[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71M3304WAHB598WD101Y4T7":3,"skill-same:01M71M3304WAHB598WD101Y4T7":67},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":25,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":44,"commitSha":45,"skillDirectory":46,"forksCount":47,"repositoryHtmlUrl":48,"repositoryHomepageUrl":49,"ownerHtmlUrl":50,"ownerBlogUrl":51,"ownerDescription":6,"updatedAt":52,"install":53,"resources":66},"01M71M3304WAHB598WD101Y4T7","mastermind-research",null,"Research: market, competitors, users, trends. A Research Manager coordinates researcher agents and reports the findings.","monoes\u002Fmonomind","monoes","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F276744696?v=4","01M71KWF37AJ2NJBP3KWBVSDGT","01M71KWF4CSF8ADH0M6HA09VD2",23,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fmastermind-research\u002FSKILL.md",[17,22],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[26,27,28,29,30,31,32,33,34,35,36,37],"research","mastermind research","domain","auto","Market","users","reports","researcher","trends","coordinates","manager","competitors","2026-11-10 10:56:23",false,"---\nname: mastermind-research\ndescription: \"Research: market, competitors, users, trends. A Research Manager coordinates researcher agents and reports the findings.\"\ntype: domain-skill\ndefault_mode: auto\n---\n\n# Mastermind Research Domain\n\nThis skill is invoked directly via `\u002Fmastermind:research`, or by `\u002Fmastermind:master` (pack extras — `monomind packs add extras`).\n\n---\n\n## Inputs\n\n- `brain_context`: BRAIN CONTEXT block (injected by master, or loaded standalone via mastermind-protocol\u002FSKILL.md brain load)\n- `prompt`: the research question or intelligence goal\n- `project_name`: monotask space name\n- `board_id`: monotask board ID (set by master, or created standalone)\n- `mode`: auto | confirm\n\n---\n\n## Complexity Assessment\n\nAssess the prompt to determine execution mode:\n\n**Simple (direct execution):** Single-answer lookup or quick scan:\n- \"What is the pricing model for Competitor X?\"\n- \"Find the current market size for SaaS tools in HR\"\n→ Use a single researcher agent. Skip manager delegation.\n\n**Complex (spawn Research Manager agent):** Any of these:\n- Full competitive landscape analysis\n- Market sizing with multiple data sources\n- User research synthesis across multiple interviews or signals\n- Trend analysis requiring cross-domain intelligence\n→ Spawn Research Manager agent with full briefing.\n\n---\n\n## Standalone Execution (when called without master)\n\nIf this skill is invoked directly (not by master):\n\n1. Load brain context following mastermind-protocol\u002FSKILL.md Brain Load Procedure (namespace: `research`)\n2. Run intake from mastermind-intake\u002FSKILL.md if prompt is vague\n3. Follow mastermind-protocol\u002FSKILL.md Monotask Space+Board Setup Procedure:\n   ```bash\n   project_name=\"${project_name:-$(basename \"$PWD\")}\"\n   space_id=$(monotask space list 2>\u002Fdev\u002Fnull | awk -F' \\| ' -v n=\"$project_name\" '$2==n{print $1}' | head -1)\n   [ -z \"$space_id\" ] && space_id=$(monotask space create \"$project_name\" 2>&1 | grep -oE '[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}')\n   [ -z \"$space_id\" ] && { echo \"ERROR: Could not find or create space '$project_name'\"; exit 1; }\n   board_id=$(monotask board create \"research\" --json | jq -r '.id \u002F\u002F empty')\n   [ -z \"$board_id\" ] && { echo \"ERROR: Failed to create research board\"; exit 1; }\n   monotask space boards add \"$space_id\" \"$board_id\" >\u002Fdev\u002Fnull 2>&1 || true\n   todo_col=$(monotask column create \"$board_id\" \"Todo\"  --json | jq -r '.id')\n   doing_col=$(monotask column create \"$board_id\" \"Doing\" --json | jq -r '.id')\n   done_col=$(monotask column create \"$board_id\" \"Done\"  --json | jq -r '.id')\n   ```\n4. Proceed with complexity assessment below\n5. At end: follow mastermind-protocol\u002FSKILL.md Brain Write Procedure (namespace: `research`)\n\n---\n\n## Complex Execution — Research Manager Agent\n\nSpawn a Research Manager agent via Task tool:\n\n```javascript\nTask({\n  subagent_type: \"researcher\",\n  description: `You are the Research Manager for project \u003Cproject_name>.\n\nCONTEXT: \u003Cdate> | Project: \u003Cproject_name> | Spawned by: mastermind:research\n\nBRAIN CONTEXT:\n\u003Cbrain_context>\n\nYOUR BOARD: \u003Cboard_id>\nYOUR GOAL: \u003Cprompt>\n\nSTEP 1 — PLAN\nDecompose the research goal into parallel intelligence streams. For each stream, identify:\n- What specific question it answers\n- Which data sources to tap (web, docs, user signals, code, analytics)\n- Which specialist is best suited\n- How outputs from different streams combine into a final answer\n\nSTEP 2 — CREATE TASKS\nFor each research stream, create a monotask card on the project board. First look up column IDs and assign shell variables:\n```bash\ncolumns=$(monotask column list \"$BOARD_ID\" --json)\nCOL_TODO_ID=$(echo \"$columns\" | jq -r '.[] | select(.title == \"Todo\" or .title == \"Backlog\") | .id' | head -1)\nCOL_DONE_ID=$(echo \"$columns\" | jq -r '.[] | select(.title == \"Done\") | .id' | head -1)\n```\nThen create the card:\n```bash\nresult=$(monotask card create \"$BOARD_ID\" \"$COL_TODO_ID\" \"\u003Cshort summary of research question, ≤80 chars>\" --json)\nCARD_ID=$(echo \"$result\" | jq -r '.id \u002F\u002F empty')\nmonotask card set-description \"$BOARD_ID\" \"$CARD_ID\" \"[specific research question this stream answers]\"\nmonotask card comment add \"$BOARD_ID\" \"$CARD_ID\" \"CONTEXT: \u003Cdate> | Project: \u003Cproject_name> | Created by: Research Manager\nBRAIN MEMORY: [paste most relevant 3-5 brain context excerpts]\nSCOPE: [sources to consult, search queries to run, depth of analysis]\nCONSTRAINTS: [recency requirements, geographic scope, data reliability thresholds]\nSUCCESS CRITERIA:\n- [ ] [checkable item — e.g. \\\"top 5 competitors identified with pricing\\\"]\nAGENT: [specialist picked in STEP 3]\nSWARM: mesh 4 gossip\nDEPENDENCIES: [task IDs or \\\"none\\\"]\nOUTPUT FORMAT: unified output schema\"\n```\n\nSTEP 3 — EXECUTE\nSpawn one Task agent per research stream (mesh topology — findings cross-pollinate).\nPick one specialist per research stream from the shared agent index (the Jev decision model when configured, keyword ranking otherwise); it only returns agents that exist. In order: the prompt's `[PICK]` line when it fits this research stream; else `mcp__monomind__pick({ task: \"\u003Cresearch stream>: \u003Cscope>\", kind: \"agents\", top: 1 })` → `agents.ranked[0].name`; else the local CLI through the version-checked `mmpick` helper (never npx; the block defines it because each Bash call starts a fresh shell):\n```bash\n# Local only — never npx. Accept a binary only when its pick output is the\n# unified index (skill entries carry `source`); see mastermind-agent-select.\nmmpick() { for c in monomind .\u002Fnode_modules\u002F.bin\u002Fmonomind; do\n    command -v \"$c\" >\u002Fdev\u002Fnull 2>&1 || continue\n    out=$(\"$c\" pick \"$@\" --json 2>\u002Fdev\u002Fnull) || continue\n    printf '%s' \"$out\" | jq -e '[.skills.ranked[]? | has(\"source\")] | (length > 0 and all)' \\\n      >\u002Fdev\u002Fnull 2>&1 && { printf '%s\\n' \"$out\"; return 0; }\n  done; return 127; }\nmmpick -t \"\u003Cresearch stream>: \u003Cscope>\" --top 1 | jq -r '.agents.ranked[0].name \u002F\u002F empty'\n```\nUse the returned name as that research stream's subagent_type. If nothing is returned, use the default below.\n- Any stream: \"researcher\"\n\nAlso run \u002Fmastermind:do --board \u003Cboard_id> to track execution.\n\nSTEP 4 — COLLECT AND RETURN\nSynthesize all research streams into an intelligence report. Return to caller:\n\ndomain: research\nstatus: complete | partial | blocked\nartifacts:\n  - path: [research report if written to disk]\n    type: report\ndecisions:\n  - what: [key findings and recommended actions]\n    why: [evidence from research]\n    confidence: [0.0-1.0]\n    outcome: pending\nlessons:\n  - what_worked: [which sources or methods yielded best signal]\n  - what_didnt: [gaps or low-quality sources]\nnext_actions:\n  - [e.g. \"run mastermind:idea to act on market insights\"]\n  - [e.g. \"run mastermind:marketing with validated positioning\"]\nboard_url: monotask:\u002F\u002F\u003Cproject_name>\u002Fresearch\nrun_id: \u003CISO8601-timestamp>`,\n  run_in_background: true\n})\n```\n\n---\n\n## Simple Execution\n\nFor simple tasks (single researcher, single question):\n\n1. Spawn one Task agent with the research question as a self-contained briefing\n2. Collect output\n3. Return unified output schema with `status: complete`\n\n---\n\n## Domain Swarm Defaults\n\n| Task Type | Agent | Swarm |\n|---|---|---|\n| Full competitive analysis | researcher + trend + UX | mesh 4 gossip balanced |\n| Market sizing | researcher | hierarchical 3 raft specialized |\n| Trend scan | researcher | single agent |\n| User research synthesis | researcher + Monodesign | hierarchical 3 raft specialized |\n| Quick factual lookup | researcher | single agent |\n","# Mastermind Research Domain\n\nThis skill is invoked directly via `\u002Fmastermind:research`, or by `\u002Fmastermind:master` (pack extras — `monomind packs add extras`).\n\n---\n\n## Inputs\n\n- `brain_context`: BRAIN CONTEXT block (injected by master, or loaded standalone via mastermind-protocol\u002FSKILL.md brain load)\n- `prompt`: the research question or intelligence goal\n- `project_name`: monotask space name\n- `board_id`: monotask board ID (set by master, or created standalone)\n- `mode`: auto | confirm\n\n---\n\n## Complexity Assessment\n\nAssess the prompt to determine execution mode:\n\n**Simple (direct execution):** Single-answer lookup or quick scan:\n- \"What is the pricing model for Competitor X?\"\n- \"Find the current market size for SaaS tools in HR\"\n→ Use a single researcher agent. Skip manager delegation.\n\n**Complex (spawn Research Manager agent):** Any of these:\n- Full competitive landscape analysis\n- Market sizing with multiple data sources\n- User research synthesis across multiple interviews or signals\n- Trend analysis requiring cross-domain intelligence\n→ Spawn Research Manager agent with full briefing.\n\n---\n\n## Standalone Execution (when called without master)\n\nIf this skill is invoked directly (not by master):\n\n1. Load brain context following mastermind-protocol\u002FSKILL.md Brain Load Procedure (namespace: `research`)\n2. Run intake from mastermind-intake\u002FSKILL.md if prompt is vague\n3. Follow mastermind-protocol\u002FSKILL.md Monotask Space+Board Setup Procedure:\n   ```bash\n   project_name=\"${project_name:-$(basename \"$PWD\")}\"\n   space_id=$(monotask space list 2>\u002Fdev\u002Fnull | awk -F' \\| ' -v n=\"$project_name\" '$2==n{print $1}' | head -1)\n   [ -z \"$space_id\" ] && space_id=$(monotask space create \"$project_name\" 2>&1 | grep -oE '[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}')\n   [ -z \"$space_id\" ] && { echo \"ERROR: Could not find or create space '$project_name'\"; exit 1; }\n   board_id=$(monotask board create \"research\" --json | jq -r '.id \u002F\u002F empty')\n   [ -z \"$board_id\" ] && { echo \"ERROR: Failed to create research board\"; exit 1; }\n   monotask space boards add \"$space_id\" \"$board_id\" >\u002Fdev\u002Fnull 2>&1 || true\n   todo_col=$(monotask column create \"$board_id\" \"Todo\"  --json | jq -r '.id')\n   doing_col=$(monotask column create \"$board_id\" \"Doing\" --json | jq -r '.id')\n   done_col=$(monotask column create \"$board_id\" \"Done\"  --json | jq -r '.id')\n   ```\n4. Proceed with complexity assessment below\n5. At end: follow mastermind-protocol\u002FSKILL.md Brain Write Procedure (namespace: `research`)\n\n---\n\n## Complex Execution — Research Manager Agent\n\nSpawn a Research Manager agent via Task tool:\n\n```javascript\nTask({\n  subagent_type: \"researcher\",\n  description: `You are the Research Manager for project \u003Cproject_name>.\n\nCONTEXT: \u003Cdate> | Project: \u003Cproject_name> | Spawned by: mastermind:research\n\nBRAIN CONTEXT:\n\u003Cbrain_context>\n\nYOUR BOARD: \u003Cboard_id>\nYOUR GOAL: \u003Cprompt>\n\nSTEP 1 — PLAN\nDecompose the research goal into parallel intelligence streams. For each stream, identify:\n- What specific question it answers\n- Which data sources to tap (web, docs, user signals, code, analytics)\n- Which specialist is best suited\n- How outputs from different streams combine into a final answer\n\nSTEP 2 — CREATE TASKS\nFor each research stream, create a monotask card on the project board. First look up column IDs and assign shell variables:\n```bash\ncolumns=$(monotask column list \"$BOARD_ID\" --json)\nCOL_TODO_ID=$(echo \"$columns\" | jq -r '.[] | select(.title == \"Todo\" or .title == \"Backlog\") | .id' | head -1)\nCOL_DONE_ID=$(echo \"$columns\" | jq -r '.[] | select(.title == \"Done\") | .id' | head -1)\n```\nThen create the card:\n```bash\nresult=$(monotask card create \"$BOARD_ID\" \"$COL_TODO_ID\" \"\u003Cshort summary of research question, ≤80 chars>\" --json)\nCARD_ID=$(echo \"$result\" | jq -r '.id \u002F\u002F empty')\nmonotask card set-description \"$BOARD_ID\" \"$CARD_ID\" \"[specific research question this stream answers]\"\nmonotask card comment add \"$BOARD_ID\" \"$CARD_ID\" \"CONTEXT: \u003Cdate> | Project: \u003Cproject_name> | Created by: Research Manager\nBRAIN MEMORY: [paste most relevant 3-5 brain context excerpts]\nSCOPE: [sources to consult, search queries to run, depth of analysis]\nCONSTRAINTS: [recency requirements, geographic scope, data reliability thresholds]\nSUCCESS CRITERIA:\n- [ ] [checkable item — e.g. \\\"top 5 competitors identified with pricing\\\"]\nAGENT: [specialist picked in STEP 3]\nSWARM: mesh 4 gossip\nDEPENDENCIES: [task IDs or \\\"none\\\"]\nOUTPUT FORMAT: unified output schema\"\n```\n\nSTEP 3 — EXECUTE\nSpawn one Task agent per research stream (mesh topology — findings cross-pollinate).\nPick one specialist per research stream from the shared agent index (the Jev decision model when configured, keyword ranking otherwise); it only returns agents that exist. In order: the prompt's `[PICK]` line when it fits this research stream; else `mcp__monomind__pick({ task: \"\u003Cresearch stream>: \u003Cscope>\", kind: \"agents\", top: 1 })` → `agents.ranked[0].name`; else the local CLI through the version-checked `mmpick` helper (never npx; the block defines it because each Bash call starts a fresh shell):\n```bash\n# Local only — never npx. Accept a binary only when its pick output is the\n# unified index (skill entries carry `source`); see mastermind-agent-select.\nmmpick() { for c in monomind .\u002Fnode_modules\u002F.bin\u002Fmonomind; do\n    command -v \"$c\" >\u002Fdev\u002Fnull 2>&1 || continue\n    out=$(\"$c\" pick \"$@\" --json 2>\u002Fdev\u002Fnull) || continue\n    printf '%s' \"$out\" | jq -e '[.skills.ranked[]? | has(\"source\")] | (length > 0 and all)' \\\n      >\u002Fdev\u002Fnull 2>&1 && { printf '%s\\n' \"$out\"; return 0; }\n  done; return 127; }\nmmpick -t \"\u003Cresearch stream>: \u003Cscope>\" --top 1 | jq -r '.agents.ranked[0].name \u002F\u002F empty'\n```\nUse the returned name as that research stream's subagent_type. If nothing is returned, use the default below.\n- Any stream: \"researcher\"\n\nAlso run \u002Fmastermind:do --board \u003Cboard_id> to track execution.\n\nSTEP 4 — COLLECT AND RETURN\nSynthesize all research streams into an intelligence report. Return to caller:\n\ndomain: research\nstatus: complete | partial | blocked\nartifacts:\n  - path: [research report if written to disk]\n    type: report\ndecisions:\n  - what: [key findings and recommended actions]\n    why: [evidence from research]\n    confidence: [0.0-1.0]\n    outcome: pending\nlessons:\n  - what_worked: [which sources or methods yielded best signal]\n  - what_didnt: [gaps or low-quality sources]\nnext_actions:\n  - [e.g. \"run mastermind:idea to act on market insights\"]\n  - [e.g. \"run mastermind:marketing with validated positioning\"]\nboard_url: monotask:\u002F\u002F\u003Cproject_name>\u002Fresearch\nrun_id: \u003CISO8601-timestamp>`,\n  run_in_background: true\n})\n```\n\n---\n\n## Simple Execution\n\nFor simple tasks (single researcher, single question):\n\n1. Spawn one Task agent with the research question as a self-contained briefing\n2. Collect output\n3. Return unified output schema with `status: complete`\n\n---\n\n## Domain Swarm Defaults\n\n| Task Type | Agent | Swarm |\n|---|---|---|\n| Full competitive analysis | researcher + trend + UX | mesh 4 gossip balanced |\n| Market sizing | researcher | hierarchical 3 raft specialized |\n| Trend scan | researcher | single agent |\n| User research synthesis | researcher + Monodesign | hierarchical 3 raft specialized |\n| Quick factual lookup | researcher | single agent |\n",{"name":5,"type":43,"description":7,"default_mode":29},"domain-skill","main","f7cd4595d0f8959822163e14f9787117765c931a",".claude\u002Fskills\u002Fmastermind-research",2,"https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind","https:\u002F\u002Fmonoes.github.io\u002Fmonomind\u002F","https:\u002F\u002Fgithub.com\u002Fmonoes","https:\u002F\u002Fmonoes.me","2026-11-10 01:53:48",{"source":8,"skillName":5,"commands":54,"prompt":64,"zipAvailable":65},[55,58,61],{"tool":56,"command":57},"npx","npx skills add monoes\u002Fmonomind --skill mastermind-research",{"tool":59,"command":60},"bunx","bunx skills add monoes\u002Fmonomind --skill mastermind-research",{"tool":62,"command":63},"pnpm","pnpm dlx skills add monoes\u002Fmonomind --skill mastermind-research","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fmastermind-research\u002FSKILL.md",true,[],[68,96,131,158,183,208],{"skillNo":69,"name":70,"nameZh":6,"description":71,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":72,"categories":73,"keywords":74,"publishedAt":95},"01M71PB3SHWKMNJM6MD65VX8D6","zk-steward","Org role guidance for a ZK steward: engineer zero-knowledge proof systems with circuit design, SNARK vs STARK choice and security review. This is zero-knowledge cryptography, not Zettelkasten note-taking.","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fzk-steward\u002FSKILL.md",[],[75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94],"zk","zk steward","steward","zero-knowledge","engineer","choice","design","knowledge","impact","query","snark","review","monomind","security review","stark","zettelkasten","cryptography","security","org","blockchain","2026-11-10 13:02:02",{"skillNo":97,"name":98,"nameZh":6,"description":99,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":100,"licenseFileUrl":101,"sourceHtmlUrl":102,"categories":103,"keywords":112,"publishedAt":95},"01M71PB18SDG28WBKPKJNB814K","writing-plans","Use when you have a spec or requirements for a multi-step coding task and must write an implementation plan before touching code. Produces bite-sized TDD tasks with exact file paths, code and commands for an engineer with zero context.","MIT","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fwriting-plans\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fwriting-plans\u002FSKILL.md",[104,107],{"categoryNo":105,"categoryName":106,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":108,"categoryName":109,"taxonomyType":110,"taxonomyTypeDesc":111},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[113,114,115,79,116,117,118,119,84,120,121,122,123,124,125,126,127,128,129,130],"writing","writing plans","plans","superpowers","implementation plan","when","you","code","tasks","plan","product","write","commands","paths","suggest","coding","context","ZeRO",{"skillNo":132,"name":133,"nameZh":6,"description":134,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":135,"sourceHtmlUrl":136,"categories":137,"keywords":141,"publishedAt":95},"01M71PAXFM488X9ARXK0T04TF5","write-spec","Write a feature spec or PRD from a problem statement or feature idea. 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Use when translating a natural-language data need into SQL, building a multi-CTE query with joins and aggregations, optimizing a query against a large partitioned table, or getting dialect-specific syntax for Snowflake, BigQuery, Postgres, etc.","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fwrite-query\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fwrite-query\u002FSKILL.md",[165],{"categoryNo":166,"categoryName":167,"taxonomyType":110,"taxonomyTypeDesc":111},"01JBSEEDCAT000000000000033","数据分析师",[84,124,169,170,171,172,82,156,118,173,174,175,176,149,177,178,179,180,181,182],"write query","postgres","for","snowflake","best practices","natural-language","with","multi","github","sql","https","syntax","plugins","data",{"skillNo":184,"name":185,"nameZh":6,"description":186,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":187,"categories":188,"keywords":193,"publishedAt":95},"01M71PAPTX0T3PF6ETFVR96SJJ","workflow-automation","Org role guidance for workflow automation: build and maintain CI\u002FCD definitions in GitHub Actions or equivalent: triggers, jobs, matrices, caching. 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