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Limits Energy

Community
bradfrost
limits-energy

Make the environmental cost of AI usage visible — floor estimates built from published per-prompt figures, rendered as lightbulb time (💡), with an optional weekly energy budget that informs but never blocks. Use when the user asks "how much energy did this session cost", "what's the environmental cost", "show the energy footer", "set an energy budget", "energy report", or enables environmental awareness in the limits-setup interview.

Overview

Publisherbradfrost
Repositoryskills
Skill namelimits-energy
Stars
51
Forks
1
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by bradfrost on GitHub. Read the source before you install it.

Installation

Install the Limits Energy AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/bradfrost/skills/tree/main/skills/mental-health/limits-energy
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/bradfrost/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/mental-health/limits-energy .claude/skills/limits-energy
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Limits Energy in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Limits Energy on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Limits Energy is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Limits: energy

Every prompt burns real electricity and water in a real data center. This skill makes that cost visible — as an honest floor estimate, not a guilt trip and not a fake precision instrument.

Pairs with limits-setup, which writes the config this skill reads.

The honesty contract (binding on you, the agent)

  1. Inform by default; gate only by explicit opt-in. Energy numbers inform. The one exception is a budget the user set with ENERGY_BUDGET_MODE=restrict, chosen eyes-open in the interview — then the gate is theirs, and the honesty burden doubles: they are being governed by a floor estimate on purpose, and every refusal says so.

  2. Cited figures only, provenance inline. The two published anchors, with what they cover:

    • ~0.24 Wh — Google's published median for a Gemini Apps text prompt (2025). Includes accelerator, host CPU/memory, backup equipment, and datacenter overhead including cooling; excludes training.
    • ~0.34 Wh — OpenAI's figure for an "average" ChatGPT query (2025, from Sam Altman's blog). Not peer-reviewed; "average query" is undefined.

    Both are vendor self-reported. Say so when asked.

  3. Never invent a conversion. There is no published per-token energy figure — do not create one. Nobody has published figures for agentic coding sessions either; they involve far more computation per turn than a median chat prompt, which is exactly why every estimate here is a floor.

  4. No shame mechanics. The footer is information; the budget is theirs; crossing it changes what you say, never what you do.

Estimating: count turns, multiply, call it a floor

A "turn" is one model call — each response, including each tool-use step in an agentic session, is one. Turns are countable; per-turn cost is not knowable, so:

estimated floor = (turns you can count) × 0.24 Wh (the conservative anchor)

The footer shows only the result (see Rendering); the method stays one question away — when they ask "how was this measured", give the math, the turn count, and both anchors with provenance. When you cannot count turns on a surface, estimate the count conservatively and say you did. Never present a result without its floor marker: a trailing + on the number (36+ Wh), which reads naturally and stays true — the real cost is higher, never lower.

Rendering: keep it light

One terse line, emoji that scale with the magnitude, no method talk. End-of-session footer (only when ENERGY_FOOTER=yes in ~/.config/ai-limits/config, or on request; skip it in automated sessions):

💡💡💡💡💡💡 6+ Wh this session · lights on for ~40 min

At bigger totals, switch units so the emoji stay meaningful:

📱📱📱 51+ Wh this week · 3 full phone charges · lights on for ~5 hours

Rules:

  • The number: counted turns × 0.24 Wh, rounded, with a trailing + carrying the floor.
  • 💡 one per Wh, capped at ten. 📱 one per full phone charge (~15 Wh), capped at ten — use phones once bulbs stop being legible.
  • Felt equivalents: lights-on time = Wh ÷ 10 W LED; a phone charge ≈ 15 Wh. Month- and year-scale equivalences (✈️ flights and beyond) need cited energy-to-carbon conversions — that's the energy tracker's job, not this footer's.
  • Nothing else on the line. Method and provenance are answers, not furniture.

Like everything text-shaped, the footer is advisory on every surface (see the enforcement matrix in limits-setup). It works by being followed.

The weekly budget (optional, three modes)

The interview offers four paths; config records them:

ChoiceConfigBehavior
No thanksENERGY_FOOTER=noEnergy never comes up
Inform onlyfooter on, no budgetThe footer, nothing else
Budget + warnENERGY_BUDGET_MODE=warnCrossing the budget gets exactly this, once per week, and nothing more: "🌍 FYI, you've exceeded your weekly energy budget of Wh."
Budget + restrictENERGY_BUDGET_MODE=restrictThe energy-gate.sh hook refuses further prompts once the week's floor crosses the budget — terse, identical, overridable through a deliberate typed act (don't volunteer the command; tell them if they ask)

The budget week is fixed: it starts Monday at the day boundary (default 4AM) and resets the following Monday — a reset you can name, not a rolling window that follows you around.

Always, whenever the footer is on — budget or not:

  • Where a filesystem exists, append one line per session to ~/.config/ai-limits/state/energy-ledger.log: 2026-08-27 wh=2+ turns=8 <one-word context> — keep this format stable and parseable; it is the data source for the budget math, the weekly report, and the first-week baseline a person sets their budget against.
  • Useful scale for picking a number: a full smartphone charge is roughly 15 Wh; an hour of a 10 W LED is 10 Wh; a published chat prompt is about a quarter of a watt-hour.
  • Restrict mode's honest label: it is the softest of the kit's three gates. Quiet hours read the clock; the session gate reads hook-written state; this gate reads a ledger the AI itself writes at session ends — real enforcement fed by best-effort data. The user chose it knowing the numbers are floors, not measurements; never let a refusal pretend otherwise.

The baseline review

When the config has ENERGY_FOOTER=yes and no ENERGY_BUDGET_WH_WEEK, and the ledger holds a week of data: the next limits/energy report opens with the baseline — the week's floor total, rendered honestly — and asks them to set their budget number against it (and the enforcement mode, if 5b recorded "not sure"). This is the one moment the kit proactively asks; it isn't a nag — it's the appointment their own 5a answer scheduled. If they defer, defer; ask again only at the next report they request.

Weekly report

When asked ("energy report", or as part of a limits report): sum the ledger's ranges, render the lightbulb row, compare to the budget if one is set, and state what the number is: a floor estimate of one slice of the true cost — it excludes training, embodied hardware, idle capacity, and anything the providers don't publish. Never let the report imply completeness it doesn't have; where the ledger has gaps, say "not measured" rather than backfilling.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Limits Energy AI skill do?

Make the environmental cost of AI usage visible — floor estimates built from published per-prompt figures, rendered as lightbulb time (💡), with an optional weekly energy budget that informs but never blocks. Use when the user asks "how much energy did this session cost", "what's the environmental cost", "show the energy footer", "set an energy budget", "energy report", or enables environmental awareness in the limits-setup interview.

Why use Limits Energy on TypingMind?

Because you install it once and use it with any model. Limits Energy is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Limits Energy in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bradfrost/skills/tree/main/skills/mental-health/limits-energy. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Limits Energy?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Limits Energy?

As many as you like. As long as a model supports skills, you can use Limits Energy with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Limits Energy AI skill free?

Yes. It is published on GitHub by bradfrost under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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