Skill UI

This tool provides a granular, per-message cost breakdown within a single conversation session. It serves as a deep-dive utility following an anomaly detection, allowing operators to pinpoint exactly which messages incurred the highest costs. It reveals critical cost components like output tokens, cache writes, and model escalations, helping users understand the true resource consumption beyond simple token counts.

This skill manages the end-to-end operational workflow for corporate email communication. It is focused on the mechanics of the mailbox itself, allowing users to triage inboxes, draft replies, send messages, and verify successful delivery. It ensures that all actions are context-aware, adhering to brand voice, and providing verifiable proof of the message's final status (e.g., Sent or Drafted).

This guide provides comprehensive, structured error handling patterns for modern applications using TypeScript, Python, and general architectural principles. It covers essential concepts like typed custom errors, Result patterns, API error transformers, and React Error Boundaries. Learn how to implement fail-fast mechanisms, differentiate user-facing messages from developer logs, and ensure system resilience against unexpected failures.

A comprehensive guide to professional Git version control. It covers best practices for branching strategies (GitHub Flow, GitFlow, Trunk-Based), writing standardized commit messages using Conventional Commits, managing merges vs. rebases, and defining effective Pull Request workflows. Ideal for any development team looking to improve code collaboration and maintain a clean, traceable history.

This file is dedicated to handling Japanese translations for the 'messages-ops' component. It currently acts as a localization placeholder, indicating that the full Japanese translation is under active implementation. Users should refer to the original English skill file for the core functionality and detailed usage instructions.

Use this tool to pre-screen ad creatives, emails, and landing page copy against synthetic audience panels. Predict response rates, sentiment, and potential objections before launching live campaigns. By scoring variants on criteria like resonance and credibility, it helps marketing teams identify top-performing messages and avoid wasting ad spend on underperforming content.

A robust tool for deploying high-impact marketing messages via SMS or WhatsApp. This function enforces multiple mandatory compliance checks—including GDPR, TCPA, and CASL consent verification, opt-out requirements, and quiet hours enforcement—before sending. It ensures that every message adheres to legal standards, minimizing risk and guaranteeing explicit user approval for execution.

This skill reconstructs the full narrative behind a piece of code by analyzing local Git history. Instead of just naming the last author (like git blame), it traces the file's origin, tracks subsequent changes, identifies related companion files, and extracts intent clues from commit messages. Use this when you need to know *why* code exists before refactoring, making risky edits, or understanding complex system behavior.

This skill generates standardized commit messages adhering to the popular gitmoji convention. By analyzing a git diff, staged files, or a plain description of changes, it selects the single most appropriate emoji to categorize the intent (e.g., feature, bug fix, refactor). It outputs a concise, well-formed, and highly scannable commit message for the user to copy, without running any actual git commands.

This utility guides users in creating standardized and descriptive commit messages for staged changes using the Conventional Commits specification. It enforces structure, defining types (feat, fix, chore, etc.), scopes, and detailed body/footer requirements, ensuring a clear and maintainable project history.

This skill decodes complex pipe-delimited HL7 v2.x messages (including ADT, ORU, MDM) into structured segments and fields. It is essential for ingesting HL7 feeds from interface engines, allowing users to surface embedded clinical narratives (OBX-5 and NTE-3) for subsequent Natural Language Processing (NLP) analysis. It also provides segment-aware de-identification capabilities to redact PHI while preserving the native HL7 message structure.