Context Window Management
- 552 installs
- 30.1k repo stars
- Updated August 4, 2026
- davila7/claude-code-templates
context-window-management is a Claude Code skill that keeps agent conversations coherent and efficient by managing token usage, summarization, trimming, and routing for developers who hit context limits or context rot in
About
context-window-management is a context engineering skill sourced from vibeship-spawner-skills that teaches strategies for summarization, trimming, routing, and avoiding context rot in LLM applications and long agent sessions. Developers reach for it when conversations hit token limits, lose critical information mid-dialogue, or degrade as more tokens accumulate without better results. The skill treats context as a finite resource with diminishing returns and guides curation of the right information density rather than maximizing raw token count. Triggers include context window, token limit, context management, context engineering, and long context keywords.
- Prevents context overflow in long agent sessions
- Automatically summarizes or prunes older messages
- Maintains relevant project knowledge across turns
- Reduces token waste and API costs
- Works with Claude Code, Cursor, and similar agents
Context Window Management by the numbers
- 552 all-time installs (skills.sh)
- Ranked #1,665 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 552 |
|---|---|
| repo stars | ★ 30.1k |
| Last updated | August 4, 2026 |
| Repository | davila7/claude-code-templates ↗ |
How do you manage LLM context window limits?
Keep Claude Code conversations coherent and efficient by intelligently managing token usage and context.
Who is it for?
Developers building long-running coding agents or LLM apps that routinely approach model context token limits.
Skip if: Skip context-window-management when sessions are short single-turn prompts that never approach token ceilings.
When should I use this skill?
A developer mentions context window limits, token budget, context rot, summarization, or long agent conversations degrading.
What you get
Context trimming plan, summarization checkpoints, and routing rules that preserve critical session information.
- context trimming plan
- summarization checkpoints
- routing rules
Files
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
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What problems does context-window-management solve?
context-window-management addresses token limit exhaustion, context rot in long dialogues, and lost critical information mid-session. The skill applies summarization, trimming, and routing so agents retain signal within finite windows.
When should I invoke context-window-management?
context-window-management triggers on keywords like context window, token limit, context management, context engineering, and long context. Use it when LLM conversations degrade or hit model ceilings.