From antigravity-awesome-skills
Injects 500K–1M tokens of clean context into AI agents while auto-summarizing conversations with tone, intent, and fact preservation. Compresses 14-turn history into 800 tokens. Useful for long-running agent sessions, large RAG injection, and fact verification across threads.
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/agentic-awesome-skills --plugin antigravity-awesome-skills43plugins reuse this skill
First indexed Jun 3, 2026
Showing the 6 earliest of 43 plugins
Injects 500K–1M tokens of clean context into AI agents while auto-summarizing conversations with tone, intent, and fact preservation. Compresses 14-turn history into 800 tokens. Useful for long-running agent sessions, large RAG injection, and fact verification across threads.
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.
Creates structured, bite-sized implementation plans from specs or requirements before writing code. Useful for breaking down multi-step tasks into testable steps with file structure and task boundaries.