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Found 223 Skills
Sendspark integration. Manage data, records, and automate workflows. Use when the user wants to interact with Sendspark data.
MIP Fund Accounting integration. Manage data, records, and automate workflows. Use when the user wants to interact with MIP Fund Accounting data.
Fills gaps in existing healthcare practitioner lists — adds missing phone numbers, credentials, specialties, contact info, education, reviews, and regulatory data. Triggers: "enrich my provider list", "fill in missing data", "add phone numbers to these doctors", "complete this practitioner database", "enrich CRM export", "fill gaps in my provider data", "supplement this healthcare list". Accepts CSV, Google Sheet URL, or pasted data. Searches for each provider's practice website, extracts missing fields, and enriches with reviews, clinical trials, and accreditation via WSAs. Do NOT use for extracting providers from practice URLs — use healthcare-providers-extract instead. Do NOT use for validating credentials — use healthcare-providers-verify instead. Do NOT use for discovering practices — use market-finder or local-places instead. Do NOT use for general extraction — use nimble-web-expert instead.
Retention Science integration. Manage data, records, and automate workflows. Use when the user wants to interact with Retention Science data.
Actindo integration. Manage data, records, and automate workflows. Use when the user wants to interact with Actindo data.
Splunk integration. Manage data, records, and automate workflows. Use when the user wants to interact with Splunk data.
Spaycial integration. Manage data, records, and automate workflows. Use when the user wants to interact with Spaycial data.
Slope integration. Manage data, records, and automate workflows. Use when the user wants to interact with Slope data.
Automated factory that converts GitHub repositories into standardized AI Skills. This tool is used when users provide a GitHub URL and want to "package", "wrap", or "create a Skill". It supports automatic retrieval of repository metadata, generation of standard directory structures, and injection of extended metadata required for lifecycle management.
A Pythonic interface to the HDF5 binary data format. It allows you to store huge amounts of numerical data and easily manipulate that data from NumPy. Features a hierarchical structure similar to a file system. Use for storing datasets larger than RAM, organizing complex scientific data hierarchically, storing numerical arrays with high-speed random access, keeping metadata attached to data, sharing data between languages, and reading/writing large datasets in chunks.
Use to define schemas, topic tags, and lineage metadata for enriched signals.
Guidance for data resharding tasks that involve reorganizing files across directory structures with constraints on file sizes and directory contents. This skill applies when redistributing datasets, splitting large files, or reorganizing data into shards while maintaining constraints like maximum files per directory or maximum file sizes. Use when tasks involve resharding, data partitioning, or directory-constrained file reorganization.