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.
npx skills add MyTeslaMate/mcp-tesla --skill drive-efficiency-coachgit clone --depth 1 https://github.com/MyTeslaMate/mcp-teslaWrote 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.
[](https://agentmods.dev/skills/myteslamate/mcp-tesla/drive-efficiency-coach)<a href="https://agentmods.dev/skills/myteslamate/mcp-tesla/drive-efficiency-coach"><img src="https://agentmods.dev/badge/skills/myteslamate/mcp-tesla/drive-efficiency-coach/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.
<a href="https://agentmods.dev/skills/myteslamate/mcp-tesla/drive-efficiency-coach"><img src="https://agentmods.dev/badge/skills/myteslamate/mcp-tesla/drive-efficiency-coach.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00032 | $0.01259 |
| Opus 5.5 | $0.00013 | $0.00504 |
| Sonnet 5.5 | $0.00006 | $0.00252 |
| Haiku 4.5 | $0.00003 | $0.00126 |
Grade A, and why
drive-efficiency-coach 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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Drive Efficiency Coach
When to use
Activate this skill when the user asks anything along the lines of:
- "Am I driving efficiently?"
- "Why does my range vary so much?"
- "How can I improve my Wh/km?"
- "Was my last trip efficient?"
- "Show me my efficiency over the last month."
If the user only asks for a charge cost summary or battery health, prefer
the charge_review or battery_health_report MCP prompts instead.
Prerequisites
You need a TeslaMate car_id. If unknown:
- Call
teslamate_get_carsto list available cars. - If exactly one is returned, use it silently.
- If multiple, ask the user which one before continuing.
Workflow
1. Pull the dataset
Call teslamate_get_car_drives with:
car_id: target carstart_date: 30 days ago in RFC3339 (e.g.2026-04-06T00:00:00Z)end_date: now in RFC3339min_distance: 5 (filters out parking-lot moves where Wh/km is meaningless)fetch_all:Truewhenever the window is longer than ~1 month — the TeslaMate API returns at most 100 entries per page, and the wrapper will auto-paginate to give you the complete set.
If the user specifies a different period (e.g. "this year", "last week"),
use that instead. Always pass both start_date and end_date. For a
year-long window, fetch_all=True is mandatory or you'll undercount.
Also pull teslamate_get_car_charges over the same window — needed for the
timeline (Charge events) and to score the Recharge dimension.
2. Compute per-drive efficiency
For each drive in the response:
- Distance: prefer the field expressed in km. If TeslaMate is configured for miles, convert (1 mi = 1.609 km).
- Energy used: derive from whichever field is present —
typically
consumption_kwh, orstart_ideal_range_km - end_ideal_range_kmmultiplied by the car's nominal Wh/km, orstart_battery_level - end_battery_levelmultiplied by usable pack capacity. Use whichever yields the cleanest number; document the source in the answer. wh_per_km = energy_used_wh / distance_kmavg_speed_kmh = distance_km / (duration_min / 60)- Skip drives where
distance_km < 5orenergy_used_wh <= 0.
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.
- 2mo ago First seen · 131 lines · 32 tokens per session scan A 46e01c8ec734
drive-efficiency-coach is a skill published in the GitHub repository MyTeslaMate/mcp-tesla (5 stars, last pushed 4mo ago), licensed MIT. It adds 32 tokens to every session and 1,259 once invoked, about $0.0001 per session on Opus 5.5. 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.
drive-efficiency-coach compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| drive-efficiency-coachMyTeslaMate/mcp-tesla | Skill | 5 | 4mo ago | MIT | A | 32 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | today | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | today | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 314k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |