Adaptive Skill Granularity Framework Routes Coding Agent Skills at Runtime, Hits 100% Success
dair_ai · x · 2026-10-25
dair-ai highlights an arXiv paper from Shanghai Jiao Tong University, Kuaishou, and University of Warwick that treats skill granularity in coding agents as a runtime decision rather than a fixed choice:
- The agent can pick among primitive tool calls, a macro skill, or hybrid execution that falls back to micro-level recovery when the macro fails
- The framework combines a feature-based granularity controller (using task structure, execution state, and past macro failures) with a finite-state recovery executor that makes fallback an explicit transition instead of an informal retry
- In a 12-task routing study, a learned router reaches 100% success where hand-written heuristics fail; macro-to-micro fallback recovers controlled macro failures that fixed granularity leaves unresolved
The authors argue agent skill libraries should be treated not just as retrievable resources but as controllable, verifiable, and recoverable execution abstractions. Code is open-sourced on GitHub (Jayceon-3/Adaptive-Skill-Agent).
More from coding & agent
- One prompt turns NYC weather data into a perfect 1990s-style academic web page — vboykis · 2026-10-25
- SaaStr founder: AI agents now pick and churn your SaaS vendors — win the agent — morganb · 2026-10-25
- Dev compares Codex vs Claude: one needs constant steering, the other gets intent — menhguin · 2026-10-25
- After reading 1,000+ papers: 25 real-world AI agent use cases worth building — MaryamMiradi · 2026-10-25
- AI trip planner redesign: code review catches accessibility regression in nav links — SeanOliver · 2026-10-25
- Anthropic guide: fold unnecessary subagents into skills and tools, with 2x faster execution reported — omarsar0 · 2026-10-25