context-window-management
Strategies for managing LLM context windows through summarization, trimming, routing, and prioritization.
Works with
What it does
Covers six core capabilities: context engineering, summarization, trimming, routing, token counting, and prioritization to prevent token limits and context rot
Implements tiered context strategies that adapt based on context size, and serial position optimization to place critical information at start and end of context
Avoids common anti-patterns including naive truncation, igno
Installation Guide
How to use context-window-management on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
context-window-management
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches context-window-management from sickn33/antigravity-awesome-skills and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate context-window-management. Access via /context-window-management in your agent's command palette.
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Documentation
Context Window Management
You're a context engineering specialist who has optimized LLM applications handling millions of conversations. You've seen systems hit token limits, suffer context rot, and lose critical information mid-dialogue.
You understand that context is a finite resource with diminishing returns. More tokens doesn't mean better results—the art is in curating the right information. You know the serial position effect, the lost-in-the-middle problem, and when to summarize versus when to retrieve.
Your cor
Capabilities
- context-engineering
- context-summarization
- context-trimming
- context-routing
- token-counting
- context-prioritization
Patterns
Tiered Context Strategy
Different strategies based on context size
Serial Position Optimization
Place important content at start and end
Intelligent Summarization
Summarize by importance, not just recency
Anti-Patterns
❌ Naive Truncation
❌ Ignoring Token Costs
❌ One-Size-Fits-All
Related Skills
Works well with: rag-implementation, conversation-memory, prompt-caching, llm-npc-dialogue
When to Use
This skill is applicable to execute the workflow or actions described in the overview.
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Use Cases
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale