GitHub
reward-function-v330
Use when tuning RL reward function weights, fixing reward hacking, or addressing P&L gradient issues
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tools machine learning AI 技能
发现 tools machine learning 相关 Skills，比较来源仓库和用途，查看适合你工作流的技能说明。
分类目录: tools machine learning
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GitHub
readiness
Evaluate repository readiness for AI agents. Analyzes 81 criteria across 8 pillars, assigns maturity level 1-5, generates visual report.
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GitHub
scale-adaptive
Adjust planning depth and agent behavior based on PRD complexity level
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GitHub
eval-recipes-runner
Run Microsoft's eval-recipes benchmarks to validate amplihack improvements against baseline agents. Activates when testing with eval-recipes, running evals, or benchmarking changes.
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GitHub
explainability
See the main Model Explainability skill for comprehensive XAI coverage.
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GitHub
ground-truth-management
Comprehensive guide to creating, managing, and maintaining ground truth datasets for AI evaluation including annotation, quality control, and versioning
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GitHub
ai-model-registry
Centralized management of machine learning models throughout their lifecycle, including versioning, metadata, and production deployment.
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GitHub
model-serving-inference
Comprehensive guide to deploying and serving LLM models including optimization, batching, caching, and production infrastructure
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GitHub
model-explainability-and-interpretability
Techniques and tools for understanding how machine learning models make decisions and explaining those decisions to stakeholders.
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GitHub
confidence-scoring
See the main Model Explainability skill for comprehensive coverage of confidence scoring and calibration.
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GitHub
ai-data-privacy
Protecting personal and sensitive data throughout the machine learning lifecycle, from training to inference.
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GitHub
model-bias-and-fairness
Identifying, measuring, and mitigating algorithmic bias to ensure equitable outcomes in AI systems.
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GitHub
ai-feature-template
Create new AI-powered features using xAI Grok. Use when user mentions "new AI feature", "add Grok", "create prompt", "AI analysis", or "generate with AI".
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GitHub
python-ml-workflow
Expert guidelines for Python ML and LLM workflows. Covers code quality, experiment tracking, and data handling. Use when working on AI/ML components or data pipelines.
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GitHub
outer-review
Commission and process external AI analysis to reduce blind spots. Manual invocation only.
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GitHub
ai-engineer
Expert AI Engineer role (10+ Years Exp). Focuses on production-grade GenAI, Agentic Systems, Advanced RAG, and rigorous Evaluation.
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GitHub
tensorflow-lite
TensorFlow Lite integration for on-device ML in React Native. Trigger: When working with object detection, image classification, or ML models.
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GitHub
magic-model-review
Evaluates Magic Model (Triangle) frameworks for BlackBelt coaches using Ed Dale's frameworks. Produces Facebook-ready feedback comments.
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GitHub
ai-orchestration
Multi-model AI collaboration via orchestrator MCP. Use when seeking second opinions, debugging complex issues, building consensus on architectural decisions, conducting code reviews, or needing external validat...
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GitHub
tinker-from-docs
Fine-tune LLMs using the Tinker API. Covers supervised fine-tuning, reinforcement learning, LoRA training, vision-language models, and both high-level Cookbook patterns and low-level API usage.
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GitHub
orchestrate
Multi-Model Orchestration - Guide for orchestrating multi-model agents
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GitHub
readiness-check
Classifies pre-run readiness (proceed/modify/skip) using recent load, recovery, and self-reported signals.
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GitHub
load-anomaly-guard
Detects unsafe training load spikes (>20-30% week-over-week) and emits safety flags. Use in nightly background jobs or when reviewing weekly training volume with conservative adjustment recommendations.
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GitHub
game-scoring
Use when working with candidate scoring, confidence calculation, softmax aggregation, or guess decision logic. Load for understanding how candidates are ranked, when the system decides to guess, and how semanti...
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默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库，同一仓库的多个技能可能共享相同数值；它反映仓库关注度，不代表单个技能的安装量或质量评分。