Nm Conserve Context Optimization

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name: context-optimization description: | Analyze and optimize context window usage with MECW principles, memory tiering, session routing, and subagent coordination version: 1.9.4 metadata: {"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/conserve", "emoji": "\ud83e\udd9e"}} source: claude-night-market source_plugin: conserve

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> Night Market Skill — ported from claude-night-market/conserve. For the full experience with agents, hooks, and commands, install the Claude Code plugin.

Table of Contents

Quick Start

When to Use

Core Hub Responsibilities

Module Selection Strategy

Context Classification

Integration Points

Resources

Context Optimization Hub

Quick Start

Basic Usage

# Analyze current context usage
python -m conserve.context_analyzer

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

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

| Utilization | Status | Action | |-------------|--------|--------| | < 30% | LOW | Continue normally | | 30-50% | MODERATE | Monitor, apply principles | | > 50% | CRITICAL | Immediate 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

| Scenario | Before 2.1.2 | After 2.1.2 | |----------|--------------|-------------| | Large test output | Truncated, partial data | Full output via file reference | | Verbose build logs | Lost after 30K chars | Complete, accessible on-demand | | Context pressure | Less from truncation | Same - 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.

Leverage 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 and the 50% rule.

MECW Theory (Extended): See modules/mecw-theory.md for pressure levels, compliance checking, and monitoring patterns.

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.

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.

Basic Usage

# Analyze current context usage
python -m conserve.context_analyzer

Avoid pre-emptive reads: Large outputs are referenced, not automatically loaded into context.

Read selectively: Use head, tail, or grep on file references.

Leverage 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.