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A structured skill that transforms how AI agents create implementation plans. Learn why each component exists and how it prevents common planning failures.
Llm
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A technical deep dive into OpenClaw agent internals — 15+ AI providers with auto-discovery, a 9-layer tool policy system, context window management with cache-aware pruning, the skills loading pipeline, and Docker sandboxing with zero-trust defaults.- Published on
A technical deep dive into OpenClaw session management — hierarchical key resolution, file-based persistence with JSONL transcripts, sub-agent spawning with lifecycle tracking, cross-session communication via A2A ping-pong, and the security model that keeps it all isolated.- Published on
An end-user guide and technical deep dive into OpenClaw memory system — how plain Markdown files, hybrid vector search, atomic reindexing, and pre-compaction flushes give an AI assistant persistent, searchable memory across sessions.- Published on
An evidence-based analysis of prompt injection defenses based on 150+ academic papers. Single defenses achieve 45-60% effectiveness; multi-layered approaches reach 87-94%. The attack surface in AI agents like OpenClaw is massive—and the consequences are devastating.- Published on
A comprehensive comparison of OpenSpec and GitHub Spec Kit. Two open-source frameworks for structured AI-assisted software development.- Published on
An evidence-based analysis of LLM context utilization based on 11 academic studies. Learn why your 200K context window might only be 50% effective, and how to engineer prompts that actually work.