ha-ev-trip-planner

Execute deterministic code quality validations as a quality gate for smart-ralph task execution. Runs Layer 3A smoke test (Tier A AST, <1 min), Layer 1 (test execution), Layer 2 (test quality analysis), Layer 3B (Tier B BMAD Party Mode, ~15 min), and Layer 4 (Security & Defense, ~2-5 min). Uses Two-Tier approach: Tier A (AST deterministic) + Tier B (BMAD Party Mode consensus). Layer 4 covers 8 security tools: bandit, safety/pip-audit, gitleaks, semgrep, checkov, deptry, vulture, trivy. Generates a checkpoint JSON consumed by smart-ralph VERIFY steps. Use when you need to validate that code meets quality and security standards before COMMIT.

Create and manage Home Assistant YAML configuration files including automations, scripts, templates, blueprints, Lovelace dashboards, and file organization. Use when working with Home Assistant configuration files (.yaml, .yml) or discussing HA automations, scripts, sensors, or dashboards.

Design, update, and refactor Home Assistant Lovelace dashboards (YAML views/partials) with a constrained, machine-safe design system: button-card-first structure, minimal card-mod styling, optional flex-horseshoe + mini-graph telemetry, strict grid/vertical-stack layout rules, centralized templates, deterministic ordering, and config validation. Use for Home Assistant dashboard work (especially config/dashboards/**), when refactoring views, adding infra/home/energy/environment panels, or translating Stitch design inspiration into safe Lovelace YAML.

Create agent feedback for harness self-improvement. Use after completing complex tasks (>30 min) to document what worked, what failed, and patterns detected. This feeds the continuous improvement loop.

Lossless LLM-optimized compression of source documents. Use when the user requests to 'distill documents' or 'create a distillate'.

Iterative consensus-building skill that runs BMAD Party Mode in loops until agents reach consensus. Each iteration enriches context with previous round findings, project domain knowledge, and relevant evidence. Use when you need multi-agent validation, architectural decisions, technical debates, or any situation where consensus is the goal and isolated opinions are insufficient.

Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, or implementing testing best practices.

Use when users ask how to write, explain, customize, migrate, secure, or troubleshoot GitHub Actions workflows, workflow syntax, triggers, matrices, runners, reusable workflows, artifacts, caching, secrets, OIDC, deployments, custom actions, or Actions Runner Controller, especially when they need official GitHub documentation, exact links, or docs-grounded YAML guidance.

Operate a Home Assistant instance via the official REST/WebSocket APIs and backups, with safe plan/apply workflows for bulk, reviewable changes.

Best practices for Home Assistant automations, helpers, scripts, device controls, and Lovelace dashboard configuration. TRIGGER THIS SKILL WHEN: - Creating or editing HA automations, scripts, or scenes - Choosing between template sensors and built-in helpers - Writing or restructuring triggers, conditions, or automation modes - Setting up Zigbee button/remote automations (ZHA or Zigbee2MQTT) - Renaming entities or migrating device_id references to entity_id - Configuring Lovelace dashboard cards and selecting the right sensor or helper to feed them SYMPTOMS THAT TRIGGER THIS SKILL: - Agent uses Jinja2 templates where native conditions, triggers, or helpers exist - Agent uses device_id instead of entity_id in triggers/actions - Agent modifies entity IDs or config objects without checking all consumers - Agent chooses wrong automation mode (e.g., single for motion lights) - Agent hard-codes min/max values or picks a raw sensor when a derived helper is more appropriate for a dashboard card

Control Home Assistant devices and automations via REST API. 25 entity domains including lights, climate, locks, presence, weather, calendars, notifications, scripts, and more. Use when the user asks about their smart home, devices, or automations.

Detect common Python vulnerabilities such as SQL injection, unsafe deserialization, and hardcoded secrets. Use as part of a secure SDLC for Python projects.