data360-code-extension-generate
Uses the Salesforce CLI plugin to build custom Python data transformations for Data Cloud. Supports initialization, local runs, scans, testing, and deployment of code extensions.
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Find your next superpower If you are an agent, refer to our llms.txt for full access.Uses the Salesforce CLI plugin to build custom Python data transformations for Data Cloud. Supports initialization, local runs, scans, testing, and deployment of code extensions.
Investigates EFS file systems that fail to mount, return errors, deny access, or perform slowly. Resolves problems involving NFS connectivity, permissions, throughput, and burst credit exhaustion.
Uses MCP tools to gather threat intelligence, generate detection opportunities, simulate attacker behavior with synthetic UDM events, and evaluate rule coverage. Generates YARA-L 2.0 rules to address gaps and supports deployment with user approval.
Examines cryptographic code for timing vulnerabilities such as division on secrets and secret-dependent branches. Supports implementation reviews and constant-time programming questions across systems, managed, and scripting languages.
Configures the org preference controlling whether a Salesforce org can receive marketplace partner offers. Covers related TransactableMarketplacePrivateOffer settings, excluding creation or management of individual offer records.
Work with theme.json, templates, template parts, and block patterns. Troubleshoot Site Editor style hierarchy, overrides, and caching, and build theme style variations.
Manages shadcn-vue components, registries, presets, and project initialization. Provides project context, component documentation, and usage examples for building and fixing UI.
Obtains AWS credentials for local development using aws login. Handles initial access setup and operations blocked by missing or expired credentials and sessions.
Search all connected sources with a single query. Locate decisions, documents, or discussions that may reside in chat, email, cloud storage, or project tracking systems.
Identifies what users want to achieve in a specific situation, what drives switching, and how they choose among options. Applies these findings to products, content, services, decisions, and AI prompts.
Acts as a routing alias when a user or upstream configuration explicitly selects lark-note. Related requests are handled by lark-meeting.
Finds checks and global state that block fuzzing progress, including hashes, checksums, magic values, and time-based seeds. Covers bypassing barriers under fuzzing build flags while avoiding patch-induced false positives.
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