Machine learning

Eval-driven skill tuning.

Local scVI/scANVI-based single-cell latent embedding and batch-aware integration from raw-count .h5ad or 10x Matrix Market input, with stable integrated…

Standardized cost-estimation framework for great_cto plans.

End-to-end ML phylogenetic tree inference — MSA, trimming, ModelFinder, IQ-TREE2/RAxML-NG.

Cargo feature flags for crates/xberg — ORT-incompatible targets (WASM, Android x86_64 emulator), type-only and tract inference companion features…

Use when splitting extracted text into chunks for LLM context windows or RAG ingestion.

Use when extracting keywords (YAKE/RAKE) from documents — and, secondarily, when detecting document language or generating embeddings for RAG and search.

Use when choosing an output format for extracted documents — plain text, markdown, djot, HTML, JSON, or DocTags.

Drafts a customized Customer Agreement starting from the Y Combinator standard form SaaS template.

Guides security professionals in implementing defense-in-depth security architectures, achieving compliance with industry frameworks (SOC2, ISO27001, GDPR…

Decide which xberg-io repository owns a cross-repository fix or API, and coordinate compatible changes across Xberg, Alef, enterprise, crawler, LLM, and OCR…

Routes any legal task to the right LLM, like OpenRouter but for legal work and grounded in benchmarks instead of brand loyalty.

Create and manage Elastic ML anomaly detection jobs via the API.

Explain Elasticsearch ML anomaly detection scores, model behavior, and result interpretation.

Elastic ML anomaly detection — investigation/RCA, score explanation, job lifecycle troubleshooting, and job operations.

This skill should be used for advanced LLM evaluation: LLM-as-judge systems, direct scoring, pairwise comparison, rubric calibration, evaluator bias…

No Inference / Source-Locked Verification.

Offensive AI security testing and exploitation framework.

Chunking, embeddings, and RAG pipeline integration

Query and report on API usage costs across LLM, embedding, and tool providers

Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA.

Track and visualize ML training experiments with Trackio.

AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU.

Answer questions about LLM and agentic-application behavior from data already ingested into Elastic: latency and error rate, token and cost utilization…