calculate-ev-range-factors

calculate-ev-range-factors is a skill for Claude Code from jeffreytse/grimoire-core. It costs 33 tokens per session (1,369 once invoked), scanned A, original, MIT.

A method for estimating an electric vehicle’s real driving range from its rated range and conditions such as temperature, speed, load, hills, and accessories.

In plain words
What is it for?
Planning EV journeys, estimating charging needs, comparing vehicles, and explaining unexpected range loss.
Why use it?
An EV’s laboratory range rating can differ substantially from what it achieves on an actual trip.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the grimoire-automotive plugin — 16 skills shipped together

Good fit Planning EV journeys, estimating charging needs, comparing vehicles, and explaining unexpected range loss.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/jeffreytse/grimoire-core/calculate-ev-range-factors
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 jeffreytse/grimoire-core --skill calculate-ev-range-factors
Clone the repo
git clone --depth 1 https://github.com/jeffreytse/grimoire-core

Made for: Claude Code.

Or install grimoire-automotive, the plugin that ships this one along with the rest of its 16 skills.

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 calculate-ev-range-factors

README.md
[![agentmods](https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/calculate-ev-range-factors/github.svg)](https://agentmods.dev/skills/jeffreytse/grimoire-core/calculate-ev-range-factors)
Your own site
<a href="https://agentmods.dev/skills/jeffreytse/grimoire-core/calculate-ev-range-factors"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/calculate-ev-range-factors/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 calculate-ev-range-factors

Your own site · 80×15
<a href="https://agentmods.dev/skills/jeffreytse/grimoire-core/calculate-ev-range-factors"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/calculate-ev-range-factors.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 33 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,369 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 $0.00033 $0.01369
Opus 5 $0.00016 $0.00685
Sonnet 5 $0.00007 $0.00274
Haiku 4.5 $0.00003 $0.00137

Measured 11d ago against content hash 7dda3ba2e705, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

calculate-ev-range-factors 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 11d 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/automotive/ev/skills/calculate-ev-range-factors/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.

Calculate EV Range Factors

Quantify how temperature, speed, payload, terrain, and accessories reduce an EV's real-world range below its EPA-rated figure, and calculate a realistic trip range.

Why This Is Best Practice

Adopted by: DOE Alternative Fuels Data Center (official federal EV resource), EPA SAE J1634 test standard, all major EV manufacturers (Tesla, GM, Ford, Hyundai use these factors in their range estimators), AAA EV research
Impact: Real-world EV range is 12–59% below EPA rating depending on conditions (AAA, 2023); temperature alone causes 41% range reduction at 20°F; 80% of EV owners report range anxiety — accurate pre-trip calculation eliminates it for planned routes; unexpected range loss causes 1-in-12 EV drivers to be stranded annually (J.D. Power)
Why best: EPA SAE J1634 test is conducted in a lab at 72°F with no HVAC, constant speed — real-world conditions diverge significantly; understanding multiplicative factor interactions prevents dangerous miscalculation

Sources: DOE AFDC "Factors Affecting EV Range" (2023); EPA SAE J1634 standard; AAA "EV Range Testing" (2022); NHTSA EV Consumer Guide

Steps

  1. Start with EPA-rated range — Look up the specific vehicle's EPA combined range at fueleconomy.gov; note this is a laboratory test result at 72°F, no climate control, standardized drive cycle.

  2. Apply temperature derating — Temperature is the largest single factor. At 20°F: multiply rated range × 0.59 (41% loss). At 0°F: × 0.50. At 32°F: × 0.77. At 50°F: × 0.90. At 72°F: × 1.00 (rated). At 95°F with AC: × 0.83. Source: AAA 2022 range testing across 5 vehicles.

  3. Apply speed factor — EV range is highly speed-sensitive (aerodynamic drag scales as speed²). At 55 mph: × 1.10 (better than EPA). At 65 mph: × 1.00 (EPA-equivalent). At 75 mph: × 0.85. At 85 mph: × 0.72. Adjust for your typical highway speed.

  4. Apply terrain factor — Net elevation gain matters: +1,000 ft net elevation gain ≈ 3–4% range reduction per 100 miles. For routes with net elevation loss (downhill destination), regenerative braking recovers 60–70% of descent energy. For flat terrain: factor = 1.00.

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. 11d ago First seen · 60 lines · 33 tokens per session scan A 7dda3ba2e705

Subscribe to this mod's changes

calculate-ev-range-factors is a skill published in the GitHub repository jeffreytse/grimoire-core (4 stars, last pushed 22d ago), licensed MIT. It adds 33 tokens to every session and 1,369 once invoked, about $0.0002 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 2026-08-31.