Guidance for analyzing log files and generating summary reports with counts aggregated across multiple date ranges and severity levels. This skill applies when tasks involve parsing log files by date, counting occurrences by severity (ERROR, WARNING, INFO), and outputting structured CSV summaries across time periods like "today", "last 7 days", or "last 30 days".
This skill provides guidance for merging data from multiple heterogeneous sources (CSV, JSON, Parquet, XML, etc.) into unified output formats with conflict detection and resolution. Use when tasks involve combining data from different file formats, field mapping between schemas, priority-based conflict resolution, or generating merged datasets with conflict reports.
Complete guide for Apache Airflow orchestration including DAGs, operators, sensors, XComs, task dependencies, dynamic workflows, and production deployment
Complete guide for Apache Spark data processing including RDDs, DataFrames, Spark SQL, streaming, MLlib, and production deployment
Guidance for writing SPARQL queries against RDF/Turtle datasets, particularly for university or academic data. This skill should be used when tasks involve querying RDF data with SPARQL, filtering entities based on multiple criteria, aggregating results, or working with Turtle (.ttl) files.
Guidance for writing and verifying SPARQL queries against RDF datasets, particularly university/academic ontologies. This skill should be used when tasks involve querying RDF data with SPARQL, working with academic datasets (students, professors, departments, courses), or performing complex graph pattern matching with filters and aggregations.
Complete guide for Apache Kafka stream processing including producers, consumers, Kafka Streams, connectors, schema registry, and production deployment
This skill provides guidance for merging data from multiple heterogeneous sources (JSON, CSV, Parquet, XML, etc.) into a unified dataset. Use this skill when tasks involve combining records from different file formats, applying field mappings, resolving conflicts based on priority rules, or generating merged outputs with conflict reports. Applicable to ETL pipelines, data consolidation, and record deduplication scenarios.
Guidance for data resharding tasks that involve reorganizing files across directory structures with constraints on file sizes and directory contents. This skill applies when redistributing datasets, splitting large files, or reorganizing data into shards while maintaining constraints like maximum files per directory or maximum file sizes. Use when tasks involve resharding, data partitioning, or directory-constrained file reorganization.
Complete guide for dbt data transformation including models, tests, documentation, incremental builds, macros, packages, and production workflows
Extract readable transcripts from Claude Code and Codex CLI session JSONL files
Guidance for setting up HuggingFace model inference services with Flask APIs. This skill applies when downloading HuggingFace models, creating inference endpoints, or building ML model serving APIs. Use for tasks involving transformers library, model caching, and REST API creation for ML models.
This skill provides guidance for semantic similarity retrieval tasks using embedding models (e.g., MTEB benchmarks, document ranking). It should be used when computing embeddings for documents/queries, ranking documents by similarity, or identifying top-k similar items. Covers data preprocessing, model selection, similarity computation, and result verification.
Integration patterns and best practices for adding persistent memory to LLM agents using the Letta Learning SDK
This skill provides guidance for training FastText text classification models with constraints on accuracy and model size. It should be used when training fastText supervised models, optimizing model size while maintaining accuracy thresholds, or when hyperparameter tuning for text classification tasks.
Guide for reverse-engineering and recreating programmatically-generated ray-traced images. This skill should be used when tasks involve analyzing a target image to determine rendering parameters, implementing path tracing or ray tracing algorithms, matching scene geometry and lighting, or achieving high similarity scores between generated and target images.
Guidance for training FastText text classification models with constraints on model size and accuracy. This skill should be used when training FastText models, optimizing hyperparameters, or balancing trade-offs between model size and classification accuracy.
Guidance for querying ML model leaderboards and benchmarks (MTEB, HuggingFace, embedding benchmarks). This skill applies when tasks involve finding top-performing models on specific benchmarks, comparing model performance across leaderboards, or answering questions about current benchmark standings. Covers strategies for accessing live leaderboard data, handling temporal requirements, and avoiding common pitfalls with outdated sources.
Manage Letta AI agent fleets declaratively with kubectl-style CLI. Use when creating, updating, or managing multiple Letta agents with shared configurations, memory blocks, tools, and folders.
Comprehensive guide for building production-grade LLM applications using LangChain's chains, agents, memory systems, RAG patterns, and advanced orchestration
SDK/API patterns for configuring LLM models on Letta agents. Use when setting model handles, adjusting temperature/tokens, configuring provider-specific settings (reasoning, extended thinking), or setting up custom endpoints.
Guidance for deploying HuggingFace models as inference APIs/services. This skill applies when tasks involve downloading pre-trained models from HuggingFace Hub, creating REST APIs for model inference, building Flask/FastAPI services around ML models, or setting up sentiment analysis, text classification, or other NLP inference endpoints.
Comprehensive guide for developing Letta agents, including architecture selection, memory design, model selection, and tool configuration. Use when building or troubleshooting Letta agents.
