[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6RB5K56AT0A25XJM2D3PRND":3,"skill-same:01M6RB5K56AT0A25XJM2D3PRND":77},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":49,"commitSha":50,"skillDirectory":51,"forksCount":52,"repositoryHtmlUrl":53,"repositoryHomepageUrl":6,"ownerHtmlUrl":54,"ownerBlogUrl":55,"ownerDescription":56,"updatedAt":57,"install":58,"resources":71},"01M6RB5K56AT0A25XJM2D3PRND","figure-it-out",null,"Design an auditable playbook when no narrower one fits: a large migration, an ambitious multi-part change, or work a human reviews after stepping away. Scales rigor to the task, runs a hypothesis loop, and logs decisions via show-me-your-work. Use for \u002Ffigure-it-out, 'figure it out', a large migration, or when no narrower playbook applies.","Nairon-AI\u002Fagentic-starter-pack","Nairon-AI","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F260662138?v=4","01M6R8PZ1Y8CSQGFN61VACZGTH","Nairon AI","01M6R8PZ6184VR3BA88YZFBC3N",4,"MIT","https:\u002F\u002Fgithub.com\u002FNairon-AI\u002Fagentic-starter-pack\u002Fblob\u002Fmain\u002Fplanning\u002Ffigure-it-out\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"it","figure it out","show","when","for","reviews","multi","via","human","me","task","design","migration","use","decisions","work","loop","playbook","logs","change","2026-11-10 10:12:30",false,"---\nname: figure-it-out\ndescription: \"Design an auditable playbook when no narrower one fits: a large migration, an ambitious multi-part change, or work a human reviews after stepping away. Scales rigor to the task, runs a hypothesis loop, and logs decisions via show-me-your-work. Use for \u002Ffigure-it-out, 'figure it out', a large migration, or when no narrower playbook applies.\"\n---\n\n# Figure it out\n\nWhen the task matches no playbook, design one. The deliverable before any code is the workflow itself: a sequence of phases that scales rigor to the task, runs the scientific method, and leaves a decision trail a human can audit after stepping away. Bias toward more rigor. The cost of building the wrong thing dwarfs the cost of being careful.\n\nDo not reinvent a playbook already present in the repo. Route focused work to narrower skills such as `diagnose`, `rca`, `speedup-proof`, `prototype`, `implement`, or `plan`. Use this skill for large cross-cutting work or work the user will review after stepping away. The rigor and audit trail are the point.\n\n## Start\n\nUse the environment's plan or task tracker. Make the first item read `brain\u002Fprinciples.md` and the linked principles relevant to this task. Then add the phases below.\n\n## Phase A: Frame\n\nGround first, then commit. Don't start the run until you can state:\n\n- The definition of done as a falsifiable predicate. Follow `brain\u002Fprinciples\u002Fprove-it-works.md`; \"done well\" must be checkable.\n- Scope, quantified: rough units and effort, plus the blockers grounding surfaced. Raise them before spending hours, not after fifty doomed commits.\n- The rigor level, biased high. One-way doors and high blast radius get more; reversible low-stakes steps get less. Rigor is gates and artifacts, not \"try harder\".\n\nPresent the framing and tradeoffs before committing to a long run. Follow `brain\u002Fprinciples\u002Fnever-block-on-the-human.md` for reversible work, but keep one checkpoint before a multi-hour run.\n\n## Phase B: Design the workflow\n\nDecompose into atomic, independently landable units. Sequence riskiest-unknown-first so option value stays high. Follow `brain\u002Fprinciples\u002Ffoundational-thinking.md`: scaffold and verification precede features.\n\n- Build the verification harness before the work, with the baseline captured from the pre-change state, so the check reads as \"old value vs new value\".\n- For one-way-door design decisions, run `architect` (which uses `arena`) with diverse, isolated candidates and an independent judge. Skip it for mechanical work whose shape is concrete. Do not rerun an arena over a settled design without new evidence.\n- Decide what fans out. Parallelize only across genuine seams. Give each worker isolated files or state; use worktrees or branches only when authorized and useful. Follow `brain\u002Fprinciples\u002Fserialize-shared-state-mutations.md`.\n- Write the designed phase list down. That list is what the human reviews.\n\nThen put the design into motion. Add concrete workflow steps to the tracker. Run each under the Phase C loop discipline and append a Phase D log row as each step lands.\n\n## Phase C: Run the loop\n\nEach unit is an experiment: state the hypothesis, make the smallest change, measure against the predicate on the real artifact, keep it if it advanced, and undo only that unit in a recoverable way if it did not. Preserve unrelated user changes.\nApply `principle-sequence-verifiable-units`, verifying each unit before starting the next instead of batching checks at the end.\n\n- Verify by inspecting the artifact, never a self-report. When something passes too easily, suspect the observation method before the system. A blank screenshot passes a lazy gate.\n- Pair delegated work with a judge and audit the delegates' artifacts yourself before trusting them. If a worker games the gate, reset and harden the contract. If the gate itself is wrong, fix the gate in its own change rather than routing around it.\n- A verdict is VERIFIED, NOT VERIFIED, or INCONCLUSIVE. Inconclusive is not a pass. Don't hide a negative.\n\n## Phase D: Keep the audit trail\n\nLog the run via `show-me-your-work`: one canonical TSV with a row per decision and unit, evidence as links. Commit the trail only when confidence must be shown in review. Prefer evidence from scripts a reviewer can rerun.\n\n## Phase E: Verify and hand back\n\nCheck the whole against the Phase A predicate on the real product, not just the harness. Follow `brain\u002Fprinciples\u002Fencode-lessons-in-structure.md`: turn recurring corrections into a gate, lint rule, check, or script.\n\n**Reply:** the playbook you designed, the rigor level and why, the decision-trail path, what's verified against the predicate, and what's still open.\n","# Figure it out\n\nWhen the task matches no playbook, design one. The deliverable before any code is the workflow itself: a sequence of phases that scales rigor to the task, runs the scientific method, and leaves a decision trail a human can audit after stepping away. Bias toward more rigor. The cost of building the wrong thing dwarfs the cost of being careful.\n\nDo not reinvent a playbook already present in the repo. Route focused work to narrower skills such as `diagnose`, `rca`, `speedup-proof`, `prototype`, `implement`, or `plan`. Use this skill for large cross-cutting work or work the user will review after stepping away. The rigor and audit trail are the point.\n\n## Start\n\nUse the environment's plan or task tracker. Make the first item read `brain\u002Fprinciples.md` and the linked principles relevant to this task. Then add the phases below.\n\n## Phase A: Frame\n\nGround first, then commit. Don't start the run until you can state:\n\n- The definition of done as a falsifiable predicate. Follow `brain\u002Fprinciples\u002Fprove-it-works.md`; \"done well\" must be checkable.\n- Scope, quantified: rough units and effort, plus the blockers grounding surfaced. Raise them before spending hours, not after fifty doomed commits.\n- The rigor level, biased high. One-way doors and high blast radius get more; reversible low-stakes steps get less. Rigor is gates and artifacts, not \"try harder\".\n\nPresent the framing and tradeoffs before committing to a long run. Follow `brain\u002Fprinciples\u002Fnever-block-on-the-human.md` for reversible work, but keep one checkpoint before a multi-hour run.\n\n## Phase B: Design the workflow\n\nDecompose into atomic, independently landable units. Sequence riskiest-unknown-first so option value stays high. Follow `brain\u002Fprinciples\u002Ffoundational-thinking.md`: scaffold and verification precede features.\n\n- Build the verification harness before the work, with the baseline captured from the pre-change state, so the check reads as \"old value vs new value\".\n- For one-way-door design decisions, run `architect` (which uses `arena`) with diverse, isolated candidates and an independent judge. Skip it for mechanical work whose shape is concrete. Do not rerun an arena over a settled design without new evidence.\n- Decide what fans out. Parallelize only across genuine seams. Give each worker isolated files or state; use worktrees or branches only when authorized and useful. Follow `brain\u002Fprinciples\u002Fserialize-shared-state-mutations.md`.\n- Write the designed phase list down. That list is what the human reviews.\n\nThen put the design into motion. Add concrete workflow steps to the tracker. Run each under the Phase C loop discipline and append a Phase D log row as each step lands.\n\n## Phase C: Run the loop\n\nEach unit is an experiment: state the hypothesis, make the smallest change, measure against the predicate on the real artifact, keep it if it advanced, and undo only that unit in a recoverable way if it did not. Preserve unrelated user changes.\nApply `principle-sequence-verifiable-units`, verifying each unit before starting the next instead of batching checks at the end.\n\n- Verify by inspecting the artifact, never a self-report. When something passes too easily, suspect the observation method before the system. A blank screenshot passes a lazy gate.\n- Pair delegated work with a judge and audit the delegates' artifacts yourself before trusting them. If a worker games the gate, reset and harden the contract. If the gate itself is wrong, fix the gate in its own change rather than routing around it.\n- A verdict is VERIFIED, NOT VERIFIED, or INCONCLUSIVE. Inconclusive is not a pass. Don't hide a negative.\n\n## Phase D: Keep the audit trail\n\nLog the run via `show-me-your-work`: one canonical TSV with a row per decision and unit, evidence as links. Commit the trail only when confidence must be shown in review. Prefer evidence from scripts a reviewer can rerun.\n\n## Phase E: Verify and hand back\n\nCheck the whole against the Phase A predicate on the real product, not just the harness. Follow `brain\u002Fprinciples\u002Fencode-lessons-in-structure.md`: turn recurring corrections into a gate, lint rule, check, or script.\n\n**Reply:** the playbook you designed, the rigor level and why, the decision-trail path, what's verified against the predicate, and what's still open.\n",{"name":5,"description":7},"main","c9d0c408715983e993b2f9ebffdbfa7f10153bcb","planning\u002Ffigure-it-out",0,"https:\u002F\u002Fgithub.com\u002FNairon-AI\u002Fagentic-starter-pack","https:\u002F\u002Fgithub.com\u002FNairon-AI","https:\u002F\u002Fwww.naironai.com\u002F","Building the future of Work","2026-09-20 17:40:05",{"source":8,"skillName":5,"commands":59,"prompt":69,"zipAvailable":70},[60,63,66],{"tool":61,"command":62},"npx","npx skills add Nairon-AI\u002Fagentic-starter-pack --skill figure-it-out",{"tool":64,"command":65},"bunx","bunx skills add Nairon-AI\u002Fagentic-starter-pack --skill figure-it-out",{"tool":67,"command":68},"pnpm","pnpm dlx skills add Nairon-AI\u002Fagentic-starter-pack --skill figure-it-out","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FNairon-AI\u002Fagentic-starter-pack\u002Fblob\u002Fmain\u002Fplanning\u002Ffigure-it-out\u002FSKILL.md",true,[72],{"path":73,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":75,"sourceHtmlUrl":76},"LICENSE","许可证",1067,"https:\u002F\u002Fgithub.com\u002FNairon-AI\u002Fagentic-starter-pack\u002Fblob\u002Fmain\u002Fplanning\u002Ffigure-it-out\u002FLICENSE",[78,110,131,152,172,193],{"skillNo":79,"name":80,"nameZh":6,"description":81,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":82,"categories":83,"keywords":92,"publishedAt":44},"01M6RBCG630Y8X99XWR9Q4GYNY","writing-shape","Take a markdown file of raw material and shape it into an article through a conversational session — drafting candidate openings, growing the piece paragraph by paragraph, arguing about format (lists, tables, callouts, quotes) at each step. Use when the user has a pile of notes, fragments, or a rough draft and wants help turning it into something publishable.","https:\u002F\u002Fgithub.com\u002FNairon-AI\u002Fagentic-starter-pack\u002Fblob\u002Fmain\u002Fwriting\u002Fwriting-shape\u002FSKILL.md",[84,87],{"categoryNo":85,"categoryName":86,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":88,"categoryName":89,"taxonomyType":90,"taxonomyTypeDesc":91},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[93,94,95,96,97,98,99,100,101,37,102,103,104,105,24,106,107,108,109,27],"writing","writing shape","shape","notes","help","file","markdown","format","session","take","draft","article","material","about","quotes","at","candidate",{"skillNo":111,"name":112,"nameZh":6,"description":113,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":114,"categories":115,"keywords":118,"publishedAt":44},"01M6RBCCCB1KMF01P5SYGFWBSW","writing-fragments","Grilling session that mines the user for fragments — heterogeneous nuggets of writing (claims, vignettes, sharp sentences, half-thoughts) — and appends them to a single document as raw material for a future article. Use when the user wants to develop ideas before imposing structure, or mentions \"fragments\", \"ideate\", or \"raw material\" for writing.","https:\u002F\u002Fgithub.com\u002FNairon-AI\u002Fagentic-starter-pack\u002Fblob\u002Fmain\u002Fwriting\u002Fwriting-fragments\u002FSKILL.md",[116,117],{"categoryNo":85,"categoryName":86,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":88,"categoryName":89,"taxonomyType":90,"taxonomyTypeDesc":91},[93,119,120,121,122,123,101,37,124,125,126,104,127,105,128,129,28,130,27],"writing fragments","grilling","ideate","develop","user","document","structure","ideas","as","sharp","claims","single",{"skillNo":132,"name":133,"nameZh":6,"description":134,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":135,"categories":136,"keywords":139,"publishedAt":44},"01M6RBC8V7NHHKMTAZB4BPXVYQ","writing-beats","Shape an article as a journey of beats, choose-your-own-adventure style. The user picks a starting beat from the raw material, you write only that beat, then offer options for where to pivot next, beat by beat, until the article reaches a natural end. Use when the user has raw material and wants to assemble it as a narrative rather than an argument.","https:\u002F\u002Fgithub.com\u002FNairon-AI\u002Fagentic-starter-pack\u002Fblob\u002Fmain\u002Fwriting\u002Fwriting-beats\u002FSKILL.md",[137,138],{"categoryNo":85,"categoryName":86,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":88,"categoryName":89,"taxonomyType":90,"taxonomyTypeDesc":91},[93,140,141,142,37,143,144,95,145,146,147,104,148,127,149,105,150,151,28,27],"writing beats","choose","write","assemble","only","journey","next","options","end","style","narrative","argument",{"skillNo":153,"name":154,"nameZh":6,"description":155,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":156,"categories":157,"keywords":160,"publishedAt":44},"01M6RBC58SG5Y5SC4ZV4ZNEFHC","stop-slop","Remove AI writing patterns from prose. Use when drafting, editing, or reviewing text to eliminate predictable AI tells.","https:\u002F\u002Fgithub.com\u002FNairon-AI\u002Fagentic-starter-pack\u002Fblob\u002Fmain\u002Fwriting\u002Fstop-slop\u002FSKILL.md",[158,159],{"categoryNo":85,"categoryName":86,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":88,"categoryName":89,"taxonomyType":90,"taxonomyTypeDesc":91},[161,162,163,93,164,165,166,167,168,37,169,170,171,28,27],"stop","stop slop","slop","content","https","text","editing","remove","prose","patterns","drafts",{"skillNo":173,"name":174,"nameZh":6,"description":175,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":176,"categories":177,"keywords":180,"publishedAt":44},"01M6RBBJTTCJ7KZCP16G9JDFZH","humanizer","Remove signs of AI-generated writing from text. Use when editing or reviewing\ntext to make it sound more natural and human-written. Based on Wikipedia's\ncomprehensive \"Signs of AI writing\" guide. Detects and fixes patterns including:\ninflated symbolism, promotional language, superficial -ing analyses, vague\nattributions, em dash overuse, rule of three, AI vocabulary words, passive\nvoice, negative parallelisms, and filler phrases.","https:\u002F\u002Fgithub.com\u002FNairon-AI\u002Fagentic-starter-pack\u002Fblob\u002Fmain\u002Fwriting\u002Fhumanizer\u002FSKILL.md",[178,179],{"categoryNo":85,"categoryName":86,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":88,"categoryName":89,"taxonomyType":90,"taxonomyTypeDesc":91},[174,93,181,182,166,183,142,184,185,168,186,187,170,188,32,189,190,191,192,27],"code","em dash","read","edit","generated","guide","opencode","three","make","wikipedia","vague","dash",{"skillNo":194,"name":195,"nameZh":6,"description":196,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":197,"categories":198,"keywords":199,"publishedAt":44},"01M6RBBEQKJ5HRH8AV9J85QB1T","feedback","Prepare, deliver, and debrief timely interpersonal feedback using a question-first coaching flow, observable behavior, owned experience, and concrete impact. Use when drafting feedback, coaching managers, rehearsing a feedback conversation, praising specific behavior, or reviewing whether feedback landed.","https:\u002F\u002Fgithub.com\u002FNairon-AI\u002Fagentic-starter-pack\u002Fblob\u002Fmain\u002Fwriting\u002Ffeedback\u002FSKILL.md",[],[195,200,201,202,203,204,37,205,206,207,208,209,27,210],"prepare","flow","first","question","behavior","conversation","coaching","debrief","impact","managers","deliver"]