[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71V57PH9PGS3G0EEY5RRDBC":3,"skill-same:01M71V57PH9PGS3G0EEY5RRDBC":73},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":46,"commitSha":47,"skillDirectory":48,"forksCount":49,"repositoryHtmlUrl":50,"repositoryHomepageUrl":6,"ownerHtmlUrl":51,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":52,"install":53,"resources":66},"01M71V57PH9PGS3G0EEY5RRDBC","company-intel",null,"Researches a hiring company's culture, engineering practices, funding\u002Fstage, size, and employee sentiment using hybrid search (vector + full-text) on the companies collection, enriched by the Bedrock knowledge base. Use this skill when a candidate asks about a specific company, what it is like to work there, its reputation, funding or size, or wants the companies behind their job matches profiled.","mongodb-partners\u002FMulti-Agent-Architectural-Guidance-Bedrock-AgentCore","mongodb-partners","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F9803686?v=4","01M71V4VSPZ03KW81YPCH5X0TW","01M71V4VT9HSQY1MYK6BRYR1CK",19,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fmongodb-partners\u002FMulti-Agent-Architectural-Guidance-Bedrock-AgentCore\u002Fblob\u002Fmain\u002Fconfig\u002Fskills\u002Fcompany-intel\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"company intel","intel","company","knowledge base","text","recruitment","candidate","collection","stage","when","use","engineering practices","knowledge","bedrock","culture","vector","search","hybrid","Hybrid Search","funding","2026-11-10 13:02:02",false,"---\nname: company-intel\ndescription: >-\n  Researches a hiring company's culture, engineering practices, funding\u002Fstage, size, and\n  employee sentiment using hybrid search (vector + full-text) on the companies collection,\n  enriched by the Bedrock knowledge base. Use this skill when a candidate asks about a\n  specific company, what it is like to work there, its reputation, funding or size, or wants\n  the companies behind their job matches profiled.\nmetadata:\n  author: anujpanchal\n  version: \"1.0\"\n  domain: recruitment\n---\n\n# Company Intel Skill\n\n## What You Can Do\n\n1. **Resolve the company target** — identify which company (or companies) the candidate wants\n   profiled, including companies referenced from prior job-match results.\n2. **Retrieve the structured company record** — run a hybrid vector + full-text search against\n   the `companies` collection.\n3. **Enrich with the knowledge base** — when the record is thin or the candidate asks a broad\n   question, pull additional context from the Bedrock knowledge base.\n4. **Present a balanced profile** — combine both sources into an attributed summary with\n   strengths and watch-outs.\n\n---\n\n## Step 1 — Resolve the Company Target\n\nWhen a candidate asks about a company:\n\n1. Identify the **company name(s)** from the message or the recent conversation (e.g. a\n   company named in an earlier job-match result). If more than three companies are requested,\n   ask the candidate to pick the top ones to profile.\n\n2. If no company is named and none is available from context, ask **one** clarifying question\n   and stop.\n\n3. Build a `queryText` string in this format for each company:\n   ```\n   \u003Ccompany name>\n   \u003Cindustry if known>\n   culture, engineering practices, funding stage, employee sentiment\n   ```\n\n---\n\n## Step 2 — Hybrid Company Search\n\nPerform a hybrid search against the `companies` collection in **a single tool call** per\ncompany. The MCP runtime runs the vector and lexical legs in parallel and merges them with\nReciprocal Rank Fusion server-side — do **not** run a separate `$search` aggregation or\ncompute RRF in-band.\n\n### Single hybrid call\n\nCall `mongodb_vector_search` with:\n- `collection`: `companies`\n- `queryText`: the string built in Step 1.3. The runtime embeds this server-side for the\n  vector leg and uses it verbatim for the BM25 lexical leg. **Do NOT call\n  `embed_multimodal_content` — the search runtime embeds `queryText` internally.**\n- `indexName`: `companies_vector_index`\n- `hybrid`: `true`\n- `lexicalIndex`: `companies_text_index`\n- `lexicalPath`: `name` — single text-indexed field the BM25 leg searches, so an exact company\n  name matches strongly. (Atlas Search hybrid mode is single-path; broader recall is recovered\n  by the vector leg.)\n- `limit`: 3 — final fused result count.\n- `fetchK`: 8 — per-leg over-fetch before RRF merge.\n\nEach result carries a `_score` (RRF score) and a `_sources` array. Pick the record whose\n`name` matches the requested company. If the exact company is not among the results, do a\ndirect `mongodb_query` → `findOne` on `companies` by `companyId` or an exact-name filter\n`{ \"name\": \"\u003Ccompany>\" }` with `\"projection\": { \"embedding\": 0 }`.\n\n---\n\n## Step 3 — Enrich with the Knowledge Base\n\nUse `bedrock_kb_retrieve` when:\n- The `companies` record is missing or thin (few culture\u002Fsentiment fields), OR\n- The candidate asks a broad question the structured fields do not answer (e.g. \"how do\n  engineers describe the on-call culture?\").\n\nCall `bedrock_kb_retrieve` with a natural-language query combining the company name and the\ncandidate's focus (e.g. \"Lumen Retail engineering on-call culture and work-life balance\").\nTreat the returned passages as supporting evidence and attribute them as knowledge-base\ncontext. Never present KB passages as first-person company statements.\n\nIf neither the collection nor the KB returns anything for the company, tell the candidate\nhonestly and do not guess.\n\n---\n\n## Step 4 — Present a Balanced Profile\n\nPresent each company in this structure:\n\n1. **Headline** — one line: `name`, `industry`, `stage`, and `headcount` band.\n2. **Culture & practices** — from `cultureSignals` and `engineeringPractices` (present the\n   documented items; do not editorialize beyond them).\n3. **Stage & size** — from `stage`, `foundedYear`, `headcount`, and `fundingTotal` when\n   present.\n4. **What people say** — from `sentimentSummary` and any KB passages, clearly labeled as\n   employee sentiment \u002F external context. Include both positive signals and documented\n   watch-outs (`pros` \u002F `cons` when present).\n5. **Fit note** — one balanced sentence on who this environment tends to suit, grounded in the\n   retrieved signals.\n\nCite `companyId` when you reference a specific record. When profiling multiple companies,\npresent them as parallel sections so the candidate can compare.\n\n---\n\n## References\n\n**References** (on demand): the API only serves files for skills that are **allowed for this\nagent** and **already activated** (`activate_skill`, or pre-activation for specialists). Call\n**`read_skill_resource`** with **`skillName`** `company-intel` and **`path`** (relative to that\nskill folder), e.g.:\n\n- `references\u002Fcollections-schema.md` — Full document schema, index definitions, and field\n  notes for the `companies` collection. Load this when you need to verify field names, filter\n  parameters, or construct precise queries.\n\nFor the **company-intel** specialist agent, this skill is **pre-activated** at turn start, so\n`read_skill_resource` works immediately. If you see `skill_not_activated`, run\n**`activate_skill`** with `company-intel` first.\n\n---\n\n## Edge Cases\n\n**Company not in the dataset:** If neither hybrid search nor a direct `findOne` finds the\ncompany, try `bedrock_kb_retrieve` once. If that is also empty, tell the candidate the company\nis not on file and offer to profile a company that is.\n\n**Thin record:** If the record exists but has few sentiment\u002Fculture fields, present what is\nthere and enrich with the KB (Step 3) rather than inventing detail.\n\n**Multiple companies requested:** Profile up to three per turn. If more are requested, ask the\ncandidate to prioritize.\n\n**Ambiguous name (multiple matches):** Ask the candidate to confirm which one (by industry or\nlocation) before profiling.\n\n## Boundaries\n\n- This skill handles company research — culture, practices, stage\u002Fsize, and sentiment — only.\n- Do not provide salary figures or compensation benchmarks — that belongs to the compensation\n  specialist.\n- Do not design learning plans or analyze skill gaps — that belongs to the career-development\n  specialist.\n- Do not match the candidate to open job listings or write interview prep — that belongs to\n  the job-match specialist.\n- Do not speculate about a company's future, layoffs, or stock performance.\n- Never reveal raw MongoDB `_id` values; use `companyId`.\n- Never fabricate company facts, culture, funding, headcount, or sentiment. All responses must\n  be grounded in collection or knowledge-base data, and presented in a balanced way.\n","# Company Intel Skill\n\n## What You Can Do\n\n1. **Resolve the company target** — identify which company (or companies) the candidate wants\n   profiled, including companies referenced from prior job-match results.\n2. **Retrieve the structured company record** — run a hybrid vector + full-text search against\n   the `companies` collection.\n3. **Enrich with the knowledge base** — when the record is thin or the candidate asks a broad\n   question, pull additional context from the Bedrock knowledge base.\n4. **Present a balanced profile** — combine both sources into an attributed summary with\n   strengths and watch-outs.\n\n---\n\n## Step 1 — Resolve the Company Target\n\nWhen a candidate asks about a company:\n\n1. Identify the **company name(s)** from the message or the recent conversation (e.g. a\n   company named in an earlier job-match result). If more than three companies are requested,\n   ask the candidate to pick the top ones to profile.\n\n2. If no company is named and none is available from context, ask **one** clarifying question\n   and stop.\n\n3. Build a `queryText` string in this format for each company:\n   ```\n   \u003Ccompany name>\n   \u003Cindustry if known>\n   culture, engineering practices, funding stage, employee sentiment\n   ```\n\n---\n\n## Step 2 — Hybrid Company Search\n\nPerform a hybrid search against the `companies` collection in **a single tool call** per\ncompany. The MCP runtime runs the vector and lexical legs in parallel and merges them with\nReciprocal Rank Fusion server-side — do **not** run a separate `$search` aggregation or\ncompute RRF in-band.\n\n### Single hybrid call\n\nCall `mongodb_vector_search` with:\n- `collection`: `companies`\n- `queryText`: the string built in Step 1.3. The runtime embeds this server-side for the\n  vector leg and uses it verbatim for the BM25 lexical leg. **Do NOT call\n  `embed_multimodal_content` — the search runtime embeds `queryText` internally.**\n- `indexName`: `companies_vector_index`\n- `hybrid`: `true`\n- `lexicalIndex`: `companies_text_index`\n- `lexicalPath`: `name` — single text-indexed field the BM25 leg searches, so an exact company\n  name matches strongly. (Atlas Search hybrid mode is single-path; broader recall is recovered\n  by the vector leg.)\n- `limit`: 3 — final fused result count.\n- `fetchK`: 8 — per-leg over-fetch before RRF merge.\n\nEach result carries a `_score` (RRF score) and a `_sources` array. Pick the record whose\n`name` matches the requested company. If the exact company is not among the results, do a\ndirect `mongodb_query` → `findOne` on `companies` by `companyId` or an exact-name filter\n`{ \"name\": \"\u003Ccompany>\" }` with `\"projection\": { \"embedding\": 0 }`.\n\n---\n\n## Step 3 — Enrich with the Knowledge Base\n\nUse `bedrock_kb_retrieve` when:\n- The `companies` record is missing or thin (few culture\u002Fsentiment fields), OR\n- The candidate asks a broad question the structured fields do not answer (e.g. \"how do\n  engineers describe the on-call culture?\").\n\nCall `bedrock_kb_retrieve` with a natural-language query combining the company name and the\ncandidate's focus (e.g. \"Lumen Retail engineering on-call culture and work-life balance\").\nTreat the returned passages as supporting evidence and attribute them as knowledge-base\ncontext. Never present KB passages as first-person company statements.\n\nIf neither the collection nor the KB returns anything for the company, tell the candidate\nhonestly and do not guess.\n\n---\n\n## Step 4 — Present a Balanced Profile\n\nPresent each company in this structure:\n\n1. **Headline** — one line: `name`, `industry`, `stage`, and `headcount` band.\n2. **Culture & practices** — from `cultureSignals` and `engineeringPractices` (present the\n   documented items; do not editorialize beyond them).\n3. **Stage & size** — from `stage`, `foundedYear`, `headcount`, and `fundingTotal` when\n   present.\n4. **What people say** — from `sentimentSummary` and any KB passages, clearly labeled as\n   employee sentiment \u002F external context. Include both positive signals and documented\n   watch-outs (`pros` \u002F `cons` when present).\n5. **Fit note** — one balanced sentence on who this environment tends to suit, grounded in the\n   retrieved signals.\n\nCite `companyId` when you reference a specific record. When profiling multiple companies,\npresent them as parallel sections so the candidate can compare.\n\n---\n\n## References\n\n**References** (on demand): the API only serves files for skills that are **allowed for this\nagent** and **already activated** (`activate_skill`, or pre-activation for specialists). Call\n**`read_skill_resource`** with **`skillName`** `company-intel` and **`path`** (relative to that\nskill folder), e.g.:\n\n- `references\u002Fcollections-schema.md` — Full document schema, index definitions, and field\n  notes for the `companies` collection. Load this when you need to verify field names, filter\n  parameters, or construct precise queries.\n\nFor the **company-intel** specialist agent, this skill is **pre-activated** at turn start, so\n`read_skill_resource` works immediately. If you see `skill_not_activated`, run\n**`activate_skill`** with `company-intel` first.\n\n---\n\n## Edge Cases\n\n**Company not in the dataset:** If neither hybrid search nor a direct `findOne` finds the\ncompany, try `bedrock_kb_retrieve` once. If that is also empty, tell the candidate the company\nis not on file and offer to profile a company that is.\n\n**Thin record:** If the record exists but has few sentiment\u002Fculture fields, present what is\nthere and enrich with the KB (Step 3) rather than inventing detail.\n\n**Multiple companies requested:** Profile up to three per turn. If more are requested, ask the\ncandidate to prioritize.\n\n**Ambiguous name (multiple matches):** Ask the candidate to confirm which one (by industry or\nlocation) before profiling.\n\n## Boundaries\n\n- This skill handles company research — culture, practices, stage\u002Fsize, and sentiment — only.\n- Do not provide salary figures or compensation benchmarks — that belongs to the compensation\n  specialist.\n- Do not design learning plans or analyze skill gaps — that belongs to the career-development\n  specialist.\n- Do not match the candidate to open job listings or write interview prep — that belongs to\n  the job-match specialist.\n- Do not speculate about a company's future, layoffs, or stock performance.\n- Never reveal raw MongoDB `_id` values; use `companyId`.\n- Never fabricate company facts, culture, funding, headcount, or sentiment. All responses must\n  be grounded in collection or knowledge-base data, and presented in a balanced way.\n",{"name":5,"metadata":43,"description":7},{"author":44,"domain":23,"version":45},"anujpanchal","1.0","main","a6bd8916bc2b0c913faf9704528ecbc7f4432c2a","config\u002Fskills\u002Fcompany-intel",5,"https:\u002F\u002Fgithub.com\u002Fmongodb-partners\u002FMulti-Agent-Architectural-Guidance-Bedrock-AgentCore","https:\u002F\u002Fgithub.com\u002Fmongodb-partners","2026-11-10 06:47:38",{"source":8,"skillName":5,"commands":54,"prompt":64,"zipAvailable":65},[55,58,61],{"tool":56,"command":57},"npx","npx skills add mongodb-partners\u002FMulti-Agent-Architectural-Guidance-Bedrock-AgentCore --skill company-intel",{"tool":59,"command":60},"bunx","bunx skills add mongodb-partners\u002FMulti-Agent-Architectural-Guidance-Bedrock-AgentCore --skill company-intel",{"tool":62,"command":63},"pnpm","pnpm dlx skills add mongodb-partners\u002FMulti-Agent-Architectural-Guidance-Bedrock-AgentCore --skill company-intel","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmongodb-partners\u002FMulti-Agent-Architectural-Guidance-Bedrock-AgentCore\u002Fblob\u002Fmain\u002Fconfig\u002Fskills\u002Fcompany-intel\u002FSKILL.md",true,[67],{"path":68,"resourceType":69,"resourceTypeDesc":70,"sizeBytes":71,"sourceHtmlUrl":72},"references\u002Fcollections-schema.md","REFERENCE","参考文档",3657,"https:\u002F\u002Fgithub.com\u002Fmongodb-partners\u002FMulti-Agent-Architectural-Guidance-Bedrock-AgentCore\u002Fblob\u002Fmain\u002Fconfig\u002Fskills\u002Fcompany-intel\u002Freferences\u002Fcollections-schema.md",[74,91,118,140],{"skillNo":75,"name":76,"nameZh":6,"description":77,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":78,"categories":79,"keywords":80,"publishedAt":38},"01M71V5AFS98MSXZTJEFXHDF8D","job-match","Matches candidate CVs to job openings using hybrid search (vector + full-text) on the jobs collection, and provides interview preparation questions from the interview_prep collection. Use this skill when a candidate submits their CV, asks about job matches, wants job recommendations, or requests interview prep questions for a specific role.","https:\u002F\u002Fgithub.com\u002Fmongodb-partners\u002FMulti-Agent-Architectural-Guidance-Bedrock-AgentCore\u002Fblob\u002Fmain\u002Fconfig\u002Fskills\u002Fjob-match\u002FSKILL.md",[],[81,82,83,34,33,25,22,84,28,23,85,36,86,87,88,89,90,24,35,27],"job match","match","recommendations","interview","cv","questions","interview preparation","cvs","requests","for",{"skillNo":92,"name":93,"nameZh":6,"description":94,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":95,"categories":96,"keywords":102,"publishedAt":38},"01M71V52W009CTK4EMEX1Z5DBZ","career-coach","Produces a skill-gap analysis and a phased learning roadmap for a candidate targeting a specific role (using hybrid search on the career_paths and skills_taxonomy collections), AND retrieves compensation benchmarks — base\u002Ftotal\u002Fpercentiles, benefits norms, and grounded negotiation ranges — for a role at a given seniority and location (using hybrid search on the compensation_data collection). Use this skill when a candidate asks what skills they need for a target role, how to transition between roles, how to reach a more senior level, wants a learning\u002Fdevelopment plan, OR asks what a role pays, what salary to expect or ask for, how pay varies by location or seniority, or how to frame a compensation negotiation.","https:\u002F\u002Fgithub.com\u002Fmongodb-partners\u002FMulti-Agent-Architectural-Guidance-Bedrock-AgentCore\u002Fblob\u002Fmain\u002Fconfig\u002Fskills\u002Fcareer-coach\u002FSKILL.md",[97],{"categoryNo":98,"categoryName":99,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000031","产品经理","PROFESSION","适用职业",[103,104,105,106,24,107,27,108,36,109,34,25,110,111,112,113,114,115,116,117],"coach","career coach","career","at","transition","senior","targeting","analysis","how","plan","ask","roadmap","how to","data collection","pay",{"skillNo":119,"name":120,"nameZh":6,"description":121,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":122,"categories":123,"keywords":129,"publishedAt":38},"01M71V50NSY3J0K4QC9D5FQV30","demo-skill","Test fixture skill for HTTP tools unit tests","https:\u002F\u002Fgithub.com\u002Fmongodb-partners\u002FMulti-Agent-Architectural-Guidance-Bedrock-AgentCore\u002Fblob\u002Fmain\u002Fapi\u002Ftests\u002Ffixtures\u002Fconfig\u002Fskills\u002Fdemo-skill\u002FSKILL.md",[124],{"categoryNo":125,"categoryName":126,"taxonomyType":127,"taxonomyTypeDesc":128},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[130,131,90,132,133,134,135,136,137,138,139],"demo skill","demo","test","unit tests","http","Tools","fixture skill","fixture","tests","unit",{"skillNo":141,"name":142,"nameZh":6,"description":143,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":144,"categories":145,"keywords":150,"publishedAt":38},"01M71V4YQYTBMPQHZPY0MVXNN3","e2e-fixture-skill","Config-only fixture: proves a new skill directory is discovered without API code changes.","https:\u002F\u002Fgithub.com\u002Fmongodb-partners\u002FMulti-Agent-Architectural-Guidance-Bedrock-AgentCore\u002Fblob\u002Fmain\u002Fapi\u002Ftests\u002Ffixtures\u002Fconfig-extra-skill-only\u002Fskills\u002Fe2e-fixture-skill\u002FSKILL.md",[146,149],{"categoryNo":147,"categoryName":148,"taxonomyType":127,"taxonomyTypeDesc":128},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":125,"categoryName":126,"taxonomyType":127,"taxonomyTypeDesc":128},[151,137,152,136,153,154,155,156,157,158,159,160],"e2e fixture skill","e2e","code","config","changes","new","new skill","only","api","directory"]