SkillsBench by MilkeyAI evaluates whether Agent Skills improve AI coding agents on realistic, verifiable tasks. The benchmark is designed for teams that need evidence before they rely on Skills in production workflows.
What SkillsBench Measures
SkillsBench compares agent performance with and without curated Skills. This paired setup makes the Skill impact visible instead of mixing it into a single leaderboard score.
| Concept | Meaning |
|---|---|
| Agent | The full system that plans, edits files, runs tools, and attempts the task. |
| Model | The underlying language model used by the agent. |
| Harness | The execution environment around the model, including tools and Skill loading. |
| Skill | Reusable procedural knowledge such as instructions, examples, scripts, or references. |
| Verifier | A deterministic check that decides whether the task was completed correctly. |
The key question is simple: did the Skill change the verified outcome?
How to Read the Benchmark
Use the leaderboard to compare agent-model configurations, but read each score as an evaluation of the full stack: model, harness, and Skill usage.
The most useful comparisons are:
- no Skills vs. curated Skills,
- curated Skills vs. self-generated Skills,
- smaller models with Skills vs. larger models without Skills,
- domain-level gains instead of only global averages,
- tasks where Skills reduce performance.
SkillsBench is most valuable when it shows where Skills help and where they do not. A negative Skill result is still useful because it shows where extra context may distract the agent.
Evaluation Flow
SkillsBench tasks follow a repeatable evaluation flow:
- Prepare a self-contained task environment.
- Run the agent without curated Skills.
- Run the agent with relevant curated Skills available.
- Score each attempt with a deterministic verifier.
- Compare the pass-rate delta across trials, domains, and configurations.
This design keeps the benchmark focused on completion, not subjective answer quality.
What Makes a Good Skill
A good Skill is compact, procedural, and testable. It should describe when it applies, what steps matter, and how the agent can validate progress.
Strong Skills usually include:
- a narrow scope,
- concrete workflow steps,
- important files, commands, or constraints,
- examples only when they improve execution,
- and references that are directly useful for the task.
Avoid turning Skills into long documentation dumps. More context is not automatically better.
Recommended Starting Points
Start with the leaderboard to understand current agent-model performance. Then read the launch post for the benchmark rationale and research findings.
For the full research context, read the launch post.