An 83-page paper from Wavestone AI Lab dissects 11 coding harnesses, including Claude Code, Codex CLI, Gemini CLI and OpenClaw. Across roughly 4 million lines of code, none imports a general-purpose agent framework and none retrieves code with vector embeddings. If you are building or choosing an agent, you can follow the paper's 18 recommendations and start from one bash tool and one while loop.
MUSE-Autoskill (2026) introduces a five-stage skill lifecycle framework. Self-created skills achieve 60.35% (+7.16%) on SkillsBench overall, and an impressive 87.94% on tasks where skill generation succeeds — surpassing the human-authored skill ceiling. This post synthesizes six arXiv papers to map the full landscape of skill evolution research.
The hard part of LLM agents is not building function calling, skills, code interpreter, and document tools individually -- it is assembling them into a system that selects the right tool, writes code when needed, decomposes tasks, verifies results, and resists prompt injection. This post organizes the key papers into six engineering decisions: function calling reliability, tool/skill selection, code-as-action, multi-step planning, skill systems, and safety plus document generation.