[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ERGSCTP8FEZZSPC8MGKTEV":3,"skill-same:01M6ERGSCTP8FEZZSPC8MGKTEV":102},{"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":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":57,"commitSha":58,"skillDirectory":59,"forksCount":60,"repositoryHtmlUrl":61,"repositoryHomepageUrl":62,"ownerHtmlUrl":63,"ownerBlogUrl":6,"ownerDescription":64,"updatedAt":65,"install":66,"resources":79},"01M6ERGSCTP8FEZZSPC8MGKTEV","find-your-level",null,"Interactive quiz that maps your AI\u002FML knowledge to a starting point in the 542-lesson, 20-phase AI Engineering from Scratch curriculum. Trigger phrases: \"where should I start\", \"find my level\", \"what do I know\", \"which phase\", \"assess my knowledge\", \"placement test\", \"skip ahead\"","rohitg00\u002Fai-engineering-from-scratch","rohitg00","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F48523873?v=4","01M6ERFX7W55NRHW3AV85T2MM8","Rohit Ghumare","01M6ERFXCHT482XQFVY40TM2FN",78488,"MIT","https:\u002F\u002Fgithub.com\u002Frohitg00\u002Fai-engineering-from-scratch\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Ffind-your-level\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000039","教师与教育","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"find your level","find","do","point","phase","scratch","my","which","curriculum","From Scratch","assess","quiz","trigger","lesson","test","ai-engineering","maps","interactive","knowledge","engineering","start","onboarding","assessment","ml","ai engineering","2026-11-10 10:12:30",false,"---\nname: find-your-level\nversion: 1.0.0\ndescription: >\n  Interactive quiz that maps your AI\u002FML knowledge to a starting point in the\n  542-lesson, 20-phase AI Engineering from Scratch curriculum.\n  Trigger phrases: \"where should I start\", \"find my level\", \"what do I know\",\n  \"which phase\", \"assess my knowledge\", \"placement test\", \"skip ahead\"\ntags: [assessment, onboarding, curriculum, ai-engineering]\n---\n\n# Find Your Level\n\nYou are administering a placement quiz for the **AI Engineering from Scratch**\ncurriculum (20 phases, 542 lessons). Your job is to figure out where the\nlearner should begin so they skip material they already know and land right\nwhere the challenge starts. Works with any agent.\n\n## Quiz Structure\n\nThere are 5 knowledge areas, 2 questions each, 10 questions total. Present\nthem in rounds of 2 (one round per area). After the learner answers both\nquestions in a round, score that area before moving on.\n\n## Scoring\n\nEach question is worth 1 point (0 = wrong or blank, 1 = correct). Each area\nscores 0-2. Total score ranges from 0 to 10.\n\n## Administering the Quiz\n\nStart by greeting the learner briefly, then jump straight into Round 1. If\nyour environment has a structured question\u002Foption tool, use it for every\nquestion; otherwise present the lettered options as plain text and wait for\nthe reply. After each round, tell the learner their score for that area\n(e.g. \"Math & Statistics: 2\u002F2\") before moving to the next round. Keep\ncommentary short. Do not explain the answers until the very end.\n\n### Answer isolation\n\nThe answer key is intentionally stored in `references\u002Fanswer-key.md`, outside\nthis quiz body. Do not read that reference before the learner submits both\nanswers for the current round. Then read only that round's key, score it, and\nkeep its explanation private until all five rounds are complete. Do not preload\nlater rounds.\n\nNever put a real answer letter, a likely answer, or the answer distribution in\na reply-format example. For plain text, use this neutral prompt exactly:\n`Reply with Q1: \u003Cletter>, Q2: \u003Cletter>.` Substitute the current question\nnumbers, but keep both values as `\u003Cletter>`.\n\n---\n\n### Round 1 -- Math & Statistics\n\n**Q1.** You have two vectors, a = [1, 2, 3] and b = [4, 5, 6]. What is their\ndot product?\n\n- A) 32\n- B) 21\n- C) 15\n- D) 27\n\n**Q2.** A fair coin is flipped 3 times. What is the probability of getting\nexactly 2 heads?\n\n- A) 1\u002F4\n- B) 1\u002F2\n- C) 1\u002F8\n- D) 3\u002F8\n\n---\n\n### Round 2 -- Classical ML\n\n**Q3.** In a classification task with 90% negative and 10% positive samples,\na model predicts everything as negative. What is its accuracy?\n\n- A) 50%\n- B) 90%\n- C) 10%\n- D) 0%\n\n**Q4.** Which of the following is a hyperparameter of a Random Forest?\n\n- A) The learned split thresholds\n- B) The leaf node predictions\n- C) The number of trees\n- D) The Gini impurity at each node\n\n---\n\n### Round 3 -- Deep Learning\n\n**Q5.** During backpropagation, what does the chain rule compute?\n\n- A) The loss gradient for each trainable weight\n- B) The best learning rate for the current optimizer\n- C) The exact number of layers the network requires\n- D) The batch size used for each training step\n\n**Q6.** What problem do residual connections (skip connections) in ResNet\nprimarily address?\n\n- A) Poor generalization on small training datasets\n- B) Slow loading of batches from persistent storage\n- C) High activation memory during model inference\n- D) Weak gradient flow through very deep networks\n\n---\n\n### Round 4 -- NLP & Transformers\n\n**Q7.** In the Transformer architecture, what does the attention mechanism\ncompute between?\n\n- A) Pixels and labels\n- B) Encoder and Decoder only\n- C) Queries, Keys, and Values\n- D) Embeddings and positions only\n\n**Q8.** What is the main benefit of LoRA (Low-Rank Adaptation) when\nfine-tuning a large language model?\n\n- A) It retrains every base-model parameter from a completely fresh initialization\n- B) It trains low-rank adapters while the base-model weights stay frozen\n- C) It removes the need for labeled examples or task-specific training data\n- D) It duplicates the model layers to increase its adaptation capacity\n\n---\n\n### Round 5 -- Applied AI\n\n**Q9.** In a RAG (Retrieval-Augmented Generation) system, what happens before\nthe LLM generates an answer?\n\n- A) Relevant documents are retrieved and added to the model prompt\n- B) The whole model is fully retrained on the user's current question\n- C) The user selects every context passage before each model request\n- D) The model searches only its pretrained parameter values\n\n**Q10.** In a multi-agent system, what is the primary purpose of a\n\"coordinator\" or \"orchestrator\" agent?\n\n- A) To replace every specialist agent with one general-purpose model\n- B) To assign tasks, route messages, and coordinate the other agents\n- C) To maximize token usage across every agent interaction\n- D) To keep an identical backup model ready for system failures\n\n---\n\n## After All 5 Rounds\n\nDisplay the area breakdown and total:\n\n```text\nMath & Statistics:    X\u002F2\nClassical ML:         X\u002F2\nDeep Learning:        X\u002F2\nNLP & Transformers:   X\u002F2\nApplied AI:           X\u002F2\n----------------------------\nTotal:                X\u002F10\n```\n\n## Score-to-Entry-Point Mapping\n\n| Total Score | Entry Point | What It Means |\n|-------------|-------------|---------------|\n| 0-3 | Phase 1: Math Foundations | Start from the ground up |\n| 4-5 | Phase 3: Deep Learning Core | You have math and ML basics |\n| 6-7 | Phase 7: Transformers Deep Dive | You know DL, time for transformers |\n| 8-9 | Phase 11: LLM Engineering | Strong foundations, go straight to LLM apps |\n| 10 | Phase 14: Agent Engineering | You know it all, build agents |\n\n## Personalized Learning Path\n\nAfter revealing the entry point, generate a markdown table covering all 20\nphases. Use the score to determine the status of each phase. Phases below the\nentry point get \"Skip\" (the learner already knows the material). Phases at or\nabove the entry point get \"Do\". If a learner scored 1\u002F2 in an area that maps\nto a skippable phase, mark that phase as \"Review\" instead of \"Skip\".\n\nArea-to-phase mapping for review detection:\n- Math & Statistics (1\u002F2) -> mark Phase 1 as \"Review\"\n- Classical ML (1\u002F2) -> mark Phase 2 as \"Review\"\n- Deep Learning (1\u002F2) -> mark Phase 3 as \"Review\"\n- NLP & Transformers (1\u002F2) -> mark Phases 5 and 7 as \"Review\"\n- Applied AI (1\u002F2) -> mark Phase 14 as \"Review\"\n\nRead the time estimates from ROADMAP.md (the canonical source of truth). Each\nphase heading contains the estimated hours in the format `(~N hours)`. Parse\nthese values instead of using hardcoded numbers. This ensures the learning path\nstays in sync with the roadmap as estimates are updated. If the repo is not\ncloned locally, fetch it from\n`https:\u002F\u002Fraw.githubusercontent.com\u002Frohitg00\u002Fai-engineering-from-scratch\u002Fmain\u002FROADMAP.md`.\n\n## Output Format\n\nGenerate the table like this:\n\n```markdown\n| Phase | Name | Status | Est. Hours |\n|-------|------|--------|------------|\n| 0 | Setup & Tooling | Skip | -- |\n| 1 | Math Foundations | Review | 30 |\n| 2 | ML Fundamentals | Skip | -- |\n| 3 | Deep Learning Core | Do | 20 |\n| ... | ... | ... | ... |\n```\n\nRules for the table:\n- \"Skip\" phases show `--` for hours (they do not count toward the total)\n- \"Review\" phases show full hours (the learner should skim them)\n- \"Do\" phases show full hours\n- Phase 0 (Setup & Tooling) is always \"Skip\" regardless of score (it is\n  tooling setup, not knowledge)\n- Sum the hours for \"Review\" and \"Do\" phases and show the total at the bottom\n\nAfter the table, add one sentence with the estimated total: \"Your personalized\npath: ~X hours across Y phases.\"\n\nThen add a brief recommendation: which phase to start with, and what to focus\non first based on their weakest area.\n\nFinally, offer the next step: `\u002Fstart-learning` saves this placement into a\npersistent `LEARNING.md` study plan, and `\u002Flearn` starts the first lesson,\ntaught interactively.\n","# Find Your Level\n\nYou are administering a placement quiz for the **AI Engineering from Scratch**\ncurriculum (20 phases, 542 lessons). Your job is to figure out where the\nlearner should begin so they skip material they already know and land right\nwhere the challenge starts. Works with any agent.\n\n## Quiz Structure\n\nThere are 5 knowledge areas, 2 questions each, 10 questions total. Present\nthem in rounds of 2 (one round per area). After the learner answers both\nquestions in a round, score that area before moving on.\n\n## Scoring\n\nEach question is worth 1 point (0 = wrong or blank, 1 = correct). Each area\nscores 0-2. Total score ranges from 0 to 10.\n\n## Administering the Quiz\n\nStart by greeting the learner briefly, then jump straight into Round 1. If\nyour environment has a structured question\u002Foption tool, use it for every\nquestion; otherwise present the lettered options as plain text and wait for\nthe reply. After each round, tell the learner their score for that area\n(e.g. \"Math & Statistics: 2\u002F2\") before moving to the next round. Keep\ncommentary short. Do not explain the answers until the very end.\n\n### Answer isolation\n\nThe answer key is intentionally stored in `references\u002Fanswer-key.md`, outside\nthis quiz body. Do not read that reference before the learner submits both\nanswers for the current round. Then read only that round's key, score it, and\nkeep its explanation private until all five rounds are complete. Do not preload\nlater rounds.\n\nNever put a real answer letter, a likely answer, or the answer distribution in\na reply-format example. For plain text, use this neutral prompt exactly:\n`Reply with Q1: \u003Cletter>, Q2: \u003Cletter>.` Substitute the current question\nnumbers, but keep both values as `\u003Cletter>`.\n\n---\n\n### Round 1 -- Math & Statistics\n\n**Q1.** You have two vectors, a = [1, 2, 3] and b = [4, 5, 6]. What is their\ndot product?\n\n- A) 32\n- B) 21\n- C) 15\n- D) 27\n\n**Q2.** A fair coin is flipped 3 times. What is the probability of getting\nexactly 2 heads?\n\n- A) 1\u002F4\n- B) 1\u002F2\n- C) 1\u002F8\n- D) 3\u002F8\n\n---\n\n### Round 2 -- Classical ML\n\n**Q3.** In a classification task with 90% negative and 10% positive samples,\na model predicts everything as negative. What is its accuracy?\n\n- A) 50%\n- B) 90%\n- C) 10%\n- D) 0%\n\n**Q4.** Which of the following is a hyperparameter of a Random Forest?\n\n- A) The learned split thresholds\n- B) The leaf node predictions\n- C) The number of trees\n- D) The Gini impurity at each node\n\n---\n\n### Round 3 -- Deep Learning\n\n**Q5.** During backpropagation, what does the chain rule compute?\n\n- A) The loss gradient for each trainable weight\n- B) The best learning rate for the current optimizer\n- C) The exact number of layers the network requires\n- D) The batch size used for each training step\n\n**Q6.** What problem do residual connections (skip connections) in ResNet\nprimarily address?\n\n- A) Poor generalization on small training datasets\n- B) Slow loading of batches from persistent storage\n- C) High activation memory during model inference\n- D) Weak gradient flow through very deep networks\n\n---\n\n### Round 4 -- NLP & Transformers\n\n**Q7.** In the Transformer architecture, what does the attention mechanism\ncompute between?\n\n- A) Pixels and labels\n- B) Encoder and Decoder only\n- C) Queries, Keys, and Values\n- D) Embeddings and positions only\n\n**Q8.** What is the main benefit of LoRA (Low-Rank Adaptation) when\nfine-tuning a large language model?\n\n- A) It retrains every base-model parameter from a completely fresh initialization\n- B) It trains low-rank adapters while the base-model weights stay frozen\n- C) It removes the need for labeled examples or task-specific training data\n- D) It duplicates the model layers to increase its adaptation capacity\n\n---\n\n### Round 5 -- Applied AI\n\n**Q9.** In a RAG (Retrieval-Augmented Generation) system, what happens before\nthe LLM generates an answer?\n\n- A) Relevant documents are retrieved and added to the model prompt\n- B) The whole model is fully retrained on the user's current question\n- C) The user selects every context passage before each model request\n- D) The model searches only its pretrained parameter values\n\n**Q10.** In a multi-agent system, what is the primary purpose of a\n\"coordinator\" or \"orchestrator\" agent?\n\n- A) To replace every specialist agent with one general-purpose model\n- B) To assign tasks, route messages, and coordinate the other agents\n- C) To maximize token usage across every agent interaction\n- D) To keep an identical backup model ready for system failures\n\n---\n\n## After All 5 Rounds\n\nDisplay the area breakdown and total:\n\n```text\nMath & Statistics:    X\u002F2\nClassical ML:         X\u002F2\nDeep Learning:        X\u002F2\nNLP & Transformers:   X\u002F2\nApplied AI:           X\u002F2\n----------------------------\nTotal:                X\u002F10\n```\n\n## Score-to-Entry-Point Mapping\n\n| Total Score | Entry Point | What It Means |\n|-------------|-------------|---------------|\n| 0-3 | Phase 1: Math Foundations | Start from the ground up |\n| 4-5 | Phase 3: Deep Learning Core | You have math and ML basics |\n| 6-7 | Phase 7: Transformers Deep Dive | You know DL, time for transformers |\n| 8-9 | Phase 11: LLM Engineering | Strong foundations, go straight to LLM apps |\n| 10 | Phase 14: Agent Engineering | You know it all, build agents |\n\n## Personalized Learning Path\n\nAfter revealing the entry point, generate a markdown table covering all 20\nphases. Use the score to determine the status of each phase. Phases below the\nentry point get \"Skip\" (the learner already knows the material). Phases at or\nabove the entry point get \"Do\". If a learner scored 1\u002F2 in an area that maps\nto a skippable phase, mark that phase as \"Review\" instead of \"Skip\".\n\nArea-to-phase mapping for review detection:\n- Math & Statistics (1\u002F2) -> mark Phase 1 as \"Review\"\n- Classical ML (1\u002F2) -> mark Phase 2 as \"Review\"\n- Deep Learning (1\u002F2) -> mark Phase 3 as \"Review\"\n- NLP & Transformers (1\u002F2) -> mark Phases 5 and 7 as \"Review\"\n- Applied AI (1\u002F2) -> mark Phase 14 as \"Review\"\n\nRead the time estimates from ROADMAP.md (the canonical source of truth). Each\nphase heading contains the estimated hours in the format `(~N hours)`. Parse\nthese values instead of using hardcoded numbers. This ensures the learning path\nstays in sync with the roadmap as estimates are updated. If the repo is not\ncloned locally, fetch it from\n`https:\u002F\u002Fraw.githubusercontent.com\u002Frohitg00\u002Fai-engineering-from-scratch\u002Fmain\u002FROADMAP.md`.\n\n## Output Format\n\nGenerate the table like this:\n\n```markdown\n| Phase | Name | Status | Est. Hours |\n|-------|------|--------|------------|\n| 0 | Setup & Tooling | Skip | -- |\n| 1 | Math Foundations | Review | 30 |\n| 2 | ML Fundamentals | Skip | -- |\n| 3 | Deep Learning Core | Do | 20 |\n| ... | ... | ... | ... |\n```\n\nRules for the table:\n- \"Skip\" phases show `--` for hours (they do not count toward the total)\n- \"Review\" phases show full hours (the learner should skim them)\n- \"Do\" phases show full hours\n- Phase 0 (Setup & Tooling) is always \"Skip\" regardless of score (it is\n  tooling setup, not knowledge)\n- Sum the hours for \"Review\" and \"Do\" phases and show the total at the bottom\n\nAfter the table, add one sentence with the estimated total: \"Your personalized\npath: ~X hours across Y phases.\"\n\nThen add a brief recommendation: which phase to start with, and what to focus\non first based on their weakest area.\n\nFinally, offer the next step: `\u002Fstart-learning` saves this placement into a\npersistent `LEARNING.md` study plan, and `\u002Flearn` starts the first lesson,\ntaught interactively.\n",{"name":5,"tags":54,"version":55,"description":56},[46,45,32,39],"1.0.0","Interactive quiz that maps your AI\u002FML knowledge to a starting point in the 542-lesson, 20-phase AI Engineering from Scratch curriculum. Trigger phrases: \"where should I start\", \"find my level\", \"what do I know\", \"which phase\", \"assess my knowledge\", \"placement test\", \"skip ahead\"\n","main","fe6c657dfb8d02cc898fcee7d4cfbf663aae1416",".claude\u002Fskills\u002Ffind-your-level",13971,"https:\u002F\u002Fgithub.com\u002Frohitg00\u002Fai-engineering-from-scratch","https:\u002F\u002Faiengineeringfromscratch.com","https:\u002F\u002Fgithub.com\u002Frohitg00","Engineer | DevRel | GDE | CNCF, AAIF, Claude, Devin, PE Ambassador | Docker Captain | AWS CB | Ex - iii, Cloudera, Solo.io, Cerbos, Oracle, Reliance Jio","2026-11-11 20:45:16",{"source":8,"skillName":5,"commands":67,"prompt":77,"zipAvailable":78},[68,71,74],{"tool":69,"command":70},"npx","npx skills add rohitg00\u002Fai-engineering-from-scratch --skill find-your-level",{"tool":72,"command":73},"bunx","bunx skills add rohitg00\u002Fai-engineering-from-scratch --skill find-your-level",{"tool":75,"command":76},"pnpm","pnpm dlx skills add rohitg00\u002Fai-engineering-from-scratch --skill find-your-level","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Frohitg00\u002Fai-engineering-from-scratch\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Ffind-your-level\u002FSKILL.md",true,[80,86,90,94,98],{"path":81,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":84,"sourceHtmlUrl":85},"agents\u002Fopenai.yaml","REFERENCE","参考文档",377,"https:\u002F\u002Fgithub.com\u002Fmatt-riley\u002Fagent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fgoreleaser-release-pipeline\u002Fagents\u002Fopenai.yaml",{"path":87,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":88,"sourceHtmlUrl":89},"evals\u002Fevals.json",5009,"https:\u002F\u002Fgithub.com\u002Fmatt-riley\u002Fagent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fgoreleaser-release-pipeline\u002Fevals\u002Fevals.json",{"path":91,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":92,"sourceHtmlUrl":93},"evals\u002Ftrigger-queries.json",1682,"https:\u002F\u002Fgithub.com\u002Fmatt-riley\u002Fagent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fgoreleaser-release-pipeline\u002Fevals\u002Ftrigger-queries.json",{"path":95,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":96,"sourceHtmlUrl":97},"references\u002Fanswer-key.md",1490,"https:\u002F\u002Fgithub.com\u002Frohitg00\u002Fai-engineering-from-scratch\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Ffind-your-level\u002Freferences\u002Fanswer-key.md",{"path":99,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":100,"sourceHtmlUrl":101},"references\u002Frelease-please-goreleaser-config.md",2907,"https:\u002F\u002Fgithub.com\u002Fmatt-riley\u002Fagent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fgoreleaser-release-pipeline\u002Freferences\u002Frelease-please-goreleaser-config.md",[103,138,167,193,221,242],{"skillNo":104,"name":105,"nameZh":6,"description":106,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":107,"categories":108,"keywords":109,"publishedAt":49},"01M6ERNQB1QMAD1FH5Z5ASYHXZ","write-manual","Writes a long technical manual that explains one subject at one pinned version, from its purpose to each request and response. Every listing comes from an offline capture, every claim traces to a ranked source, and an audit must report TOTAL 0. 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Interviews the learner, runs the placement quiz, and writes LEARNING.md, a persistent study plan the learn skill drives. Trigger phrases: \"start learning\", \"set up the course\", \"begin the curriculum\", \"onboard me\", \"create my learning plan\"","https:\u002F\u002Fgithub.com\u002Frohitg00\u002Fai-engineering-from-scratch\u002Fblob\u002Fmain\u002Fskills\u002Fstart-learning\u002FSKILL.md",[144,149,152],{"categoryNo":145,"categoryName":146,"taxonomyType":147,"taxonomyTypeDesc":148},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",{"categoryNo":150,"categoryName":151,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000038","人力资源",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[44,154,155,156,39,157,158,159,48,160,161,162,163,164,32,165,36,166,30,136],"learning","start learning","SET","time","up","learn","create","plan","me","course","learner","study","interviews",{"skillNo":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":171,"categories":172,"keywords":173,"publishedAt":49},"01M6ERNECV0WVSTXHX8KRF9MBJ","motion-figure","Builds figures that move or respond to the reader, in three tiers: SMIL animations and slider widgets inside lessons, interactive mechanism figures that show the named parts of a real system with a computed verdict, and ordered dither bands in the course palette. Triggers: \"animated figure\", \"add a slider widget\", \"interactive figure\", \"show how this system works\", \"make this diagram move\", \"dither masthead\", \"mechanism figure\", \"figure fence\".","https:\u002F\u002Fgithub.com\u002Frohitg00\u002Fai-engineering-from-scratch\u002Fblob\u002Fmain\u002Fskills\u002Fmotion-figure\u002FSKILL.md",[],[174,175,176,177,178,179,180,181,182,183,119,184,185,163,186,187,188,189,41,190,191,192],"motion","motion figure","Animation","show","move","respond","palette","diagram","widgets","how","make","figures","SVG","three","system","triggers","with","animations","lessons",{"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":202,"publishedAt":49},"01M6ERNAC0DQ8ER3A99VQBXNNZ","mcpa-certification","AI-native tutor and onboarding workflow for the MCPA (Model Context Protocol Associate) certification in AI Engineering from Scratch. Use when a learner wants to prepare for the MCPA, resume their certification path, learn the next lesson interactively, run and verify practical labs, take the diagnostic or a full mock, or remediate weak exam domains from GitHub with Claude Code, Codex, ChatGPT, Cursor, or another agent.","https:\u002F\u002Fgithub.com\u002Frohitg00\u002Fai-engineering-from-scratch\u002Fblob\u002Fmain\u002Fskills\u002Fmcpa-certification\u002FSKILL.md",[199,200,201],{"categoryNo":145,"categoryName":146,"taxonomyType":147,"taxonomyTypeDesc":148},{"categoryNo":150,"categoryName":151,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[203,204,37,205,206,207,208,159,209,210,211,48,212,213,214,215,216,217,218,219,220],"mcpa certification","certification","practical","model","domains","exam","workflow","code","model context protocol","native","next","run","take","chatgpt","full","context","ai-native","when",{"skillNo":222,"name":223,"nameZh":6,"description":224,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":225,"categories":226,"keywords":228,"publishedAt":49},"01M6ERN5TAAAJ7FE9N86SX6M2M","lesson-screen","Opens a lesson screen in the browser next to the terminal while a course tutor teaches. It shows the lesson's registered figures, code traces with real values, and clickable predictions. It also builds a figure for the lesson on the fly with the course figure skills and any figure skill the host has installed: a blueprint, a mechanism, an isometric machine, or a 3D model. Clicks return to the tutor as events. Trigger phrases: \"open the lesson screen\", \"show me this visually\", \"show this in 3D\", \"draw the architecture\", \"visualize this lesson\", \"build a figure for this lesson\", \"lesson screen\".","https:\u002F\u002Fgithub.com\u002Frohitg00\u002Fai-engineering-from-scratch\u002Fblob\u002Fmain\u002Fskills\u002Flesson-screen\u002FSKILL.md",[227],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[37,229,230,177,231,206,232,233,234,235,236,210,237,238,162,213,239,240,241,185],"screen","lesson screen","3D","open","host","Blueprint","predictions","show me","architecture","browser","terminal","fly","events",{"skillNo":243,"name":159,"nameZh":6,"description":244,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":245,"categories":246,"keywords":250,"publishedAt":49},"01M6ERN1HS8HW1TRJVX5P7SPY0","Interactive lesson tutor for the AI Engineering from Scratch curriculum. Reads LEARNING.md, fetches the next lesson, teaches it section by section in the terminal, quizzes at the end, and records progress. Works cloned or entirely over raw.githubusercontent.com — no setup required. Trigger phrases: \"next lesson\", \"teach me\", \"continue the course\", \"let's learn\", \"resume learning\"","https:\u002F\u002Fgithub.com\u002Frohitg00\u002Fai-engineering-from-scratch\u002Fblob\u002Fmain\u002Fskills\u002Flearn\u002FSKILL.md",[247,248,249],{"categoryNo":145,"categoryName":146,"taxonomyType":147,"taxonomyTypeDesc":148},{"categoryNo":150,"categoryName":151,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[159,37,39,251,48,162,252,213,239,163,32,36,253,254,154,41,255,33,134,136,137,29],"end","progress","records","continue","interactive-learning"]