optimize-ev-charging

optimize-ev-charging is a skill for Claude Code from FernanMoreno/DomoAI. It costs 23 tokens per session (642 once invoked), scanned A, original, MPL-2.0.

A procedure for planning electric-vehicle charging within a deadline, electricity prices, and a site's power limit. It checks the charger and current conditions, then proposes and validates a schedule before any approved change.

In plain words
What is it for?
It helps determine when and how an EV should charge, explain the proposed plan, and schedule or apply it after approval.
Why use it?
It avoids charging plans that rely on an unverified charger, outdated information, or more site power than is available. It also keeps the final action behind operator approval.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Good fit It helps determine when and how an EV should charge, explain the proposed plan, and schedule or apply it after approval.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/fernanmoreno/domoai/optimize-ev-charging
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add FernanMoreno/DomoAI --skill optimize-ev-charging
Clone the repo
git clone --depth 1 https://github.com/FernanMoreno/DomoAI

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for optimize-ev-charging

README.md
[![agentmods](https://agentmods.dev/badge/skills/fernanmoreno/domoai/optimize-ev-charging/github.svg)](https://agentmods.dev/skills/fernanmoreno/domoai/optimize-ev-charging)
Your own site
<a href="https://agentmods.dev/skills/fernanmoreno/domoai/optimize-ev-charging"><img src="https://agentmods.dev/badge/skills/fernanmoreno/domoai/optimize-ev-charging/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for optimize-ev-charging

Your own site · 80×15
<a href="https://agentmods.dev/skills/fernanmoreno/domoai/optimize-ev-charging"><img src="https://agentmods.dev/badge/skills/fernanmoreno/domoai/optimize-ev-charging.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 23 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 642 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00023 $0.00642
Opus 5 $0.00012 $0.00321
Sonnet 5 $0.00005 $0.00128
Haiku 4.5 $0.00002 $0.00064

Measured 4d ago against content hash d6b9dba51b6c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

optimize-ev-charging scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/core/optimize-ev-charging/SKILL.md · 60 lines

How it starts

The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Optimize EV charging

This portable procedure creates a bounded charging proposal. It does not infer charger authority from a vehicle label and never calls a vendor route.

Declared operations

  • discover_devices
  • get_state
  • get_energy_context
  • optimize_scenario
  • validate_plan
  • explain_solution
  • operator_approval
  • commit_or_schedule_bundle

Operation bindings

  • discover_devicesmcp.discover_devices (read)
  • get_statemcp.get_state (read)
  • get_energy_contextmcp.get_energy_context (read)
  • optimize_scenariomcp.optimize_scenario (proposal)
  • validate_planmcp.validate_plan (validation)
  • explain_solutionmcp.explain_solution (read)
  • operator_approvaloperator.request_approval (approval)
  • commit_or_schedule_bundlemcp.commit_or_schedule_bundle (mutation)

Procedure

  1. discover_devices — identify a qualified EV charger and its canonical power capability.
  2. get_state — read connector, charging and availability state; stop when stale or unavailable.
  3. get_energy_context — read tariff and site-power context for the complete deadline horizon.
  4. optimize_scenario — propose charging slots with explicit capacity, deadline and import limits.
  5. validate_plan — bind the proposal to current policy, capabilities and state dependencies.
  6. explain_solution — show deadline margin, power assumptions and any unserved energy.
  7. operator_approval — request approval for the exact ordered bundle when mutation is required.
  8. commit_or_schedule_bundle — submit only the approved, digest-bound bundle for runtime admission and readback.

Safety rules

  • Never exceed charger, household or contract-power limits; a missing limit is a stop condition.
  • Do not treat vehicle presence as permission to charge or as proof of connector safety.
  • A stale connector, meter or power-feedback state stops the procedure.
  • Approval covers the exact bundle digest, not a future replacement proposal.
  • Timeout, rejected admission or missing readback is UNKNOWN; do not retry blindly.
  • No direct adapter, vendor, solver or alarm/security operation is allowed.

Read the full file on GitHub · 60 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 60 lines · 23 tokens per session scan A d6b9dba51b6c

Subscribe to this mod's changes

optimize-ev-charging is a skill published in the GitHub repository FernanMoreno/DomoAI (0 stars, last pushed 5d ago), licensed MPL-2.0. It adds 23 tokens to every session and 642 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-08.

Related

Other skills, from other repositories

augur-serenity

Serenity AI — AI/semiconductor supply chain bottlenecks, chokepoint assets.

BruceLanLan/augur · 23 tokens

electric-truck-readiness

Use when a carrier asks about electric trucks — Tesla Semi, Freightliner eCascadia, Volvo VNR Electric, Peterbilt 579EV, range, charging, total cost of ownership, regulatory pushes (CARB ACT, ACF), incentives, infrastructure, depot vs en-route charging, and which routes/applications are EV-ready today.

x3allamerican/skills · 78 tokens

downtime-cost-calculator

Calculate and analyze the true cost of equipment downtime, enabling farmers to make informed decisions about repair, replacement, and preventive maintenance investments.

farmfriend-labs/agent-skills-farming · 0 tokens

recurring-revenue

Designs the recurring revenue layer for hardware companies: software license, maintenance and support, consumables, professional services, and data/insights tiers. Models LTV, defines the Year-1 bundling strategy, and builds the commoditization defense. A hardware company that sells only hardware has a lumpy…

0xF4ng/aether-growth-fieldwork · 81 tokens

sector-rotation

An analysis framework for comparing industries in the Chinese A-share stock market, using business conditions, price momentum, valuation, and money flows. It produces rankings and higher- or lower-allocation suggestions.

HKUDS/Vibe-Trading · 39 tokens

jetson-diagnostic

Read-only Jetson health snapshot for identity, memory, GPU, thermal, power, storage, services, and top processes.

NVIDIA/skills · 30 tokens