Skills context-optimization
📦

context-optimization

Safe

Optimize Context Windows

Also available from: ChakshuGautam,Asmayaseen,muratcankoylan

Context windows limit what AI models can process at once. This skill provides techniques to maximize effective context capacity through compaction, masking, KV-cache optimization, and partitioning, effectively doubling or tripling what your AI can handle.

Supports: Claude Codex Code(CC)
🥉 72 Bronze
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Test it

Using "context-optimization". Context utilization at 85%, conversation is becoming slow and expensive

Expected outcome:

Applied compaction: summarized 20 previous messages into 3 key decisions, reduced context by 60%. Triggered observation masking on tool outputs from turns 1-5.

Using "context-optimization". Need to process a 50-page document with 32k context window

Expected outcome:

Partitioned document into 4 sections. Assigned each to isolated sub-agent. Aggregated results: all sections processed, final summary fits in 8k tokens.

Using "context-optimization". System prompt and tool definitions repeat in every request

Expected outcome:

Reordered context: system prompt first, then tool definitions, then conversation. Achieved 75% cache hit rate, reducing latency by 40%.

Security Audit

Safe
v1 • 2/24/2026

All 16 static findings are false positives. The skill is a documentation/guide containing code examples for context optimization. Python code snippets were incorrectly flagged as shell commands, and text patterns like 'MD5' in '3+ turns' and skill names were misidentified as security issues. No actual security risks present.

1
Files scanned
187
Lines analyzed
0
findings
1
Total audits
No security issues found
Audited by: claude

Quality Score

38
Architecture
100
Maintainability
87
Content
34
Community
100
Security
91
Spec Compliance

What You Can Build

Long-running AI Agents

Build production AI agents that maintain context over extended sessions without hitting token limits

Large Document Processing

Process documents larger than the context window by partitioning and aggregating results

Cost Reduction

Reduce API costs by minimizing token usage through caching and compression strategies

Try These Prompts

Basic Context Check 
Check the current context utilization. If it exceeds 70%, apply compaction by summarizing older messages and preserving key decisions.
Tool Output Masking 
For tool outputs from 3+ turns ago that have served their purpose, replace them with compact references containing only key findings.
Cache-Friendly Ordering 
Reorder context elements to maximize cache hits: place system prompt and tool definitions first, then reusable content, then unique content last.
Sub-Agent Partitioning 
Split the current task into independent subtasks. Assign each to a separate sub-agent with isolated context. Aggregate results after all complete.

Best Practices

  • Measure before optimizing - establish baseline token usage and performance metrics
  • Apply compaction before masking - summarization preserves more signal than removal
  • Design for cache stability - use consistent formatting and avoid dynamic content in prompts

Avoid

  • Aggressive compression - compressing below 50% causes significant quality loss
  • Masking critical observations - never mask data needed for current reasoning
  • Ignoring monitoring - optimization effectiveness degrades over time without measurement

Frequently Asked Questions

Does this skill actually increase the context window?
No. This skill optimizes how you use the available context, making it feel larger by removing redundancy and compressing data.
What is the best optimization strategy for conversation-heavy tasks?
Compaction with summarization works best. Summarize old conversation turns while preserving key decisions and commitments.
How much token reduction can I expect?
Compaction achieves 50-70% reduction with less than 5% quality loss. Masking achieves 60-80% reduction in masked observations.
Does caching work across different conversations?
Prefix caching only works when prompts have identical prefixes. Keep system prompts stable to maximize cache hits.
When should I use sub-agent partitioning?
Partition when a task is too complex for one context, or when subtasks have conflicting context requirements.
How do I know when to trigger optimization?
Monitor token utilization above 80%, response quality degradation, or increasing latency as primary triggers.

Developer Details

Author

sickn33

License

MIT

Repository

https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/context-optimization

Ref

main

File structure

📄 SKILL.md