Progressive Skill Disclosure & Context Compaction Engine for Autonomous AI Coding Agents. Reduces prompt token bloat by 96.7% using JIT discovery, ephemeral schema injection, and semantic receipts.
View on GitHub ↗Homepage ↗Progressive Skill Disclosure & Context Compaction Engine for Autonomous AI Coding Agents Preloading hundreds of enterprise tool schemas dilutes model attention and inflates token costs. SkillPrism indexes skills out-of-context and injects only what the model needs, right when it needs it.
⚡ The Problem: Context Bloat & Attention Dilution
When autonomous coding agents (Claude Code, Cursor, Windsurf, Antigravity) connect to enterprise MCP servers or large API catalogs: ~24,500 Tokens Wasted: Full JSON schemas for hundreds of endpoints are dumped into the system prompt on turn 1. Model Hallucinations: When models face 100+ tools, tool selection accuracy drops dramatically ("Lost in the Middle"). Token Cost Explosion: Every reasoning turn repeatedly transmits schemas the agent never touches.
💡 The Solution: SkillPrism Progressive JIT Disclosure
SkillPrism moves the entire tool catalog outside the LLM context: 1. Zero-State (~199 tokens): The agent prompt starts with only two meta-tools: searchskills and loadskill. 2. Sub-Millisecond BM25 Hybrid Search (
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