"slug":1287,"type":16},"API Development","api-development",{"name":1209,"slug":1210,"type":16},{"name":1290,"slug":1291,"type":16},"MCP","mcp","2026-04-06T17:56:10.357665",{"slug":1191,"name":1191,"fn":1294,"description":1295,"org":1296,"tags":1297,"stars":1164,"repoUrl":1165,"updatedAt":1300},"read edit and manipulate PDF files","Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text\u002Ftables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting\u002Fdecrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1298,1299],{"name":1230,"slug":1231,"type":16},{"name":1190,"slug":1191,"type":16},"2026-04-06T17:56:02.483316",{"slug":1302,"name":1302,"fn":1303,"description":1304,"org":1305,"tags":1306,"stars":1164,"repoUrl":1165,"updatedAt":1313},"pptx","create and edit PowerPoint presentations","Use this skill any time a .pptx or .potx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx or .potx file (even if the extracted content will be used elsewhere, like in an email or summary); editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates (.potx), layouts, speaker notes, or comments. Trigger whenever the user mentions \"deck,\" \"slides,\" \"presentation,\" or references a .pptx or .potx filename, regardless of what they plan to do with the content afterward. If a .pptx or .potx file needs to be opened, created, or touched, use this skill.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1307,1310],{"name":1308,"slug":1309,"type":16},"PowerPoint","powerpoint",{"name":1311,"slug":1312,"type":16},"Presentations","presentations","2026-07-18T05:16:24.1471",{"slug":1315,"name":1315,"fn":1316,"description":1317,"org":1318,"tags":1319,"stars":1164,"repoUrl":1165,"updatedAt":1329},"skill-creator","create and optimize agent skills","Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1320,1321,1322,1325,1328],{"name":1200,"slug":1201,"type":16},{"name":14,"slug":15,"type":16},{"name":1323,"slug":1324,"type":16},"Evals","evals",{"name":1326,"slug":1327,"type":16},"Performance","performance",{"name":1220,"slug":1221,"type":16},"2026-04-19T06:45:40.804",490,{"items":1332,"total":1434},[1333,1347,1363,1374,1390,1409,1421],{"slug":1334,"name":1334,"fn":1335,"description":1336,"org":1337,"tags":1338,"stars":23,"repoUrl":24,"updatedAt":1346},"accessibility-review","run WCAG accessibility audits","Run a WCAG 2.1 AA accessibility audit on a design or page. Trigger with \"audit accessibility\", \"check a11y\", \"is this accessible?\", or when reviewing a design for color contrast, keyboard navigation, touch target size, or screen reader behavior before handoff.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1339,1342,1343],{"name":1340,"slug":1341,"type":16},"Accessibility","accessibility",{"name":1156,"slug":1157,"type":16},{"name":1344,"slug":1345,"type":16},"WCAG","wcag","2026-04-06T17:58:05.682394",{"slug":1348,"name":1348,"fn":1349,"description":1350,"org":1351,"tags":1352,"stars":23,"repoUrl":24,"updatedAt":1362},"account-research","research accounts for sales intel","Research a company or person and get actionable sales intel. Works standalone with web search, supercharged when you connect enrichment tools or your CRM. Trigger with \"research [company]\", \"look up [person]\", \"intel on [prospect]\", \"who is [name] at [company]\", or \"tell me about [company]\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1353,1356,1359],{"name":1354,"slug":1355,"type":16},"CRM","crm",{"name":1357,"slug":1358,"type":16},"Research","research",{"name":1360,"slug":1361,"type":16},"Sales","sales","2026-04-06T17:56:41.410418",{"slug":1364,"name":1364,"fn":1365,"description":1366,"org":1367,"tags":1368,"stars":23,"repoUrl":24,"updatedAt":1373},"analyze","answer data questions and run analyses","Answer data questions -- from quick lookups to full analyses. Use when looking up a single metric, investigating what's driving a trend or drop, comparing segments over time, or preparing a formal data report for stakeholders.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1369,1370,1371],{"name":21,"slug":22,"type":16},{"name":18,"slug":19,"type":16},{"name":1372,"slug":227,"type":16},"SQL","2026-04-06T17:57:21.593647",{"slug":1375,"name":1375,"fn":1376,"description":1377,"org":1378,"tags":1379,"stars":23,"repoUrl":24,"updatedAt":1389},"architecture","create and evaluate architecture decision records","Create or evaluate an architecture decision record (ADR). Use when choosing between technologies (e.g., Kafka vs SQS), documenting a design decision with trade-offs and consequences, reviewing a system design proposal, or designing a new component from requirements and constraints.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1380,1383,1385,1386],{"name":1381,"slug":1382,"type":16},"ADR","adr",{"name":1384,"slug":1375,"type":16},"Architecture",{"name":14,"slug":15,"type":16},{"name":1387,"slug":1388,"type":16},"Engineering","engineering","2026-04-06T17:57:49.26444",{"slug":1391,"name":1391,"fn":1392,"description":1393,"org":1394,"tags":1395,"stars":23,"repoUrl":24,"updatedAt":1408},"audit-support","support SOX 404 control testing","Support SOX 404 compliance with control testing methodology, sample selection, and documentation standards. Use when generating testing workpapers, selecting audit samples, classifying control deficiencies, or preparing for internal or external audits.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1396,1399,1402,1405],{"name":1397,"slug":1398,"type":16},"Audit","audit",{"name":1400,"slug":1401,"type":16},"Finance","finance",{"name":1403,"slug":1404,"type":16},"Regulatory Compliance","regulatory-compliance",{"name":1406,"slug":1407,"type":16},"SOX","sox","2026-04-06T17:57:36.714815",{"slug":1410,"name":1410,"fn":1411,"description":1412,"org":1413,"tags":1414,"stars":23,"repoUrl":24,"updatedAt":1420},"brand-review","review content against brand voice","Review content against your brand voice, style guide, and messaging pillars, flagging deviations by severity with specific before\u002Fafter fixes. Use when checking a draft before it ships, when auditing copy for voice consistency and terminology, or when screening for unsubstantiated claims, missing disclaimers, and other legal flags.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1415,1416,1419],{"name":1174,"slug":1175,"type":16},{"name":1417,"slug":1418,"type":16},"Marketing","marketing",{"name":1275,"slug":1276,"type":16},"2026-04-06T17:58:19.548331",{"slug":1422,"name":1422,"fn":1423,"description":1424,"org":1425,"tags":1426,"stars":23,"repoUrl":24,"updatedAt":1433},"brand-voice-enforcement","enforce brand voice in content","This skill applies brand guidelines to content creation. It should be used when the user asks to \"write an email\", \"draft a proposal\", \"create a pitch deck\", \"write a LinkedIn post\", \"draft a presentation\", \"write a Slack message\", \"draft sales content\", or any content creation request where brand voice should be applied. Also triggers on \"on-brand\", \"brand voice\", \"enforce voice\", \"apply brand guidelines\", \"brand-aligned content\", \"write in our voice\", \"use our brand tone\", \"make this sound like us\", \"rewrite this in our tone\", or \"this doesn't sound on-brand\". Not for generating guidelines from scratch (use guideline-generation) or discovering brand materials (use discover-brand).\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1427,1428,1429,1432],{"name":1174,"slug":1175,"type":16},{"name":1271,"slug":1272,"type":16},{"name":1430,"slug":1431,"type":16},"Content Creation","content-creation",{"name":1275,"slug":1276,"type":16},"2026-04-06T18:00:23.528956",200]