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Found 33 Skills
Project structure initialization and metadata generation. This skill should be used when creating a new project, initializing project structure based on type (golang or social profile), or generating/updating README documentation.
This skill should be used when users need to work with the Vercel AI SDK for building AI-powered applications. It provides comprehensive guidance on core APIs (generateText, streamText), UI components (useChat, useCompletion), tool calling, structured data generation, provider management, streaming protocols, and advanced features like middleware and custom providers.
Utilize AI to assist in testing activities, including test data generation, defect root cause analysis, test prioritization, and intelligent test recommendation. The default output format is Markdown, and you can request Excel/CSV/JSON formats instead. This skill applies to AI-assisted testing scenarios.
Generate sample security events, attack scenarios, and synthetic alerts for Elastic Security. Use when demoing, populating dashboards, testing detection rules, or setting up a POC.
Generate Salesforce Flows using the MCP tool execute_metadata_action. Use when the user asks to create, build, or generate a flow — including Screen, Autolaunched, Record-Triggered (before/after-save), Scheduled. Also trigger for flow-like requests such as "when a record is created", "trigger daily at", "send an email when", "update the field when", "automate", "workflow", or "flow XML/metadata". This is the only skill for Salesforce Flow generation.
Salesforce data operations with 130-point scoring. Use this skill to create, update, delete, bulk import/export, generate test data, and clean up org records using sf CLI and anonymous Apex. TRIGGER when: user creates test data, performs bulk import/export, uses sf data CLI commands, needs data factory patterns for Apex tests, or needs to seed/clean records in a Salesforce org. DO NOT TRIGGER when: SOQL query writing only (use platform-soql-query), Apex test execution (use platform-apex-test-run), or metadata deployment (use platform-metadata-deploy).
factory_boy test data generation specialist. Covers Factory, DjangoModelFactory, SQLAlchemyModelFactory, all field declarations (Faker, LazyAttribute, Sequence, SubFactory, RelatedFactory, post_generation, Trait, Maybe, Dict, List), batch creation, pytest integration, and Celery task testing patterns. USE WHEN: user mentions "factory_boy", "test factory", "DjangoModelFactory", "SQLAlchemyModelFactory", asks about "test data generation", "factory traits", "SubFactory", "factory fixtures". DO NOT USE FOR: pytest internals - use `pytest`; Django setup - use `pytest-django`; Hypothesis property testing - use `pytest` with Hypothesis
Use this skill when users need to create, generate, or modify Salesforce Sharing Rules metadata. TRIGGER when: users mention sharing rules, record sharing, criteria-based sharing, role-based sharing, guest user sharing, portal user sharing, sharingRules, sharingCriteriaRules, sharingGuestRules, sharingOwnerRules, .sharingRules-meta.xml files, or ask to share records with specific roles or groups. Also trigger when users want to configure record-level access beyond org-wide defaults (OWD), share object records with roles, groups, or guest users, or set up Experience Site guest user record visibility. SKIP when: user needs permission sets or profiles (use platform-permission-set-generate), or needs object-level security rather than record-level sharing (use platform-permission-set-generate).
Create or update database seed scripts for development and testing environments. Use when setting up test data, initializing development databases, creating demo environments, resetting to known state, or generating realistic sample data.
Plan comprehensive test data management including synthetic data generation, data anonymization, versioning, and environment-specific strategies.
Create diverse synthetic test inputs for LLM pipeline evaluation using dimension-based tuple generation. Use when bootstrapping an eval dataset, when real user data is sparse, or when stress-testing specific failure hypotheses. Do NOT use when you already have 100+ representative real traces (use stratified sampling instead), or when the task is collecting production logs.
Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.