machine-learning
2.5K
28-pipeline-model-architecture
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。
TencentBlueKing
data-ai
sorting
stars
current ordering strategy
query
all entries
refine the visible subset
machine-learning
2.5K
mf
All-in-one Module Federation skill. Use when the user asks anything about MF — concepts, configuration, runtime API, shared dependencies, type errors, runtime error code troubleshooting, slow builds, Bridge integration, or adding MF to an existing project.
module-federation
data-ai
machine-learning
2.4K
ai-models
Research, compare, and update AI model configurations. Covers text model tiers, image generation models, image tool models, pricing data sourcing, and the budget/rate-limit system. Use when bumping model versions, adding new models, updating pricing, or auditing model specs against provider documentation.
gridaco
data-ai
machine-learning
2.4K
evaluation
Evaluates accuracy of quantized or unquantized LLMs using NeMo Evaluator Launcher (NEL). Triggers on "evaluate model", "benchmark accuracy", "run MMLU", "evaluate quantized model", "accuracy drop", "run nel". Handles deployment, config generation, and evaluation execution. Not for quantizing models (use ptq) or deploying/serving models (use deployment).
NVIDIA
data-ai
machine-learning
2.4K
ptq
This skill should be used when the user asks to "quantize a model", "run PTQ", "post-training quantization", "NVFP4 quantization", "FP8 quantization", "INT8 quantization", "INT4 AWQ", "quantize LLM", "quantize MoE", "quantize VLM", or needs to produce a quantized HuggingFace or TensorRT-LLM checkpoint from a pretrained model using ModelOpt.
NVIDIA
data-ai
machine-learning
2.3K
1k-implementing-figma-designs
Implements Figma designs 1:1 using OneKey component library (还原设计稿).
OneKeyHQ
data-ai
machine-learning
2.3K
parameters
This skill should be used when the user asks to "add a parameter", "define parameters", "create an enum parameter", "add a gain control", "add a frequency parameter", "use parameter groups", "randomize parameters", "reset to defaults", "smooth a parameter", "use LogParamSmooth", "create presets", "serialize state", "handle OnParamChange", "copy parameter values", "use InitDouble", "use InitEnum", "use InitBool", "use parameter shapes", "use ShapePowCurve", "parameter flags", or discusses parameter definition, grouping, batch operations, smoothing, presets, or state serialization in an iPlug2 plugin.
iPlug2
data-ai
machine-learning
2.3K
glmv-grounding
A skill that uses GLM-V native grounding capabilities for coordinate conversion, bounding-box visualization, and more. GLM-V native grounding can locate any target specified by the prompt in an image and output relative coordinates normalized to 0-1000 based on image size. Coordinate formats include 2D bounding box (default), 2D points, and 3D bounding box. GLM-V also supports spatiotemporal localization and tracking of multiple prompt-specified targets in videos, outputting 2D bounding boxes per second.
zai-org
data-ai
machine-learning
2.1K
gtm-metrics
When the user wants to define GTM metrics, build a metrics dashboard, measure pipeline efficiency, or track AI product performance. Also use when the user mentions 'GTM metrics,' 'revenue latency,' 'pipeline metrics,' 'TTFV,' 'time-to-first-value,' 'data health,' 'attribution,' 'conversion rate,' 'CAC,' 'LTV,' 'NRR,' 'GTM dashboard,' 'magic number,' 'pipeline velocity,' or 'funnel metrics.' This skill covers GTM measurement from metric selection through dashboard design, including AI-specific cost metrics, attribution models, and weekly review cadences. Do NOT use for technical implementation, code review, or software architecture.
tech-leads-club
data-ai
machine-learning
2K
azure-ai-ml-py
Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines. Triggers: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".
microsoft
data-ai
machine-learning
2K
mflux-model-porting
Port ML models into mflux/MLX with correctness-first validation, then refactor toward mflux style.
filipstrand
data-ai
machine-learning
1.9K
debug-gradient-flow
Diagnose gradient flow issues in training, especially for compiled models (torch.compile/make_fx). Systematically isolates which loss components (energy, force, virial) contribute gradients to which parameters, and identifies where the gradient chain breaks.
deepmodeling
data-ai
machine-learning
1.9K
economy-mode
Shifts Layer 3 model selection to cost-optimized alternatives when economy mode is active.
bradygaster
data-ai
machine-learning
1.9K
model-selection
Per-agent model selection with 4-layer hierarchy and fallback chains
bradygaster
data-ai
machine-learning
1.9K
main2main
Guides adaptation of vLLM-Ascend to upstream vLLM main branch changes. Supports two workflows: (1) Proactive upgrade: analyze vLLM code diff, generate prioritized change report, adapt vllm-ascend code. (2) CI failure diagnosis: when schedule_test_vllm_main CI is red, automatically extract errors from logs, trace root causes to upstream commits, generate diagnostic report, and apply fixes. The skill produces code changes, a report file, and a structured summary. It does NOT perform git/PR operations. After the skill completes in standalone mode, create a branch, commit, and submit a PR using the structured summary as PR body. Use this skill whenever: - The user wants to upgrade/adapt vllm-ascend to a newer vLLM commit - The user shares a GitHub Actions URL or run ID from main2main tests - The user mentions CI failures related to vLLM main branch updates or "main2main" test failures - The user wants to compare vLLM changes and assess impact on vllm-ascend - The user asks to analyze, debug, or fix failures cau
vllm-project
data-ai
machine-learning
1.9K
performing-regression-analysis
Execute this skill empowers AI assistant to perform regression analysis and modeling using the regression-analysis-tool plugin. it analyzes datasets, generates appropriate regression models (linear, polynomial, etc.), validates the models, and provides performa... Use when analyzing code or data. Trigger with phrases like 'analyze', 'review', or 'examine'.
jeremylongshore
data-ai
machine-learning
1.9K
evaluating-machine-learning-models
Build this skill allows AI assistant to evaluate machine learning models using a comprehensive suite of metrics. it should be used when the user requests model performance analysis, validation, or testing. AI assistant can use this skill to assess model accuracy, p... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
jeremylongshore
data-ai
machine-learning
1.9K
engineering-features-for-machine-learning
Execute create, select, and transform features to improve machine learning model performance. Handles feature scaling, encoding, and importance analysis. Use when asked to "engineer features" or "select features". Trigger with relevant phrases based on skill purpose.
jeremylongshore
data-ai
machine-learning
1.9K
mixed-precision-trainer
Manage mixed precision trainer operations. Auto-activating skill for ML Training. Triggers on: mixed precision trainer, mixed precision trainer Part of the ML Training skill category. Use when working with mixed precision trainer functionality. Trigger with phrases like "mixed precision trainer", "mixed trainer", "mixed".
jeremylongshore
data-ai
machine-learning
1.9K
optimizing-prompts
Execute this skill optimizes prompts for large language models (llms) to reduce token usage, lower costs, and improve performance. it analyzes the prompt, identifies areas for simplification and redundancy removal, and rewrites the prompt to be more conci... Use when optimizing performance. Trigger with phrases like 'optimize', 'performance', or 'speed up'.
jeremylongshore
data-ai
machine-learning
1.9K
roc-curve-plotter
Manage roc curve plotter operations. Auto-activating skill for ML Training. Triggers on: roc curve plotter, roc curve plotter Part of the ML Training skill category. Use when working with roc curve plotter functionality. Trigger with phrases like "roc curve plotter", "roc plotter", "roc".
jeremylongshore
data-ai
machine-learning
1.9K
splitting-datasets
Process split datasets into training, validation, and testing sets for ML model development. Use when requesting "split dataset", "train-test split", or "data partitioning". Trigger with relevant phrases based on skill purpose.
jeremylongshore
data-ai
machine-learning
1.9K
training-machine-learning-models
Build train machine learning models with automated workflows. Analyzes datasets, selects model types (classification, regression), configures parameters, trains with cross-validation, and saves model artifacts. Use when asked to "train model" or "evalua... Trigger with relevant phrases based on skill purpose.
jeremylongshore
data-ai