SkillsBench

Overview

SkillsBench evaluates whether coding agents can discover and apply task-bundled Agent Skills. Each task contains an instruction, an optional skill directory, a Docker environment, an oracle solution, and a verifier. EvalScope builds the task Docker image, runs the selected agent or oracle in that image, then executes the task verifier.

Task Description

• Task Type: Agent skill usage / tool-assisted task completion

• Input: The natural-language task prompt from task.md

• Output: Files or state changes produced inside the task container, scored by the task verifier

• Dataset: Local SkillsBench task repository supplied through extra_params.tasks_dir

• Environment: Per-task Docker image built from environment/Dockerfile

• Skills: Optional task-bundled skills from environment/skills

• Metric: score from /logs/verifier/reward.txt; success is 1 when score > 0

Key Features

• Builds or reuses a content-hashed Docker image for each selected task.

• Runs the task in no-skill or with-skill mode without mixing the two conditions in one EvalScope run.

• Injects task-bundled skills through EvalScope’s agent skill runtime instead of baking runner-specific skill paths into the image.

• Supports EvalScope native agents and external agent runners through the shared agent environment interface.

• Saves verifier stdout, reward, and optional CTRF artifacts under the run output directory.

Evaluation Notes

• Default skill_mode is no-skill.

• Run no-skill and with-skill separately, then compare runs with EvalScope’s run comparison tools.

• self-gen and tasks-extra are not supported by this adapter version.

• runner='oracle' runs the official oracle/solve.sh for smoke testing; agent runs require agent_config.

• The verifier executes verifier/test.sh and may install dependencies from the network.

Scoring and Comparison

• score is the verifier reward parsed from /logs/verifier/reward.txt.

• success is derived locally as 1.0 when score > 0, otherwise 0.0.

• EvalScope does not automatically compute the no-skill versus with-skill delta; run both modes and compare their reports.

Properties

Property	Value
Benchmark Name	skillsbench
Dataset ID	skillsbench
Paper	N/A
Tags	Agent, MultiTurn
Metrics	score
Default Shots	0-shot
Evaluation Split	N/A

Data Statistics

Statistics not available.

Sample Example

Sample example not available.

Prompt Template

Prompt Template:

{question}

Extra Parameters

Parameter	Type	Default	Description
tasks_dir	str	``	Path to the SkillsBench tasks directory.
task_ids	list	[]	Optional list of task ids to run.
skill_mode	str	no-skill	SkillsBench skill mode. Choices: [‘no-skill’, ‘with-skill’]
force_rebuild	bool	False	Force rebuilding task Docker images.
agent_timeout_sec	float	None	Override task agent timeout.
verifier_timeout_sec	float	None	Override task verifier timeout.
runner	str	agent	Use “oracle” to run oracle/solve.sh instead of an agent. Choices: [‘agent’, ‘oracle’]

Usage

Using CLI

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets skillsbench \
    --limit 10  # Remove this line for formal evaluation

Using Python

from evalscope import run_task
from evalscope.config import TaskConfig

task_cfg = TaskConfig(
    model='YOUR_MODEL',
    api_url='OPENAI_API_COMPAT_URL',
    api_key='EMPTY_TOKEN',
    datasets=['skillsbench'],
    dataset_args={
        'skillsbench': {
            # extra_params: {}  # uses default extra parameters
        }
    },
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)