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Found 25 Skills
Guides product management for human data platforms—annotation and labeling products, workforce workflows, task design, quality systems (gold sets, adjudication, inter-annotator agreement), customer ML-team project delivery, contributor experience, and privacy-safe handling of human-generated training data. Use when prioritizing roadmap for labeling/RLHF/eval data platforms, writing PRDs for annotation or QA features, defining success metrics for throughput and quality, scoping enterprise customer workflows, or balancing cost-quality-speed tradeoffs—not for hands-on model training (data-scientist), warehouse/analytics pipelines (data-warehouse-engineer), generic BRD workshops without product lens (business-analyst), AI solution architecture for copilots (applied-ai-architect-commercial-enterprise), or control implementation for audits (compliance-engineer). UX flows: product-designer. Eval harnesses: prompt-engineer-agent-prompts-evals. Pricing/packaging for platform: product-management-monetization.
Exploratory Data Analysis (EDA): profiling, visualization, correlation analysis, and data quality checks. Use when understanding dataset structure, distributions, relationships, or preparing for feature engineering and modeling.
Native Arrow filesystem integration with PyArrow. Optimized for Parquet workflows, zero-copy data transfer, predicate pushdown, and column pruning. Covers S3, GCS, HDFS with PyArrow datasets.
Delta Lake integration with cloud storage (S3, GCS, Azure). Covers storage_options, PyArrow filesystem, time travel, and partitioned writes.
Reading and writing data with Pandas from/to cloud storage (S3, GCS, Azure) using fsspec and PyArrow filesystems.
Using DuckDB with remote cloud storage via HTTPFS extension, fsspec, and Delta Lake integration. Covers S3, GCS, Azure, and S3-compatible endpoints.
Interactive web apps for data science: Streamlit, Panel, and Gradio. Use for prototyping ML models, creating data exploration dashboards, and sharing insights with non-technical stakeholders.
Observability and monitoring for data pipelines using OpenTelemetry (traces) and Prometheus (metrics). Covers instrumentation, dashboards, and alerting.
Data visualization for Python: Matplotlib, Seaborn, Plotly, Altair, hvPlot/HoloViz, and Bokeh. Use when creating exploratory charts, interactive dashboards, publication-quality figures, or choosing the right library for your data and audience.
Manage data programs, governance operations, and data reliability. Cover data roadmaps, stakeholder coordination, metadata stewardship, lifecycle management, monitoring, incident response, capacity planning, and SLA frameworks. Triggers on "manage data team", "data roadmap", "governance review", "data incident", "SLA framework", "data ops", "stewardship", "data product delivery", or "data KPIs". Human annotation/labeling platform PM: product-management-human-data-platform.
Run a structured business analysis workflow: requirements elicitation with stakeholder interviews and MoSCoW prioritization, as-is/to-be process mapping with BPMN notation, data-driven analysis (CBA, SWOT, root cause), and production-ready documentation (BRD, FRD, user stories with INVEST acceptance criteria). Triggers on "gather requirements", "write user stories", "map business process", "BRD", "FRD", "gap analysis", "cost-benefit analysis", "facilitate workshop", "business requirements", "process mapping", "as-is to-be", "stakeholder interview", or "business analyst". For management consulting (issue trees, steerCo business cases, operating model), use business-consultant—not business-analyst. For business model canvas, market sizing, and unit economics research, use business-model-researcher. Human data / labeling platform PRDs: product-management-human-data-platform. Monetization PRDs and packaging: product-management-monetization.
Segment integration. Manage Workspaces. Use when the user wants to interact with Segment data.