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npx skills add https://github.com/Kingxiao/agentforge --skill agentforge-context
Please help me install this Agent Skill. Skill: agentforge-context Source: https://github.com/Kingxiao/agentforge/tree/main/agentforge-context Command: npx skills add https://github.com/Kingxiao/agentforge --skill agentforge-context
Context engineering is the agent's cognitive bandwidth management. It ensures finite context windows are used efficiently, memory is organized, and prompts are optimized for long-running tasks across multiple phases.
Load and enable the Phase 3 Context Engineering prompts, activate layered prompts, prompt caching, and automatic compression in your agent to improve throughput and lower token costs.
High-intent search queries and answers about installing and using this skill.
Manage token budgets for long-running AI agents by implementing auto-compact memory management and prompt caching. This optimizes finite context windows to improve throughput and lower costs across multi-phase workflows.
Context engineering for multi-tenant AI workflows isolates per-request contexts securely while organizing memory. It applies layered prompts and progressive disclosure to load context incrementally without cross-tenant data leakage.
Implement RAG budgeting for large-scale codebase tasks by generating codebase repo maps and applying compression. This controls token usage and allocates context efficiently when processing cross-repo information.
Yes, this approach works for stateless platform environments by utilizing layered system prompts and memory caching. It reconstructs necessary context per request without relying on persistent server-side session states.
The best way to reduce token costs in multi-phase agent workflows is combining prompt caching with automatic compression. This prevents redundant processing and discards inactive context to maximize cost efficiency.
soo-kate-yeonManages token budgets, session persistence, and multi-agent context transfers for workflows with strict constraints.
YoraexeApply cost-reduction patterns to cloud-native apps and serverless functions.
JhonHanderConstruct RAG pipelines with OpenAI embeddings and FAISS, Chroma, or Pinecone vector stores.
awrshiftManage layered memory for Claude Code agents across sessions.
