Agent Academy is a free, source-linked library of skills, prompts, MCP servers and bot templates, organised into courses by profession, for people who hand real work to AI agents: 1,467 live resources, each with its original link, license and safety tags.
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Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
Sourcegithub.com/wshobson/agents/blob/main/plugins/data-engineering/skills/airflow-dag-patterns/SKILL.md
Gate fine-tuned checkpoints with drift budgets, paired comparison, and forgetting checks before promotion. Use after a training run produces a checkpoint, when deciding whether a tuned model ships, or when a promoted model needs re-gating against updated goldens.
Sourcegithub.com/wshobson/agents/blob/main/plugins/llm-finetuning/skills/checkpoint-promotion/SKILL.md
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
Sourcegithub.com/wshobson/agents/blob/main/plugins/data-engineering/skills/data-quality-frameworks/SKILL.md
Prepare, format, and validate datasets for supervised fine-tuning and preference training. Use when converting raw data into training format, applying chat templates, configuring sequence packing, generating synthetic training data, or writing a dataset card before a run.
Sourcegithub.com/wshobson/agents/blob/main/plugins/llm-finetuning/skills/dataset-curation/SKILL.md
Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.
Sourcegithub.com/wshobson/agents/blob/main/plugins/data-engineering/skills/dbt-transformation-patterns/SKILL.md
Build the evaluation harness that gates every fine-tuning run — golden sets, per-failure-mode graders, judge calibration, and base-model baselines. Use when starting a fine-tuning effort, when converting traces into an eval set, or when calibrating a judge against human labels.
Sourcegithub.com/wshobson/agents/blob/main/plugins/llm-finetuning/skills/eval-harness-first/SKILL.md
Decide whether to fine-tune at all, and route to the right method (SFT, DPO/ORPO/KTO, GRPO/RLVR, continued pretraining) and base model. Use when starting any fine-tuning effort, when unsure whether RAG or prompting would suffice, or when choosing between preference-optimization and reinforcement methods.
Sourcegithub.com/wshobson/agents/blob/main/plugins/llm-finetuning/skills/finetuning-method-selection/SKILL.md
Train reasoning and verifiable-task behavior with GRPO and reinforcement learning from verifiable rewards (RLVR). Use when task success is algorithmically checkable (math, code, tool calls, structured output), when designing GRPO reward functions, or when a GRPO run diverges or reward-hacks.
Sourcegithub.com/wshobson/agents/blob/main/plugins/llm-finetuning/skills/grpo-rlvr-training/SKILL.md
Configure LoRA and QLoRA supervised fine-tuning with current best-practice hyperparameters. Use when writing or reviewing a LoRA/QLoRA training configuration, choosing rank/alpha/target modules, or deciding between LoRA, QLoRA, and full fine-tuning.
Sourcegithub.com/wshobson/agents/blob/main/plugins/llm-finetuning/skills/lora-qlora-recipes/SKILL.md
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
Sourcegithub.com/wshobson/agents/blob/main/plugins/machine-learning-ops/skills/ml-pipeline-workflow/SKILL.md
Use this skill when designing or reviewing a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features
Sourcegithub.com/wshobson/agents/blob/main/plugins/database-design/skills/postgresql-table-design/SKILL.md
Align a fine-tuned model with preference data using DPO, ORPO, KTO, or SimPO. Use when preference pairs or thumbs-up/down feedback exist, when choosing between preference-optimization methods, or when a DPO run needs hyperparameters or debugging.
Sourcegithub.com/wshobson/agents/blob/main/plugins/llm-finetuning/skills/preference-optimization/SKILL.md
Export a promoted fine-tuned model in the right deployment format — merged safetensors, LoRA-only, GGUF with imatrix, or FP8. Use after a checkpoint passes promotion, when choosing a quantization format for a target device, or when an exported model fails its smoke test.
Sourcegithub.com/wshobson/agents/blob/main/plugins/llm-finetuning/skills/quantized-export/SKILL.md
Design composable recommendation, ranking, and feed pipelines using the six-stage Source→Hydrator→Filter→Scorer→Selector→SideEffect framework popularized by xAI's open-sourced X For You algorithm. Use when building any system that picks "the top K items for a (user, context)" — content feeds, search ranking, RAG rerankers, task prioritizers, notification triage, ad selection.
Sourcegithub.com/wshobson/agents/blob/main/plugins/machine-learning-ops/skills/recsys-pipeline-architect/SKILL.md
Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.
Sourcegithub.com/wshobson/agents/blob/main/plugins/data-engineering/skills/spark-optimization/SKILL.md
Convert evaluation traces and production logs into SFT examples and preference pairs. Use when graded traces or failure examples exist and need to become training data, when applying rejection sampling to model outputs, or when building DPO pairs from passing and failing runs.
Sourcegithub.com/wshobson/agents/blob/main/plugins/llm-finetuning/skills/trace-to-training-data/SKILL.md
Fine-tune vision-language models (VLMs) with supervised learning on image+text data. Use when adapting a VLM to a visual domain or task, configuring frozen-vision-tower LoRA, or debugging a VLM fine-tune that trains without learning.
Sourcegithub.com/wshobson/agents/blob/main/plugins/llm-finetuning/skills/vision-sft/SKILL.md
Answer data questions -- from quick lookups to full analyses. Use when looking up a single metric, investigating what's driving a trend or drop, comparing segments over time, or preparing a formal data report for stakeholders.
Sourcegithub.com/anthropics/knowledge-work-plugins/blob/main/data/skills/analyze/SKILL.md
Build an interactive HTML dashboard with charts, filters, and tables. Use when creating an executive overview with KPI cards, turning query results into a shareable self-contained report, building a team monitoring snapshot, or needing multiple charts with filters in one browser-openable file.
Sourcegithub.com/anthropics/knowledge-work-plugins/blob/main/data/skills/build-dashboard/SKILL.md
Create publication-quality visualizations with Python. Use when turning query results or a DataFrame into a chart, selecting the right chart type for a trend or comparison, generating a plot for a report or presentation, or needing an interactive chart with hover and zoom.
Sourcegithub.com/anthropics/knowledge-work-plugins/blob/main/data/skills/create-viz/SKILL.md
Generate or improve a company-specific data analysis skill by extracting tribal knowledge from analysts. BOOTSTRAP MODE - Triggers: "Create a data context skill", "Set up data analysis for our warehouse", "Help me create a skill for our database", "Generate a data skill for [company]" → Discovers schemas, asks key questions, generates initial skill with reference files ITERATION MODE - Triggers: "Add context about…
Sourcegithub.com/anthropics/knowledge-work-plugins/blob/main/data/skills/data-context-extractor/SKILL.md
Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.
Sourcegithub.com/anthropics/knowledge-work-plugins/blob/main/data/skills/data-visualization/SKILL.md
Profile and explore a dataset to understand its shape, quality, and patterns. Use when encountering a new table or file, checking null rates and column distributions, spotting data quality issues like duplicates or suspicious values, or deciding which dimensions and metrics to analyze.
Sourcegithub.com/anthropics/knowledge-work-plugins/blob/main/data/skills/explore-data/SKILL.md
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common…
Sourcegithub.com/anthropics/knowledge-work-plugins/blob/main/bio-research/skills/instrument-data-to-allotrope/SKILL.md