context-optimize

name	context-optimize
description	Long context optimization with self-editing — extract key information, compress, summarize, and auto-prune stale context using the H2O + Context-1 patterns. Use this whenever documents are too long, the context window is running low, you need to synthesize multiple files, or accumulated tool results are polluting the context. Triggers immediately on 'compress this', 'summarize this', 'document is too long', 'context overflow', 'clean up context', 'prune context', '컨텍스트 정리'. Triggers on /compress, /prune.
trigger	/compress, /optimize, /prune, "컨텍스트 압축", "문서 압축", "긴 문서 요약", "context overflow", "컨텍스트 정리"
version	2.0.0
updated	"2026-03-27T00:00:00.000Z"

Context Optimize Skill

When to use:

• When you need to analyze a long document or codebase
• When the context window is running low
• When you need to synthesize multiple files
• When cleaning up documents before starting a project implementation
• When accumulated search/tool results are polluting the context (v2.0 self-editing)

Quick Start

# Extract key information (Heavy-Hitter)
/compress optimize <file>

# Compress a document
/compress <file>

# LLM-based summarization (requires Claude CLI)
/compress <file> --llm

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Usage Scenarios

1. Pre-project document cleanup

Situation: Planning docs and specs are too long to read in one pass
Solution:  /compress optimize docs/spec.md --heavy-count=20
Result:    Extract only the top 20 key items for quick understanding

2. Context overload

Situation: "Context window exceeded" or degraded response quality
Solution:  /compress <large-file> --summary-ratio=0.3
Result:    70% compression to free up context headroom

3. Synthesizing multiple files

Situation: Need to reference 10+ files at once
Solution:  /compress build "summarize" docs/*.md
Result:    RAG hybrid extracts only relevant content

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Commands

Command	Description	Example
optimize <file>	Heavy-Hitter extraction	/compress optimize spec.md
compress <file>	Compress (preserve start/end)	/compress README.md
build <query> <files>	RAG hybrid	/compress build "API list" src/*.ts

Options

Option	Description	Default
--heavy-count=N	Number of key items to extract	10
--summary-ratio=N	Compression ratio (0.1–0.9)	0.3
--llm	Use LLM-based summarization	false
--json	Output in JSON format	false

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Technical Principles

H2O (Heavy-Hitter Oracle)

Places critical information at the top to mitigate the "Lost in the Middle" phenomenon:

Type	Priority	Example
h1 header	1	# Title
Class definition	1	class Foo
h2 header	2	## Section
Function definition	2	function bar()
Table header	2	| col1 | col2 |
Code block	3	```javascript
List	4	- item

Bonus system:

• Top 10% of document: priority multiplier 0.8x (higher priority)
• Bottom 10% of document: priority multiplier 0.9x
• Critical keywords (CRITICAL, IMPORTANT, 🔥): multiplier 0.5x

Compressive Context

Older or less important content is summarized; recent and critical content is preserved as-is:

[First 5 lines — preserved as-is]
... (compressed) ...
[Middle sampling]
... (compressed) ...
[Last 5 lines — preserved as-is]

LLM Mode (--llm)

Semantic summarization using Claude CLI:

• No additional API cost — subscription only
• Automatically falls back to heuristic when running inside Claude Code

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How to Run

This skill internally calls contextOptimizer.js:

node project-team/services/contextOptimizer.js <command> <file> [options]

Examples

# Heavy-Hitter extraction
node project-team/services/contextOptimizer.js optimize docs/spec.md --heavy-count=15 --json

# Compress
node project-team/services/contextOptimizer.js compress large-file.md --summary-ratio=0.2

# LLM-based (run in a separate terminal)
node project-team/services/contextOptimizer.js compress large-file.md --llm

# RAG hybrid
node project-team/services/contextOptimizer.js build "API endpoints" src/*.ts

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Self-Editing Context (v2.0)

Inspired by Chroma Context-1: "Model selectively removes irrelevant documents during retrieval to free context capacity." Context-1 achieves 94.1% prune accuracy with this pattern.

The Problem

During long sessions, tool results accumulate in the conversation:

• Early search results become irrelevant as the task evolves
• Stale file reads pollute context when those files have since been modified
• Debugging output from resolved issues wastes context on solved problems

This is context pollution — the same problem Context-1 solves for retrieval.

/prune — Self-Editing Command

When context feels polluted, run /prune (or /compress prune). The process:

1. SCAN: Review all tool results and file reads in the current conversation
2. CLASSIFY each result:
   - KEEP: Still relevant to the current task
   - STALE: Was relevant but task has moved on
   - NOISE: Was never relevant (wrong search, failed attempt)
3. SUMMARIZE stale/noise items into one-line summaries
4. OUTPUT: A compact context summary replacing the bloated results

Prune Decision Criteria

Signal	Classification	Action
File read → file was later modified	STALE	Summarize: "read X, since modified"
Search result → query was refined	STALE	Summarize: "searched X, refined to Y"
Error output → error was fixed	NOISE	Summarize: "fixed error in X"
Debugging trace → bug resolved	NOISE	Drop entirely
Tool result still referenced in current task	KEEP	Preserve as-is
Recent result (last 3 turns)	KEEP	Preserve as-is

When to Self-Edit

Proactively suggest /prune when:

• Session has exceeded 20 tool calls
• The same file has been read 3+ times (likely stale earlier reads)
• A search was followed by a more specific search (earlier one is noise)
• Debugging output exists for a resolved issue

Integration with /compact

/prune is lighter than /compact:

• /prune — removes noise from tool results within the active session
• /compact — compresses the entire conversation for a fresh start

Use /prune first. If still overloaded, then /compact.

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Related Resources

• Service README: project-team/services/README.md
• MCP server: project-team/services/mcp-context-server.js