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Context Optimization

Community
athola
context-optimization

Optimizes context window via MECW principles and memory tiering. Use when context exceeds 30% or before long multi-step tasks.

Overview

Publisherathola
Repositoryclaude-night-market
Skill namecontext-optimization
Stars
343
Forks
37
Bundled files
12
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.

  • 12 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 Context Optimization 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/context-optimization
  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/context-optimization .claude/skills/context-optimization
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Context Optimization 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 Context Optimization 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 Context Optimization 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.

Context Optimization Hub

When To Use

  • Threshold Alert: When context usage approaches 50% of the window.
  • Complex Tasks: For operations requiring multi-file analysis or long tool chains.

When NOT To Use

  • Simple single-step tasks with low context usage
  • Already using mcp-code-execution for tool chains

Core Hub Responsibilities

  1. Assess context pressure and MECW compliance.
  2. Route to appropriate specialized modules.
  3. Coordinate subagent-based workflows.
  4. Manage token budget allocation across modules.
  5. Synthesize results from modular execution.

Module Selection Strategy

python
def select_optimal_modules(context_situation, task_complexity):
    if context_situation == "CRITICAL":
        return ["mecw-assessment", "subagent-coordination"]
    elif task_complexity == "high":
        return ["mecw-principles", "subagent-coordination"]
    else:
        return ["mecw-assessment"]

Context Classification

UtilizationStatusAction
< 30%LOWContinue normally
30-50%MODERATEMonitor, apply principles
> 50%CRITICALImmediate optimization required

Large Output Handling (Claude Code 2.1.2+)

Behavior Change: Large bash command and tool outputs are saved to disk instead of being truncated; file references are provided for access.

Impact on Context Optimization

ScenarioBefore 2.1.2After 2.1.2
Large test outputTruncated, partial dataFull output via file reference
Verbose build logsLost after 30K charsComplete, accessible on-demand
Context pressureLess from truncationSame - only loaded when read

Best Practices

  • Avoid pre-emptive reads: Large outputs are referenced, not automatically loaded into context.
  • Read selectively: Use head, tail, or grep on file references.
  • Use full data: Quality gates can access complete test results via files.
  • Monitor growth: File references are small, but reading the full files adds to context.

Integration Points

  • Token Conservation: Receives usage strategies, returns MECW-compliant optimizations.
  • CPU/GPU Performance: Aligns context optimization with resource constraints.
  • MCP Code Execution: Delegates complex patterns to specialized MCP modules.

Resources

  • MECW Theory: See modules/mecw-principles.md for core concepts, the 50% rule, and quick-start code examples.
  • Context Analysis: See modules/mecw-assessment.md for risk identification.
  • Workflow Delegation: See modules/subagent-coordination.md for decomposition patterns.
  • Context Waiting: See modules/context-waiting.md for deferred loading strategies.
  • Cache Alignment: See modules/cache-aligned-prefixes.md for ordering context so provider KV caches hit (stable prefix first, volatile last).
  • Compression Choice: See modules/compression-strategies.md to pick one of /clear and /catchup, a continuation agent, archive and summarize, or delegation, with savings and risk.
  • Large Tool Outputs: See modules/reversible-compression.md for archiving an oversized output to a retrievable handle.
  • Pasted Logs: See modules/log-debugging-hygiene.md for filtering a log before any compression. /filter-log anchors on it.

Troubleshooting

Common Issues

If context usage remains high after optimization, check for large files that were read entirely rather than selectively. If MECW assessments fail, ensure that your environment provides accurate token count metadata. For permission errors when writing output logs to /tmp, verify that the project's temporary directory is writable.

Exit Criteria

  • Context pressure assessed against the MECW 50% rule
  • A memory tier or routing decision recorded for the current state
  • Large outputs referenced by file or handle, not read in full
  • When the request controls the provider payload, prefix ordering checked against modules/cache-aligned-prefixes.md (stable first, volatile last)
  • Optimization downgraded to advisory when the harness already caches (cache writes cost more than they save on non-repeated prefixes)

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 Context Optimization AI skill do?

Optimizes context window via MECW principles and memory tiering. Use when context exceeds 30% or before long multi-step tasks.

Why use Context Optimization on TypingMind?

Because you install it once and use it with any model. Context Optimization 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 Context Optimization in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/athola/claude-night-market/tree/master/plugins/conserve/skills/context-optimization. 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 Context Optimization?

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 Context Optimization?

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

Is the Context Optimization 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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