Electronic lab notebook workflows — structuring entries, templates, linking data, and audit-ready records.
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Write documentation developers actually use: READMEs, API docs, ADRs, runbooks, and docs-as-code workflows. Use when writing or improving technical documentation.
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Compare files, text, and data effectively with diff strategies, semantic comparison, and review workflows.
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Dose-response modeling — EC50 estimation, curve fitting, synergy analysis, and benchmark-dose methods.
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Idiomatic Dart: null safety, async, Flutter-ready patterns, packages, and tooling. Use when writing, reviewing, or structuring Dart code (Flutter or server/CLI).
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Analyze text data with NLP — preprocessing, embeddings, topic modeling, sentiment, classification, and evaluation. Use when turning unstructured text into structured insight.
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Build SaaS customer support — tiered support models, SLAs, macros, escalation, and support-driven product feedback.
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Run the data science workflow end to end — framing, exploration, feature engineering, modeling, validation, and communicating results. Use when turning raw data into decisions or predictions.
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Secure containerized workloads — image hardening, registry scanning, runtime protection, and supply-chain integrity.
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Idiomatic modern C#: nullable reference types, records, async/await, LINQ, and .NET project structure. Use when writing, reviewing, or structuring C# applications.
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Professional CSS: cascade mastery, modern layout, custom properties, and maintainable architecture. Use when writing, reviewing, or debugging CSS.
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Systematic debugging: reproduce, isolate, hypothesize, verify — with tooling for hard bugs. Use when stuck on a bug, flaky failure, or production incident.
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Crystal structure fundamentals — lattices, space groups, Miller indices, and reading crystallographic data correctly.
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CSV processing done right — parsing edge cases, dialects, and large files — use when ingesting or exporting CSV data.
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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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Relational schema design — normalization, keys, constraints, and evolution — use when modeling data or reviewing a schema.
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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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Design role-based agent teams with CrewAI — agents, tasks, crews, and collaboration patterns for complex workflows.
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Manage CRM systems — data architecture, pipeline design, automation, user adoption, and reporting.
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Design clear, actionable dashboards with KPI hierarchy, data visualization best practices, and layout patterns.
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Meet EU Cyber Resilience Act vulnerability duties — coordinated disclosure, SBOMs, and lifecycle security for digital products.
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Build a morning briefing ritual with Muse: overnight priorities, today's calendar, weather-aware planning, and top 3 focus items as a concise chat summary.
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Scheduled jobs on PaaS and in the cloud — cron syntax, reliability, and observability — vendor-neutral patterns.
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Monitoring scheduled jobs — heartbeat checks, missed runs, and duration anomalies — platform-agnostic patterns.
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