Train models with verifiers environments using hosted RL or prime-rl. Use when asked to configure RL runs, tune key hyperparameters, diagnose instability, set up difficulty filtering and oversampling, or create practical train and eval loops for new environments.
This skill should be used when the user asks to "follow red team methodology", "perform bug bounty hunting", "automate reconnaissance", "hunt for XSS vulnerabilities", "enumerate subdomains", or needs security researcher techniques and tool configurations from top bug bounty hunters.
Mpx 跨端输出 RN （简称为 Mpx2RN or Mpx2DRN）的样式适配开发指南，当用户问题或上下文中同时包含 Mpx、RN、样式三个关键要素时强制调用，如：Mpx2RN 样式适配、Mpx2RN 样式报错等。当用户问题不涉及 Mpx 跨端输出 RN 或与样式无关时不应调用，如：Mpx 输出小程序相关问题、RN 相关问题等。
Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus
Builds and maintains native Rust/C bindings and FFI layers that connect router-side classifiers and signal evaluation to compiled model runtimes. Use when adding or modifying native model bindings, updating FFI interfaces, or changing how the router calls into compiled classifier code.
Modifies routing policy after signal extraction, including matched-decision logic, candidate-model selection, and downstream looper behavior. Use when changing decision predicates, thresholds, priorities, model ranking, cost or latency routing, or other post-signal routing policy.
Maintains matched-decision predicates, looper policy, and candidate-model selection behavior after signal extraction. Use when the task changes decision trees, thresholds, downstream model ranking, or other routing policy that runs after signals are already produced.
Modifies training-stack workflows, selector or embedding pipelines, evaluation artifacts, or runtime-facing outputs under src/training. Use when changing model selection training, embedding pipelines, evaluation scripts, experiments, or other training outputs that feed runtime behavior.
Modifies training-stack workflows, selector or embedding pipelines, evaluation scripts, and runtime-facing artifact expectations. Use when a primary skill touches src/training, tools/make/models.mk, or training docs for runtime-fed artifacts.
Problem-solving strategies for gradient methods in optimization
Generate and execute a training launch command for FastVideo models
read this skill for a token-efficient summary of the invariant subsystem
read this skill for a token-efficient summary of the simulation subsystem
Create and curate evaluation datasets with ground truth for TruLens
Generate a Dust3D 3D model from a 2D turnaround reference image by tracing body parts as tube-shaped node chains, writing an XML file, and running dust3d to produce a .obj mesh, a .ds3 document, and the source .xml file.
Scaffold a complete knowledge system. Detects platform, conducts conversation, derives configuration, generates everything. Validates against 15 kernel primitives. Triggers on "/setup", "/setup --advanced", "set up my knowledge system", "create my vault".
This skill should be used when the user asks to "learn from Kaggle", "study Kaggle solutions", "analyze Kaggle competitions", or mentions Kaggle competition URLs. Provides access to extracted knowledge from winning Kaggle solutions across NLP, CV, time series, tabular, and multimodal domains.
Diagnose training issues with Tinker — slow steps, hanging sessions, output mismatches, error messages, renderer problems, and deployment issues. Use this skill whenever a user reports that training is slow, steps take too long, sessions are hanging, model outputs differ between Tinker and external engines (vLLM, SGLang), they get a confusing error message, training quality is poor (high KL, bad outputs), or they suspect something is wrong. Also trigger when users ask "is this a Tinker issue or my issue?", "is Tinker down?", report unexpected wait times, see output quality regressions, get opaque errors, or want to profile/debug their training or deployment pipeline. This skill walks through systematic triage to determine root cause.
YOLO 2026 — state-of-the-art real-time object detection
Agent-driven YOLO fine-tuning — annotate, train, export, deploy
BK-CI 流水线核心模型（Model）架构详解，涵盖 Pipeline/Stage/Container/Task 四层结构、模型序列化、版本管理、模型校验。当用户理解流水线数据结构、开发流水线功能、处理模型转换或进行模型扩展时使用。
