by Teaonly · Agent Tool · ★ 79
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SKILL.mk — A Makefile-Format SKILL Document This project proposes a specification for SKILL documents in Makefile format. These documents offer clear logical chains, low token consumption (on-demand loading), verifiability, and auditability. Why Makefile Format Makefile-format SKILL documents have the following advantages, making them well-suited for integration into Agent frameworks: Built-in Logical DAG: Most SKILLs implicitly contain a logical DAG, which is essentially a Plan Mode. Describing it with an explicit DAG not only reduces the token cost of descriptions, but also improves Agent execution accuracy and lowers error rates. On-Demand Loading: The meta information in SKILL.mk provides a keyword list, where each keyword directly corresponds to a Makefile target. Through a dedicated SKILL.mk loader tool, only the relevant Recipe context is loaded on demand. This effectively reduces token costs at Agent runtime. Strong Verifiability: SKILL.mk itself is verifiable and runnable, and also supports auditability (git tracking, invocation statistics, etc.). It is well-suited for self-evolution, especially since individual Recipes can be optimized independently.
| Stars | 79 |
| Forks | 3 |
| Language | Shell |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 72.5489183517557/100 |
| Open Issues | 3 |
| Last Updated | 2026-05-04 |
| Created | 2026-05-02 |
| Platforms | cli |
| Est. Tokens | ~5k |
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SKILL.mk is Specification and Tools for Makefile-formatted Agent Skills.. It is categorized as a Agent Tool with 79 GitHub stars.
SKILL.mk is primarily written in Shell.
You can find installation instructions and usage details in the SKILL.mk GitHub repository at github.com/Teaonly/SKILL.mk. The project has 79 stars and 3 forks, indicating an active community.
SKILL.mk is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to SKILL.mk on Agent Skills Hub include lenny-skills, repren. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.