No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by metaevo-ai · Agent Tool · ★ 153
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[ICML 2026] Meta Context Engineering via Agentic Skill Evolution Superseding Static Harnesses with Learnable Skills for Context Optimization This repository accompanies the paper Meta Context Engineering via Agentic Skill Evolution. Meta Context Engineering (MCE) is a bi-level agentic framework that co-evolves context engineering skills and context artifacts, replacing rigid CE heuristics with learnable skills that automatically discover optimal context representations and optimization procedures. Key Results MCE achieves consistent improvements across five diverse domains (finance, chemistry, medicine, law, AI safety): Efficiency gains: 13.6× faster training than ACE 4.8× fewer rol
| Stars | 153 |
| Forks | 20 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 73.3930551953919/100 |
| Open Issues | 1 |
| Last Updated | 2026-05-04 |
| Created | 2026-01-29 |
| Platforms | claude-code, python |
| Est. Tokens | ~790k |
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meta-context-engineering is [ICML 2026] Meta Context Engineering via Agentic Skill Evolution. It is categorized as a Agent Tool with 153 GitHub stars.
meta-context-engineering is primarily written in Python. It covers topics such as agent-skills, agents, claude.
You can find installation instructions and usage details in the meta-context-engineering GitHub repository at github.com/metaevo-ai/meta-context-engineering. The project has 153 stars and 20 forks, indicating an active community.
meta-context-engineering is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to meta-context-engineering on Agent Skills Hub include skillport, awesome-agent-skills, claude-code-sub-agent-collective. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: