[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71M8D5KC072XZMSVX73T0GY":3,"skill-same:01M71M8D5KC072XZMSVX73T0GY":70},{"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":17,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":51,"repositoryHtmlUrl":47,"repositoryHomepageUrl":52,"ownerHtmlUrl":53,"ownerBlogUrl":54,"ownerDescription":6,"updatedAt":55,"install":56,"resources":69},"01M71M8D5KC072XZMSVX73T0GY","adaptive-coordinator2",null,"Use when an org role acts as adaptive coordinator and must re-partition and re-route agent work mid-run when agents stall or assumptions break. Stresses batched dispatch, evidence-based reconciliation and honest coverage reports, unlike the fixed-topology mesh-coordinator.","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\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fadaptive-coordinator2\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"adaptive","adaptive coordinator2","github","monomind","work","leadership","route","reports","coordination","assumptions","when","use","coordinator","partition","re","evidence","reconciliation","as","mesh","coverage","2026-11-10 10:56:23",false,"---\nname: adaptive-coordinator2\ndescription: \"Use when an org role acts as adaptive coordinator and must re-partition and re-route agent work mid-run when agents stall or assumptions break. Stresses batched dispatch, evidence-based reconciliation and honest coverage reports, unlike the fixed-topology mesh-coordinator.\"\ntags: [\"leadership\",\"operations\",\"coordination\",\"agents\"]\ntools: []\nlicense: Apache-2.0\nsource: https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\n---\n# Adaptive Coord. II — Best Practices\n\n## Focus\nCoordinates a group of agents whose topology and task split should change mid-run as conditions change — unlike a fixed mesh or hierarchy, this role actively re-partitions work and re-routes based on what's coming back.\n\n## Best practices\n- Treat topology as a decision, not a default — pick hierarchical, mesh, or pipeline per task based on dependency shape, and be willing to switch mid-run if the shape turns out wrong.\n- Re-plan on signal, not on schedule — a stalled subagent, a contradicted assumption, or a much-larger-than-expected slice are all triggers to re-partition immediately.\n- Keep partitions genuinely independent when running in parallel — if two slices need to negotiate mid-flight, that's a sign the topology should be sequential or hierarchical instead.\n- Dispatch all independent slices in a single batch so parallelism is real, not simulated by sequential calls.\n- Reconcile divergent results by re-examining evidence, not by averaging or picking the longest answer — record what each subagent actually found before deciding.\n- Report coverage honestly: which slices returned, which stalled, and what remains unverified as a result.\n- Escalate unreconcilable conflicts explicitly rather than silently picking a winner.\n\n## Common pitfalls\n- Committing to one topology upfront and forcing a bad-fit problem into it instead of adapting when the shape becomes clear.\n- Claiming \"consensus\" or \"failure recovery\" when there is no real detection mechanism behind it — describe reconciliation as what it actually is.\n- Splitting work into slices that turn out interdependent, causing subagents to stall waiting on each other with no channel to resolve it.\n- Over-adapting: switching topology or re-splitting work so often that no subagent gets enough runway to finish anything.\n\n## Tools & techniques\n- Task-tool batched dispatch for genuine concurrency across independent slices.\n- Shared-state reconciliation (memory\u002Fnotice-board patterns) instead of assuming peer-to-peer negotiation exists.\n- Explicit re-partition trigger list: stalled agent, contradicted assumption, size mismatch, new dependency discovered.\n- Post-run coverage report distinguishing \"reconciled,\" \"unreconciled conflict,\" and \"no response\" per slice.\n","# Adaptive Coord. II — Best Practices\n\n## Focus\nCoordinates a group of agents whose topology and task split should change mid-run as conditions change — unlike a fixed mesh or hierarchy, this role actively re-partitions work and re-routes based on what's coming back.\n\n## Best practices\n- Treat topology as a decision, not a default — pick hierarchical, mesh, or pipeline per task based on dependency shape, and be willing to switch mid-run if the shape turns out wrong.\n- Re-plan on signal, not on schedule — a stalled subagent, a contradicted assumption, or a much-larger-than-expected slice are all triggers to re-partition immediately.\n- Keep partitions genuinely independent when running in parallel — if two slices need to negotiate mid-flight, that's a sign the topology should be sequential or hierarchical instead.\n- Dispatch all independent slices in a single batch so parallelism is real, not simulated by sequential calls.\n- Reconcile divergent results by re-examining evidence, not by averaging or picking the longest answer — record what each subagent actually found before deciding.\n- Report coverage honestly: which slices returned, which stalled, and what remains unverified as a result.\n- Escalate unreconcilable conflicts explicitly rather than silently picking a winner.\n\n## Common pitfalls\n- Committing to one topology upfront and forcing a bad-fit problem into it instead of adapting when the shape becomes clear.\n- Claiming \"consensus\" or \"failure recovery\" when there is no real detection mechanism behind it — describe reconciliation as what it actually is.\n- Splitting work into slices that turn out interdependent, causing subagents to stall waiting on each other with no channel to resolve it.\n- Over-adapting: switching topology or re-splitting work so often that no subagent gets enough runway to finish anything.\n\n## Tools & techniques\n- Task-tool batched dispatch for genuine concurrency across independent slices.\n- Shared-state reconciliation (memory\u002Fnotice-board patterns) instead of assuming peer-to-peer negotiation exists.\n- Explicit re-partition trigger list: stalled agent, contradicted assumption, size mismatch, new dependency discovered.\n- Post-run coverage report distinguishing \"reconciled,\" \"unreconciled conflict,\" and \"no response\" per slice.\n",{"name":5,"tags":43,"tools":46,"source":47,"license":14,"description":7},[23,44,26,45],"operations","agents",[],"https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind","main","f7cd4595d0f8959822163e14f9787117765c931a","packages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fadaptive-coordinator2",2,"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":57,"prompt":67,"zipAvailable":68},[58,61,64],{"tool":59,"command":60},"npx","npx skills add monoes\u002Fmonomind --skill adaptive-coordinator2",{"tool":62,"command":63},"bunx","bunx skills add monoes\u002Fmonomind --skill adaptive-coordinator2",{"tool":65,"command":66},"pnpm","pnpm dlx skills add monoes\u002Fmonomind --skill adaptive-coordinator2","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fadaptive-coordinator2\u002FSKILL.md",true,[],[71,98,134,159,183,210],{"skillNo":72,"name":73,"nameZh":6,"description":74,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":75,"categories":76,"keywords":77,"publishedAt":97},"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",[],[78,79,80,81,82,83,84,85,86,87,88,89,21,90,91,92,93,94,95,96],"zk","zk steward","steward","zero-knowledge","engineer","choice","design","knowledge","impact","query","snark","review","security review","stark","zettelkasten","cryptography","security","org","blockchain","2026-11-10 13:02:02",{"skillNo":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":102,"licenseFileUrl":103,"sourceHtmlUrl":104,"categories":105,"keywords":116,"publishedAt":97},"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",[106,111],{"categoryNo":107,"categoryName":108,"taxonomyType":109,"taxonomyTypeDesc":110},"01JBSEEDCAT000000000000018","写作与文案","SCENARIO","使用场景",{"categoryNo":112,"categoryName":113,"taxonomyType":114,"taxonomyTypeDesc":115},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[117,118,119,82,120,121,28,122,87,123,124,125,126,127,128,129,130,131,132,133],"writing","writing plans","plans","superpowers","implementation plan","you","code","tasks","plan","product","write","commands","paths","suggest","coding","context","ZeRO",{"skillNo":135,"name":136,"nameZh":6,"description":137,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":138,"sourceHtmlUrl":139,"categories":140,"keywords":144,"publishedAt":97},"01M71PAXFM488X9ARXK0T04TF5","write-spec","Write a feature spec or PRD from a problem statement or feature idea. Use when turning a vague idea or user request into a structured document, scoping a feature with goals and non-goals, defining success metrics and acceptance criteria, or breaking a big ask into a phased spec.","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fwrite-spec\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fwrite-spec\u002FSKILL.md",[141],{"categoryNo":142,"categoryName":143,"taxonomyType":114,"taxonomyTypeDesc":115},"01JBSEEDCAT000000000000031","产品经理",[127,145,146,147,148,85,149,28,150,151,22,126,152,153,154,155,156,157,29,158],"write spec","spec","user request","problem","acceptance","ask","vague","request","scoping","idea","metrics","feature","problem statement","document",{"skillNo":160,"name":161,"nameZh":6,"description":162,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":163,"sourceHtmlUrl":164,"categories":165,"keywords":169,"publishedAt":97},"01M71PASSZY800C0RPEARBS918","write-query","Write optimized SQL for your dialect with best practices. 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",[166],{"categoryNo":167,"categoryName":168,"taxonomyType":114,"taxonomyTypeDesc":115},"01JBSEEDCAT000000000000033","数据分析师",[87,127,170,171,172,173,85,29,28,174,175,176,177,22,20,178,179,180,181,182],"write query","postgres","for","snowflake","best practices","natural-language","with","multi","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":195,"publishedAt":97},"01M71PAPTX0T3PF6ETFVR96SJJ","workflow-automation","Org role guidance for workflow automation: build and maintain CI\u002FCD definitions in GitHub Actions or equivalent: triggers, jobs, matrices, caching. Covers path filters, dependency caching, parallel jobs, timeouts and fail-fast ordering.","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fworkflow-automation\u002FSKILL.md",[189,192],{"categoryNo":190,"categoryName":191,"taxonomyType":109,"taxonomyTypeDesc":110},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":193,"categoryName":194,"taxonomyType":109,"taxonomyTypeDesc":110},"01JBSEEDCAT000000000000023","Skill与智能体开发",[196,197,198,199,172,200,201,202,203,86,204,205,206,87,21,20,207,208,209,179],"Workflow Automation","automation","workflow","parallel","actions","ci","triggers","github actions","maintain","filters","caching","build","dependency","devops",{"skillNo":211,"name":212,"nameZh":6,"description":213,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":214,"categories":215,"keywords":217,"publishedAt":97},"01M71PAM19Z1XTV9NPCFSE2MY8","workflow-architect","Org role guidance for a workflow architect: map full workflow trees for systems, user journeys and agents, covering every branch and failure. Covers discovering workflows from code, handoff contracts, timeouts, recovery paths and observable state.","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fworkflow-architect\u002FSKILL.md",[216],{"categoryNo":193,"categoryName":194,"taxonomyType":109,"taxonomyTypeDesc":110},[198,218,219,220,221,222,223,172,224,225,123,226,227,23,21,20,228,44,179,229],"workflow architect","architect","trees","user journeys","branch","planning","map","workflows","architecture","state","recovery","systems"]