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Usage Limit Reducer

Community
Dubibubii
usage-limit-reducer

Use when the user is hitting Claude usage limits, burning through tokens fast, running a long conversation, or asks how to use Claude Code more efficiently. Triggers on phrases like "hit my limit", "running out of tokens", "usage limit", "save tokens", "reduce usage", "am I wasting tokens", "this chat is getting long", "which model should I use". Diagnoses the current session, runs a real token-usage report from local JSONL logs, and applies Dubi's 11 rules for reducing Claude usage.

Overview

PublisherDubibubii
Repositoryusage-limit-reducer
Skill nameusage-limit-reducer
Stars
202
Forks
39
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Usage Limit Reducer 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/Dubibubii/usage-limit-reducer/tree/main
  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/Dubibubii/usage-limit-reducer.git \
  .claude/skills/usage-limit-reducer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Usage Limit Reducer 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 Usage Limit Reducer 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 Usage Limit Reducer 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.

Usage Limit Reducer

Apply Dubi's 11 rules for cutting Claude token usage. Claude re-reads the entire conversation every turn, so 98.5% of tokens often go to re-reading history instead of generating responses. This skill diagnoses where the user's tokens are going and applies the rules that actually move the needle.

How to run this skill

Do the steps in order. Skip any step that clearly doesn't apply, but don't skip all of them — the value is in matching rules to what the user is actually doing.

Step 1 — Run the real token-usage report

Rule #4: "you can't fix what you can't measure." Claude Code already writes every token, model, and timestamp to ~/.claude/projects/<project>/<session>.jsonl. Run the bundled script to show the breakdown (use the absolute path to scripts/usage-report.py inside this skill's directory):

bash
python3 <SKILL_DIR>/scripts/usage-report.py --days 7

Flags: --days N (default 7), --project <substring> to scope by cwd, --json for machine-readable output. Share the headline numbers with the user — cache-hit % and model mix are the two that matter most. A low cache-hit % (under ~60%) means too many fresh chats or cache-busting edits; high Opus share for routine work means Rule #8 applies.

Step 2 — Diagnose the current session

Before advising, check three things:

  1. Conversation length. If this session already has many turns, rule #2 applies directly. Suggest /compact (summarize in place) or /clear then paste a one-paragraph summary as the first message of the new chat.
  2. CLAUDE.md presence. Check the project root for CLAUDE.md and ~/.claude/CLAUDE.md. If missing, rule #6 applies — offer to create one with the user's role, style, and project conventions so every new session doesn't burn 3–5 messages on setup.
  3. Model in use. If the user is on Opus for trivial tasks (grammar, formatting, short answers, quick renames), rule #8 applies — recommend /model claude-haiku-4-5 for those and keep Opus for deep work.

Step 3 — Apply the rules that match

Pick the 2–4 rules most relevant to what the user is doing right now. Don't dump all 11 on them. For each rule you pick, say (a) what to do, (b) why it works, (c) the concrete next action.

#RuleClaude Code action
1Don't follow up to correct — restart with a fixed promptWhen Claude misunderstands, /clear and re-prompt instead of piling on corrections
2Fresh chat every 15–20 turns/compact to summarize in place, or /clear + paste summary
3Batch questions into one messageCombine related asks into one prompt; Claude often answers better with full picture
4Track actual token usageRun the script in Step 1
5Reuse recurring contextPut it in CLAUDE.md, a skill, or .context/ — not re-pasted each session
6Set up memory / user preferencesCreate or update CLAUDE.md; use the memory system under ~/.claude/projects/*/memory/
7Turn off features you don't useAudit ~/.claude/settings.json for unused MCP servers, hooks, permissions
8Use Haiku for simple tasks/model claude-haiku-4-5 for grammar, drafts, formatting, quick lookups
9Spread work across the day5-hour rolling window — split into 2–3 sessions instead of one marathon
10Work off-peakPeak is 5–11am PT / 8am–2pm ET weekdays; evenings and weekends stretch your plan
11Enable Overage as a safety netSettings → Usage on Pro/Max plans — pay-as-you-go kicks in at the limit

Step 4 — Offer concrete next actions

End with a short, ordered list of what to do now, based on the diagnosis. Examples:

  • "Run /compact — this conversation is 40+ turns."
  • "I can create a CLAUDE.md with your preferences — want me to?"
  • "Switch to Haiku for this rename: /model claude-haiku-4-5."

Don't recite every rule. Don't lecture. Match the advice to what the session actually shows.

Rules not implementable in Claude Code

  • Rule #1 ("Edit your prompt") is a claude.ai web UI feature — in Claude Code the equivalent is /clear + restart.
  • Rule #5 ("Upload to Projects") is also web-only — use CLAUDE.md, skills, and .context/ instead.
  • Rule #11 (Overage) is plan-level — mention it, don't implement it.

What this skill does NOT do

  • It does not silently change the user's model or settings. Recommend, then act only on confirmation.
  • It does not summarize-and-clear without asking — the user may have unsaved context in the conversation.
  • It does not send data anywhere. The usage report is strictly local.

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 Usage Limit Reducer AI skill do?

Use when the user is hitting Claude usage limits, burning through tokens fast, running a long conversation, or asks how to use Claude Code more efficiently. Triggers on phrases like "hit my limit", "running out of tokens", "usage limit", "save tokens", "reduce usage", "am I wasting tokens", "this chat is getting long", "which model should I use". Diagnoses the current session, runs a real token-usage report from local JSONL logs, and applies Dubi's 11 rules for reducing Claude usage.

Why use Usage Limit Reducer on TypingMind?

Because you install it once and use it with any model. Usage Limit Reducer 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 Usage Limit Reducer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Dubibubii/usage-limit-reducer/tree/main. 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 Usage Limit Reducer?

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 Usage Limit Reducer?

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

Is the Usage Limit Reducer AI skill free?

It is published on GitHub by Dubibubii. Check the repository for licensing terms. 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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