From antigravity-awesome-skills
Injects 500K-1M clean tokens of external context into AI agents, auto-summarizes conversations preserving tone/intent, compresses 14-turn history to 800 tokens. Use for long sessions and RAG.
How this skill is triggered — by the user, by Claude, or both
Slash command
/antigravity-awesome-skills:recallmaxThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
RecallMax enhances AI agent memory capabilities dramatically. Inject 500K to 1M clean tokens of external context without hallucination drift. Auto-summarize conversations while preserving tone, sarcasm, and intent. Compress multi-turn histories into high-density token sequences.
RecallMax enhances AI agent memory capabilities dramatically. Inject 500K to 1M clean tokens of external context without hallucination drift. Auto-summarize conversations while preserving tone, sarcasm, and intent. Compress multi-turn histories into high-density token sequences.
Free forever. Built by the Genesis Agent Marketplace.
npx skills add christopherlhammer11-ai/recallmax
RecallMax cleanly injects external context (documents, RAG results, prior conversations) into the agent's working memory. Unlike naive concatenation, it:
As conversations grow, RecallMax automatically summarizes older turns while preserving:
Compress a 14-turn conversation history into ~800 high-density tokens that retain full semantic meaning. The compressed output can be re-expanded if needed.
Built-in cross-reference checks for controversial or ambiguous claims within the conversation context. Flags contradictions and unsupported assertions.
@tool-use-guardian - Tool-call reliability wrapper (also free from Genesis Marketplace)npx claudepluginhub sickn33/antigravity-awesome-skills --plugin antigravity-awesome-skillsInjects 500K-1M clean tokens of external context, auto-summarizes conversations with tone/intent preservation, and compresses 14-turn history into ~800 tokens. Use for long-running agent sessions or large RAG injection.
Designs and evaluates context compression strategies for long-running agent sessions. Use when agents exceed memory limits, need conversation summarization, or optimize tokens-per-task.
Guides creation, editing, and verification of skills for AI coding agents using test-driven development with subagent scenarios. Use when authoring or debugging skills.