Data & AI skills

CSV processing done right — parsing edge cases, dialects, and large files — use when ingesting or exporting CSV data.

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Crystal structure fundamentals — lattices, space groups, Miller indices, and reading crystallographic data correctly.

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Manage ML datasets with the datasets ecosystem — loading, streaming, preprocessing, versioning, and sharing. Use when data handling (not modeling) is the bottleneck.

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Build custom data visualizations with D3: selections, scales, axes, transitions, and reusable chart patterns. Use when off-the-shelf charts aren't enough.

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Manage LLM context windows deliberately — budgeting, prioritization, summarization, and retrieval instead of dumping everything in. Use when prompts grow long, costs rise, or the model loses track of key details.

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Analyzing contact tracing data — network reconstruction, transmission chains, and evaluating tracing performance.

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Build dashboards with Chart.js: layout, KPI cards, cross-chart filtering, theming, and real-time panels. Use for operational and analytics dashboards.

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Estimate causal effects from observational and experimental data — DAGs, confounding control, quasi-experimental designs, and sensitivity analysis. Use when you need "what causes what," not just correlation.

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Working with observational climate data — reanalyses, station records, anomalies, trends, and honest uncertainty.

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Build with Cohere's enterprise AI — Command models, Embed embeddings, and Rerank for retrieval pipelines.

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Split documents for retrieval effectively — fixed, semantic, structural, and hierarchical chunking with overlap tuning. Use when RAG retrieval quality depends on how documents are divided.

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NLP on clinical text — de-identification, concept extraction, phenotyping from notes, and LLM safety guardrails.

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ClickHouse columnar analytics — schema design, MergeTree tuning, and fast queries — use for OLAP and high-volume event data.

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Translating coarse global climate projections to local scales — dynamical vs statistical downscaling, bias correction, and added value.

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Analyzing behavioral experiment data — RT/accuracy preprocessing, mixed-effects models, and robust inference.

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Bayesian data analysis — priors, likelihoods, posterior computation, model comparison, and honest reporting.

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Statistical methods for biology and medicine — study design, hypothesis testing, regression, and survival analysis.

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Precision position measurement — proper motions, parallaxes, reference frames, and working with Gaia data.

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End-to-end bioinformatics analysis — sequence QC, alignment, and omics pipelines for research data.

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Build data-centric LLM applications with indexing and retrieval frameworks — document ingestion, indices, query engines, and RAG pipelines. Use when the core problem is getting the right data in front of the model.

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Build LLM applications with chain-style orchestration frameworks — prompts, chains, tool-calling agents, memory, and retrieval pipelines. Use when structuring multi-step LLM programs, adding tools to a model, or composing RAG systems in code.

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Craft effective AI image prompts with structured syntax, style control, and iterative refinement techniques.

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Design and build LLM agents: ReAct loops, tool schemas, planning, memory, guardrails, evals, and human-in-the-loop. Use when building autonomous multi-step AI systems.

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Proven prompts behind viral AI-made videos across 4 models — Claude, Muse AI, ChatGPT, Gemini. Browse by model, category, or tech tag, copy a creator's prompt, adapt it to your own model and renderer. Use when you need a motion-graphic, explainer visual, 3D scene, interactive animation, or AI video-gen prompt that already worked in the wild.

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