「train」の検索結果

Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).

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Assembles microbial-community sequencing into metagenome-assembled genomes (MAGs) with metaFlye (ONT), metaSPAdes/MEGAHIT (Illumina), and hifiasm-meta/metaMDBG (PacBio HiFi), then recovers genomes via multi-binner consolidation (MetaBAT2, MaxBin2, CONCOCT, SemiBin2, VAMB -> DAS_Tool) and QCs them against MIMAG with CheckM2, GUNC, and GTDB-Tk. Covers why a metagenome is not a genome (uneven coverage, micro-diversity, strain collapse to consensus), differential-coverage binning, co-assembly vs per-sample, the rRNA-operon collapse that fails short-read MAGs, and strain resolution with inStrain. Use when reconstructing genomes from a microbiome, soil, ocean, or gut community, recovering MAGs, or resolving strain-level variation.

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This skill should be used when user asks to "improve my mAP", "why is my model overfitting", "my training is diverging", "read my results.csv", "interpret my training curves", "my AP50 is good but AP50-95 is bad", "my recall is low", "how do I pick learning rate", "which augmentations should I use", "should I use a bigger model", "tune hyperparameters", or asks how to train YOLO26 for detection, instance or semantic segmentation, pose, OBB, classification, or depth.

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Generates code that fine-tunes a base model using SageMaker serverless training jobs. Use when the user says "start training", "fine-tune my model", "I'm ready to train", or when the plan reaches the finetuning step. Supports SFT, DPO, RLVR, and RLAIF trainers, including RLVR Lambda reward function and RLAIF custom prompt creation.

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Builds a high-fidelity interactive legal assessment as a single self-contained HTML artifact. Output includes a live countdown timer, contract review tasks with hover-annotated problem clauses, candidate answer textareas, model answers hidden behind reveal blocks, scenario-based legal memo tasks, strategy and function-building questions, and a pre-submission checklist that encodes the marking criteria. Use when the user needs to (1) assess a legal candidate with a realistic timed exercise, (2) train or onboard junior lawyers using problem sets rather than doctrine, (3) help a candidate prepare for a real take-home assessment they are facing, (4) build educational materials for law students, in-house teams, or compliance training, or (5) produce scenario-based training modules on specific legal topics. Triggers on "legal test", "take-home", "mock exam", "contract redline exercise", "candidate assessment", "legal training exercise", "practice test", or similar phrasing even when informal.

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Train a custom anime LoRA on the ANIMA base model with Citron's local Gradio trainer (kohya sd-scripts), <6GB VRAM, character/style LoRAs; covers setup, dataset prep, training params, and using the result in the anima-base workflow

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Train or fine-tune language models with TRL or Unsloth on Hugging Face Jobs, including SFT, DPO, GRPO, reward models, and GGUF conversion. Use for cloud LLM training; use huggingface-vision-trainer for vision tasks.

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Pre-flight checklist for long-running ML training jobs covering config diff, run naming, path verification, monitoring setup, and restart-cleanup. Use when the user asks to launch, kick off, start, restart, or kill a training run, or mentions launching a multi-hour or multi-day GPU job (python train, accelerate launch, torchrun, deepspeed, sbatch, tmux training).

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Automatically complete partial code snippets while satisfying semantic constraints including variable types, invariants, pre/post-conditions, interface contracts, and expected input/output behavior. Use when users provide incomplete code with specific requirements like "complete this function that takes a list and returns sorted unique elements" or "fill in this method body that must maintain the invariant that x stays positive" or "implement this interface method with these type constraints." Produces compilable, executable code with tests and a constraint satisfaction report.

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Train machine learning models end-to-end, covering data loading, preprocessing, architecture selection, training loops, validation, and checkpointing. Use when the user requests model training or provides relevant inputs for this workflow.

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Use when user asks to clone a voice, train a custom voice model, or synthesize speech with a cloned voice. iFlytek Voice Clone tts(声音复刻) — train a custom voice model from audio samples and synthesize speech with the cloned voice. Supports the full workflow: get training text → create task → upload audio → submit training → poll results → synthesize with cloned voice. Pure Python stdlib, no pip dependencies.

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K12 体育培训 (K12 / 青少年体育培训 (从业者视角) — 面向 3-18 岁青少年的校外体育培训生意，政策改革驱动的高增长赛道。覆盖: (a) 业态分层 — 少儿体适能 (3-12 岁基础运动能力/感统/协调) vs 专项培训 (篮球/游泳/跳绳/羽毛球/网球/击剑/武术等单项技能) vs 中考体育备考 (初中冲刺，按各地体育中考考纲提分); (b) 政策脊椎 — 双减 (2021 教育部「双减」: 学科类压减、非学科类规范) 把体育从「兴趣班」推成刚需，体育中考分值改革 (云南/北京/各省把分值提到 50-100 分) 制造确定性需求，《国家学生体质健康标准》+《义务教育体育与健康课程标准 (2022 版)》是教学纲领; (c) 单店模型与连锁扩张 — 坪效/师生比/续费率/转介绍率/课消，直营 vs 加盟，社区店 vs Mall 店 vs 进校 (课后服务/课后三点半); (d) 教研与师资 — 课程体系标准化 (分龄分级)、教练认证 (社会体育指导员/各单项协会教练员/中国少儿体适能等)、教练「能打 ≠ 能教」断层; (e) 运营 SOP — 招生获客 (地推/社群/试听转化)、排课与场地利用率、家长沟通与续费、安全与运动损伤防控/保险; (f) 中考体育教研 — 考纲拆解 (耐力跑/跳绳/引体/坐位体前屈/球类技能)、提分训练计划、体测数据追踪。学派分歧: 体适能通识派 vs 专项技能派、中考应试提分派 vs 长期运动素养派、直营重模型 vs 加盟轻扩张、进校 (toB/课后服务) vs 校外门店 (toC)、引进体系 (如美国 SPARK/体适能认证) vs 本土自研课程。不含: 竞技体校/专业运动员青训选材深水区、成人健身/私教 (见 personal-trainer)、学科类教培、纯体育用品零售。) Master OS — automated mastery of K12 / 青少年体育培训 (从业者视角) — 面向 3-18 岁青少年的校外体育培训生意，政策改革驱动的高增长赛道。覆盖: (a) 业态分层 — 少儿体适能 (3-12 岁基础运动能力/感统/协调) vs 专项培训 (篮球/游泳/跳绳/羽毛球/网球/击剑/武术等单项技能) vs 中考体育备考 (初中冲刺，按各地体育中考考纲提分); (b) 政策脊椎 — 双减 (2021 教育部「双减」: 学科类压减、非学科类规范) 把体育从「兴趣班」推成刚需，体育中考分值改革 (云南/北京/各省把分值提到 50-100 分) 制造确定性需求，《国家学生体质健康标准》+《义务教育体育与健康课程标准 (2022 版)》是教学纲领; (c) 单店模型与连锁扩张 — 坪效/师生比/续费率/转介绍率/课消，直营 vs 加盟，社区店 vs Mall 店 vs 进校 (课后服务/课后三点半); (d) 教研与师资 — 课程体系标准化 (分龄分级)、教练认证 (社会体育指导员/各单项协会教练员/中国少儿体适能等)、教练「能打 ≠ 能教」断层; (e) 运营 SOP — 招生获客 (地推/社群/试听转化)、排课与场地利用率、家长沟通与续费、安全与运动损伤防控/保险; (f) 中考体育教研 — 考纲拆解 (耐力跑/跳绳/引体/坐位体前屈/球类技能)、提分训练计划、体测数据追踪。学派分歧: 体适能通识派 vs 专项技能派、中考应试提分派 vs 长期运动素养派、直营重模型 vs 加盟轻扩张、进校 (toB/课后服务) vs 校外门店 (toC)、引进体系 (如美国 SPARK/体适能认证) vs 本土自研课程。不含: 竞技体校/专业运动员青训选材深水区、成人健身/私教 (见 personal-trainer)、学科类教培、纯体育用品零售。: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on K12 / 青少年体育培训 (从业者视角) — 面向 3-18 岁青少年的校外体育培训生意，政策改革驱动的高增长赛道。覆盖: (a) 业态分层 — 少儿体适能 (3-12 岁基础运动能力/感统/协调) vs 专项培训 (篮球/游泳/跳绳/羽毛球/网球/击剑/武术等单项技能) vs 中考体育备考 (初中冲刺，按各地体育中考考纲提分); (b) 政策脊椎 — 双减 (2021 教育部「双减」: 学科类压减、非学科类规范) 把体育从「兴趣班」推成刚需，体育中考分值改革 (云南/北京/各省把分值提到 50-100 分) 制造确定性需求，《国家学生体质健康标准》+《义务教育体育与健康课程标准 (2022 版)》是教学纲领; (c) 单店模型与连锁扩张 — 坪效/师生比/续费率/转介绍率/课消，直营 vs 加盟，社区店 vs Mall 店 vs 进校 (课后服务/课后三点半); (d) 教研与师资 — 课程体系标准化 (分龄分级)、教练认证 (社会体育指导员/各单项协会教练员/中国少儿体适能等)、教练「能打 ≠ 能教」断层; (e) 运营 SOP — 招生获客 (地推/社群/试听转化)、排课与场地利用率、家长沟通与续费、安全与运动损伤防控/保险; (f) 中考体育教研 — 考纲拆解 (耐力跑/跳绳/引体/坐位体前屈/球类技能)、提分训练计划、体测数据追踪。学派分歧: 体适能通识派 vs 专项技能派、中考应试提分派 vs 长期运动素养派、直营重模型 vs 加盟轻扩张、进校 (toB/课后服务) vs 校外门店 (toC)、引进体系 (如美国 SPARK/体适能认证) vs 本土自研课程。不含: 竞技体校/专业运动员青训选材深水区、成人健身/私教 (见 personal-trainer)、学科类教培、纯体育用品零售。 problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「K12 体育培训」「青少年体育培训」「少儿体适能」「中考体育」「体育中考」

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Odoo 19 development knowledge base with 18 specialized guides covering Actions (ir.actions.*, cron jobs, server actions), Controllers (HTTP routing, endpoints, auth types), Data files (XML/CSV records, shortcuts, noupdate), API Decorators (@api.depends, @api.constrains, @api.ondelete, @api.onchange, @api.model, @api.private), SQL Constraints (models.Constraint replacing _sql_constraints), Database Indexes (models.Index), Module development (manifest, wizards, reports), Field types (Char, Text, Monetary, relational fields), Manifest configuration (__manifest__.py, dependencies, asset bundles), Mixins (mail.thread, mail.activity.mixin, mail.alias.mixin, utm.mixin), ORM Model methods (search, CRUD, domain filters, recordsets, CamelCase model naming), Migration scripts (pre/post/end hooks, data migration), OWL frontend components (hooks, services, lifecycle), Performance optimization (N+1 prevention, batch ops, _read_group), QWeb Reports (PDF/HTML, paper formats, barcodes, t-out), Security/ACL (record rules, field permissions, privilege-based groups, @api.private), Testing (TransactionCase, HttpCase, mocking, query count assertions), Transactions (savepoints, UniqueViolation, serialization failures), Translations (i18n, PO files, translatable fields), XML Views (list/form/search, kanban card templates, xpath inheritance, QWeb templates). Use when writing, reviewing, or debugging any Odoo 19 Python or XML code, creating or modifying modules, fixing performance issues, or looking up Odoo 19 API patterns and best practices. Includes CSS/SCSS asset authoring and review for Odoo addons.

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Establishes a project's quality bar as a written contract and stops agents quietly lowering it. Interviews the user on which dimensions matter, supplies sane default thresholds when they have no number in mind, records everything in CONSTRAINTS.md, and watches the diff for a weakened bar — new @ts-ignore or eslint-disable suppressions, skipped or deleted tests, assertions stripped out, unimplemented stubs, thresholds edited down. Use when no quality bar is written down, when the user says "set up constraints" or "define our standards", when the user wants dimensions they care about — accessibility, web performance, coverage — set up as enforced constraints, when an agent keeps silencing checks or skipping tests to get to green, when you need a coverage or performance threshold and don't know what number to pick, or when an agent writes more code than anyone will read.

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🇺🇸 Reddit Marketing & Growth Playbook 2026 — The complete Reddit operations SOP for AI/SaaS founders. Covers account warming (Karma 0→500 in 20 days), shadow ban prevention, multi-account matrix via fingerprint browsers, subreddit selection across 10K+ communities, content templates (90/10 rule), Product Hunt × Reddit flywheel, AMA strategy. Why this matters NOW: Reddit content is 40.11% of ChatGPT/Claude training data — the highest-weighted English UGC source. Battle-tested by AFFiNE Reddit campaigns generating 3-4万有效曝光 with 5-8% GitHub Star conversion, plus 7 case studies (Adobe 3x conversion, Base44 $80M AMA, SenseNova-U1 multi-sub launch, Starterstory 0→3K users). 🇨🇳 Reddit 运营增长实战手册 — AI/SaaS 出海 founder 的 Reddit 完整 SOP。覆盖账号体系(20天养号 Karma 0→500)、影子封禁防御、指纹浏览器矩阵、10万+ subreddits 筛选、内容模板(90/10 法则)、Product Hunt × Reddit 飞轮、AMA 打法。**为什么现在做:Reddit 内容占 ChatGPT/Claude 训练数据 40.11%,是最高权重的英文 UGC 来源**。基于 AFFiNE Reddit 实战 3-4 万曝光 + 5-8% Star 转化,含 7 个完整案例(Adobe 3x 转化、Base44 $80M AMA、SenseNova-U1 多社区联动、Starterstory 0→3K 用户冷启动)。 🇯🇵 Reddit マーケティング＆グロースプレイブック — AI/SaaS創業者向けの完全なReddit運用SOP。アカウント育成（20日でKarma 0→500）、シャドウBAN防止、フィンガープリントブラウザマトリックス、サブレディット選定、コンテンツテンプレート（90/10ルール）、Product Hunt × Redditフライホイール、AMA戦略をカバー。**重要な理由：Reddit コンテンツは ChatGPT/Claude のトレーニングデータの40.11%を占める英語 UGC 最大の情報源**。AFFiNE Redditキャンペーン3-4万インプレッション＋5-8% GitHubスター変換で実証。7つの実例（Adobe 3倍変換、Base44 $80M AMA、SenseNova-U1、Starterstory 0→3Kユーザー）。 🇰🇷 Reddit 마케팅 & 그로스 플레이북 — AI/SaaS 창업자를 위한 완전한 Reddit 운영 SOP. 계정 워밍업(20일 Karma 0→500), Shadow Ban 방지, 핑거프린트 브라우저 매트릭스, 서브레딧 선정, 콘텐츠 템플릿(90/10 룰), Product Hunt × Reddit 플라이휠, AMA 전략을 다룹니다. **지금 해야 하는 이유: Reddit 콘텐츠는 ChatGPT/Claude 학습 데이터의 40.11%를 차지하는 최대 영어 UGC 소스**. AFFiNE Reddit 캠페인 3-4만 노출 + 5-8% GitHub Star 전환으로 검증. 7가지 사례 연구(Adobe 3배 전환, Base44 $80M AMA, SenseNova-U1, Starterstory 0→3K 사용자). Triggers: "reddit marketing" | "reddit growth" | "reddit ads" | "reddit advertising" | "reddit strategy" | "reddit promotion" | "subreddit marketing" | "reddit playbook" | "reddit SOP" | "reddit shadow ban" | "shadow ban prevention" | "reddit karma" | "reddit AMA" | "reddit product launch" | "reddit AI training data" | "reddit GEO" | "AFFiNE reddit case" | "Base44 AMA" | "SenseNova Reddit" | "subreddit selection" | "r/SaaS posting" | "r/MachineLearning self-promotion" | "fingerprint browser matrix" | "AdsPower reddit" | "reddit account farming" | "reddit 90/10 rule" | "reddit content strategy" | "reddit indie founder" | "reddit cold start" | "Reddit 运营" | "Reddit 营销" | "Reddit 防封" | "Reddit 矩阵号" | "Reddit 出海" | "Reddit 引流" | "Reddit 冷启动" | "影子封禁" | "Reddit Karma" | "Reddit 养号" | "Reddit AMA" | "Reddit マーケティング" | "Reddit グロース" | "Reddit 마케팅" | "Reddit 그로스"

デメテルの法則（最小知識の原則）に基づくコードレビューと設計支援。オブジェクトの連鎖呼び出し （Train Wreck）を検出し、直接の友人とのみ会話する設計へ変換する。結合度の低減と 変更容易性の向上を促進する。コードレビュー、新規実装、リファクタリング時に オブジェクト間の結合が深い場合に使用。 対象言語: Java, Kotlin, Scala, TypeScript, Python, Ruby, Go, Rust。 トリガー：「デメテルの法則」「連鎖呼び出しを減らしたい」「Train Wreckを直して」 「結合度を下げたい」「ドット連鎖が多い」「最小知識の原則」「Law of Demeter」 といったオブジェクト間結合関連リクエストで起動。

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for Kerberos delegation, SPN trust edges, S4U abuse, RBCD, constrained or unconstrained delegation, and service-ticket acceptance. Use when the user asks about constrained delegation, unconstrained delegation, RBCD, S4U, SPNs, ticket acceptance, or how a Kerberos trust edge turns into effective privilege under sandbox assumptions. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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Assess organizational training needs using ADDIE methodology and Kirkpatrick's 4-Level evaluation framework. Maps performance gaps to learning objectives using Bloom's taxonomy. Use when planning L&D budgets, designing training programs, or evaluating training effectiveness.

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Use when a task needs the judgment of an Automotive Engineer — computing weight transfer and tire friction-circle limits for a braking-while-cornering maneuver to set brake-force distribution, sizing a powertrain and gear ratio against a 0-60 mph acceleration target while checking traction versus power limits, sizing a crumple zone's crush distance against FMVSS 208 occupant-deceleration limits, or checking a powertrain-mount or driveline natural frequency against engine firing-order excitation for NVH isolation. Distinct from an automotive-service-technician (diagnoses and repairs a vehicle already designed) — this role owns the system-level design decisions (structure, powertrain sizing, suspension geometry, restraint/crash structure, NVH) that produce the vehicle the technician later services.

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Use this skill when planning or executing the human side of an Agentforce or Einstein AI feature rollout — user trust-building, AI-specific training, structured feedback collection via the Feedback API, and adoption measurement via Agentforce Analytics. NOT for turning the Einstein for Sales features on — use agentforce/agentforce-sales-ai-setup. NOT for rollout training with no AI in scope — use admin/change-management-and-training.

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Personal trainer operations: client check-ins, workout-program delivery, progress reminders, session reminders, lead follow-up, and workout tracking. Use when an owner is a coach/personal trainer or when managing fitness clients and training routines.

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Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker, pair scoring for two-stage retrieval / pair classification), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.

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Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). Supports SFT, DPO, GRPO, KTO, RLOO and Reward Model training via CLI commands.

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Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.

日本語の概要は準備中です。原文の説明を表示しています。