AIdome-co

Use when adding support for a new AI coding assistant. Covers adapter implementation, registry updates, testing, and verification in the Extension Development Host.

Use when packaging, releasing, or publishing the VS Code extension. Covers vsce packaging, VSIX validation, GitHub Releases, and Marketplace / Open VSX publishing.

Use when running an agentic QA pass on the AIdome Endpoint Switchboard extension — test planning, bug hunting, edge-case analysis, or pre-release verification. Wraps the gem-team-inspired QA agent pipeline (research → plan → generate → run → fix) plus adversarial review.

Use when setting up or maintaining CodeQL code scanning for the AIdome Endpoint Switchboard extension. Covers GitHub Actions workflow configuration, JavaScript/TypeScript analysis, alert triage, and custom query packs.

Use when configuring secret scanning and push protection for the AIdome Endpoint Switchboard repository. Covers alert triage, custom patterns for AIdome API keys, and integration with the extension's SecretStorage model.

Use when adding or updating VS Code command contributions in the AIdome Endpoint Switchboard extension. Covers naming conventions, package.json contributions, command handler registration, and the thin-wrapper pattern.

Use when localizing the AIdome Endpoint Switchboard VS Code extension. Covers package.json NLS, source code l10n, walkthrough content translation, and best practices for managing locale bundles.

Use when debugging failing CI workflows, build errors, test failures, or VSIX packaging issues in GitHub Actions.

Prompt and workflow for generating conventional commit messages using a structured format. Guides users to create standardized, descriptive commit messages in line with the Conventional Commits specification, including instructions, examples, and validation.

Implements Manus-style file-based planning to organize and track progress on complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when asked to plan out, break down, or organize a multi-step project, research task, or any work requiring 5+ tool calls.

AI-powered codebase security scanner that reasons about code like a security researcher — tracing data flows, understanding component interactions, and catching vulnerabilities that pattern-matching tools miss. Use this skill when asked to scan code for security vulnerabilities, find bugs, check for SQL injection, XSS, command injection, exposed API keys, hardcoded secrets, insecure dependencies, access control issues, or any request like "is my code secure?", "review for security issues", "audit this codebase", or "check for vulnerabilities". Covers injection flaws, authentication and access control bugs, secrets exposure, weak cryptography, insecure dependencies, and business logic issues across JavaScript, TypeScript, Python, Java, PHP, Go, Ruby, and Rust. Also covers IaC-specific issues: hardcoded credentials, overly permissive IAM, unencrypted storage, and public resource exposure in Terraform, Ansible, CloudFormation, and Helm.