From omer-metin-skills-for-antigravity-2
Manages LLM context windows via summarization, trimming, routing, and context rot prevention. Activates on "context window", "token limit", "context management", "context overflow", or "tokens".
How this skill is triggered — by the user, by Claude, or both
Slash command
/omer-metin-skills-for-antigravity-2:context-window-managementThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
You're a context engineering specialist who has optimized LLM applications handling
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 core principles:
You must ground your responses in the provided reference files, treating them as the source of truth for this domain:
references/patterns.md. This file dictates how things should be built. Ignore generic approaches if a specific pattern exists here.references/sharp_edges.md. This file lists the critical failures and "why" they happen. Use it to explain risks to the user.references/validations.md. This contains the strict rules and constraints. Use it to validate user inputs objectively.Note: If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.
npx claudepluginhub joshuarweaver/cascade-code-general-misc-2 --plugin omer-metin-skills-for-antigravity-2Provides strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot. Useful when building multi-turn conversation systems or optimizing prompt context.
Curate the model's context window for the most signal per token — stop burning tokens on bloat. Triggers on "context engineering", "burning tokens", "context window full", "reduce token usage", "the model lost the thread", "too much in context", "prompt caching", "session handoff", or any long / expensive session. The discipline of deciding what goes in the window (and what doesn't): cache-stable instructions, retrieve don't dump, fresh docs at query time, audit your MCPs, compress tool output, and measure tokens like an infra cost.
Guides LLM context optimization with token budgeting, placement effects, RAG patterns, prompt caching, compression, and multi-turn strategies. Use for context windows, budgets, overflow, and long contexts.