Long-running agents — and the
system foundations that sustain them.

Distill a company's docs into agent-ready Skills.

Point it at a folder of product docs. An LLM reads everything and writes two kinds of Skill: detail skills (how to use each capability) and intent skills (which path to pick when several lead to the same goal). Output plugs directly into Claude Code, Cursor, or any agent that reads SKILL.md. Zero human authoring, zero third-party runtime dependencies (pure Python stdlib). GEO closed loop (measure → diagnose → rewrite → re-measure) ensures products get cited by AI search engines; edge-exploration agent auto-discovers cross-product combo scenarios. Ships with auto-generated MCP servers, D3 knowledge graph, live SSE telemetry, and dual-currency subscription billing.

Enable Chinese LLMs to autonomously complete complex software engineering tasks.

A high-level goal decomposes into a DAG of verifiable subgoals. Different models handle planning, execution, review, and verification — no model grades its own work. Failures auto-escalate through three model tiers (Qwen → DeepSeek → strongest available), subgoals execute in parallel, tripwires detect 13 behavioral anomaly patterns in real-time, and an experience system compresses failures into reusable skill templates. Supports multi-day projects (Roadmap → Milestone → Task → Subgoal DAG) with continuous autonomous advancement.

A practitioner's guide to securing web applications in the AI Coding era.

When AI writes 80% of your business logic, where do the security boundaries live? This book dissects the attack surface of LLM-powered applications — prompt injection, privilege escalation, data exfiltration, supply-chain poisoning — and delivers engineering-grade defense-in-depth: pre-commit secret guards, TOCTOU quota-race protection, multi-layer firewall coordination, agent behavior sandboxing. Every pattern is extracted from real production incidents.