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Token Conservation

Community
athola
token-conservation

Enforces token quota management at session start with conservation and compression checks. Use at the start of every session or before large context loads.

Overview

Publisherathola
Repositoryclaude-night-market
Skill nametoken-conservation
Stars
343
Forks
37
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 athola on GitHub. Read the source before you install it.

Installation

Install the Token Conservation 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/athola/claude-night-market/tree/master/plugins/conserve/skills/token-conservation
  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/athola/claude-night-market.git /tmp/claude-night-market
mkdir -p .claude/skills
cp -r /tmp/claude-night-market/plugins/conserve/skills/token-conservation .claude/skills/token-conservation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Token Conservation 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 Token Conservation 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 Token Conservation 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.

Token Conservation Workflow

When To Use

  • Run at the start of every session and whenever prompt sizes or tool calls begin to spike.
  • Mandatory before launching long-running analyses, wide diffs, or massive context loads.

When NOT To Use

  • Context-optimization already handles the scenario
  • Simple queries with minimal context

Required TodoWrite Items

  1. token-conservation:quota-check
  2. token-conservation:context-plan
  3. token-conservation:delegation-check
  4. token-conservation:compression-review
  5. token-conservation:logging

Step 1 – Quota Check (quota-check)

  • Record current session duration and weekly usage (from /status or notebook). Note the 5-hour rolling cap and weekly cap highlighted in the Claude community notice.
  • Capture remaining budget and set a max token target for this task.

Step 2 – Context Plan (context-plan)

  • Set a discovery read budget BEFORE reading any files. Count each Read call and each content-mode Grep as one read. Glob and files-with-matches Grep are free.

    • Implement from spec/requirements: max 8 reads
    • Bug fix at known location: max 5 reads
    • Refactor with known scope: max 1 read per file being changed
    • Open exploration: max 15 reads
  • Read order (most valuable first): spec/requirements, files to modify, imports/interfaces, then stop and start writing.

  • When budget is spent: ask the user if more context is needed. Do NOT self-authorize additional reads. Only explicit user approval overrides the budget.

  • Prefer Read with offset/limit params or Grep tool over loading whole files. A Read targeting <50 lines counts as 0.5 reads. Avoid cat/sed/awk via Bash: Claude Code 2.1.21+ steers toward native file tools (Read, Edit, Write, Grep, Glob).

  • PDFs (Claude Code 2.1.30+): Use Read with pages: "1-5" for targeted PDF reading instead of loading entire documents. Large PDFs (>10 pages) return a lightweight reference when @-mentioned, so use the pages parameter to read specific sections. Hard limits: 100 pages max, 20MB max per PDF. Exceeding these previously locked sessions permanently (fixed in 2.1.31).

  • Convert prose instructions into bullet lists before prompting so only essential info hits the model.

  • Before a web search or fetch, decide whether the claim needs a source at all. modules/source-selection.md sorts claims into those worth citing (versions, performance, security, APIs, pricing) and those that are not, with the token cost of each approach.

Step 3 – Delegation Check (delegation-check)

  • Evaluate whether compute-intensive tasks can go to Qwen MCP or other external tooling (use qwen-delegation skill if needed).
  • For local work, favor deterministic scripts (formatters, analyzers) instead of LLM reasoning when possible.

Step 4 – Compression Review (compression-review)

  • Summarize prior steps/results before adding new context. Remove redundant history, collapse logs, and avoid reposting identical code.
  • Use prompt caching ideas: reference prior outputs instead of restating them when the model has already processed the information (cite snippet IDs).
  • Decide whether the current thread should be compacted:
    • If only recent context is stale, use "Summarize from here" (Claude Code 2.1.32+) via the message selector to partially summarize the conversation. This preserves recent context while compressing older portions
    • If the active workflow is finished and earlier context will not be reused, instruct the user to run /new
    • If progress requires the existing thread but the window is bloated, prompt them to run /compact before continuing
  • Automatic memory (Claude Code 2.1.32+): Claude now records and recalls session memories automatically. This adds minor token overhead but improves cross-session continuity. No action needed: be aware it contributes to baseline context usage.

Step 5 – Logging (logging)

Document the conservation tactics that were applied and note the remaining token budget. If the budget is low, explicitly warn the user and propose secondary plans. Record any recommendations made regarding the use of /new or /compact, or justify why neither was necessary, to inform future context-handling decisions.

Output Expectations

  • A short explanation of token-saving steps, delegated tasks, and remaining runway.
  • Concrete next-action list that keeps the conversation lean (e.g.):
    • "next turn: provide only failing test output lines 40-60"
  • Explicit reminder about /new or /compact whenever you determine it would save tokens (otherwise state that no reset/compaction is needed yet).

Exit Criteria

  • All 5 TodoWrite items created and checked off in order: quota-check, context-plan, delegation-check, compression-review, logging
  • Discovery read budget set before the first Read call, with the task type (spec/bug/refactor/exploration) and max-reads limit recorded
  • Output summary states the remaining token runway (or warns "budget low") and includes an explicit /new or /compact recommendation, or a justification for why neither is needed
  • No additional file reads taken after the read budget is exhausted without explicit user approval in the same session
  • Every version, performance, security, API or pricing claim in the output carries a citation or an uncertainty marker, per modules/source-selection.md

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 Token Conservation AI skill do?

Enforces token quota management at session start with conservation and compression checks. Use at the start of every session or before large context loads.

Why use Token Conservation on TypingMind?

Because you install it once and use it with any model. Token Conservation 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 Token Conservation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/athola/claude-night-market/tree/master/plugins/conserve/skills/token-conservation. 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 Token Conservation?

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 Token Conservation?

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

Is the Token Conservation AI skill free?

Yes. It is published on GitHub by athola 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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