Loading...
Loading...
Found 53 Skills
Import structured data into Neo4j — LOAD CSV, CALL IN TRANSACTIONS, neo4j-admin database import full (offline bulk), apoc.load.csv/json, apoc.periodic.iterate, driver batch writes. Covers method selection, header file format, type coercion, null handling, ON ERROR modes, CONCURRENT TRANSACTIONS, pre-import constraint setup, and post-import validation. Use when importing CSV/JSON/Parquet files, migrating relational data to graph, or bulk-loading large datasets. Does NOT handle unstructured document/PDF/vector chunking pipelines — use neo4j-document-import-skill. Does NOT handle live app write patterns (MERGE/CREATE) — use neo4j-cypher-skill. Does NOT handle neo4j-admin backup/restore/config — use neo4j-cli-tools-skill.
Salesforce Data Cloud Retrieve phase. Use this skill when the user runs Data Cloud SQL, async queries, vector search, search-index workflows, or metadata introspection for Data Cloud objects. TRIGGER when: user runs Data Cloud SQL, describe, async queries, vector search, search-index workflows, or metadata introspection for Data Cloud objects. DO NOT TRIGGER when: the task is standard CRM SOQL (use querying-soql), segment creation or calculated insight design (use segmenting-datacloud), or STDM/session tracing/parquet analysis (use observing-agentforce).
Profile datasets to understand schema, quality, and characteristics. Use when analyzing data files (CSV, JSON, Parquet), discovering dataset properties, assessing data quality, or when user mentions data profiling, schema detection, data analysis, or quality metrics. Provides basic and intermediate profiling including distributions, uniqueness, and pattern detection.
Explore and query data on S3, Cloudflare R2, GCS, MinIO, or any S3-compatible storage. Use when the user mentions an s3://, r2://, gs://, or gcs:// URL, asks "what's in this bucket", wants to list remote files, preview remote Parquet/CSV/JSON, or query data on object storage without downloading it. Also triggers when the user wants to know the size, schema, or row count of remote datasets.
Profile a new tabular dataset before modeling. Find target leakage, missing data patterns, high-cardinality categoricals, near-constant features, redundant pairs, and non-linear relationships that Pearson correlation misses. Use whenever the user hands you a CSV or parquet and asks "what should I do with this?" Always run this skill before training any model on data you haven't seen before.
Convert files between 140+ formats using the ConversionTools MCP server. Use when the user needs to convert documents (Word, PDF, Excel, PowerPoint), data formats (JSON, CSV, XML, YAML, Parquet), images (PNG, JPG, WebP, AVIF, HEIC, JXL, SVG), audio (MP3, WAV, FLAC), video (MOV, MKV, AVI to MP4), e-books (EPUB, MOBI, AZW), OCR text extraction, AI-powered data extraction, AI text-to-speech (TTS), AI speech-to-text transcription (STT), subtitle conversion (SRT, VTT, ASS), or website screenshots.
Load a sharded, on-disk dataset (sharded .npy, Parquet/Arrow, raw binary, sharded HDF5, custom layouts) into a distributed cuPyNumeric ndarray via a manual partition + leaf @task launch with CPU/OMP/GPU variants. Use when no single-call loader fits, including when per-shard row counts differ across files. Prefer cupynumeric.load or legate.io.hdf5.from_file when they apply.
Fast in-process analytical database for SQL queries on DataFrames, CSV, Parquet, JSON files, and more. Use when user wants to perform SQL analytics on data files or Python DataFrames (pandas, Polars), run complex aggregations, joins, or window functions, or query external data sources without loading into memory. Best for analytical workloads, OLAP queries, and data exploration.
Analyze datasets to discover patterns, anomalies, and relationships. Use when exploring data files, generating statistical summaries, checking data quality, or creating visualizations. Supports CSV, Excel, JSON, Parquet, and more.
How raw multimodal robot data maps onto the Rerun data model. Read FIRST, before modeling or converting a dataset — and whenever you are about to convert/ingest/preprocess robot data into an .rrd or build a Rerun recording, even if not asked for the data model. Resolves the entity-vs-component, property-vs-component-vs-layer, and static-vs-temporal decisions and routes to the mechanism (do it with readers and lenses, not hand-built chunks or per-message rr.log): rerun-chunk-processing and the importer skills rerun-mcap, rerun-urdf, rerun-parquet, rerun-mp4, rerun-lerobot.
EDA toolkit. Analyze CSV/Excel/JSON/Parquet files, statistical summaries, distributions, correlations, outliers, missing data, visualizations, markdown reports, for data profiling and insights.
Salesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows. Use this skill when the user needs a multi-step Data Cloud pipeline, cross-phase troubleshooting, or data space and data kit management. TRIGGER when: user needs a multi-step Data Cloud pipeline, asks to set up or troubleshoot Data Cloud across phases, manages data spaces or data kits, or wants a cross-phase sf data360 workflow. DO NOT TRIGGER when: work is isolated to a single phase (use the matching phase-specific skill), the task is STDM/session tracing/parquet telemetry (use agentforce-observe), standard CRM SOQL (use platform-soql-query), or Apex implementation (use platform-apex-generate).