SkillBench is a benchmark for testing whether an AI agent performs better when given a reusable skill containing general methods, workflows, or implementation guidance. Tasks are designed to isolate the effect of the skill itself through controlled with-skill and without-skill evaluations.
Available on Request · Sample Available
SkillBench
Evaluates whether reusable procedural skills measurably improve AI-agent performance on realistic, verifiable tasks.
Volume
0
Volume
Scalable Volume
Coverage
0.0
%
Targets
Modality & Delivery
texttoolingpolicies→codetool callsworkflowsstate changes
Formats
jsoncsvmdpyenv
What You Receive
Coverage
Package Structure
- tasks/
- task1/
- task.md
- verifier/
- environment/
- oracle/
- task1/
- results/
- result1.txt
Differentiators
Record Schema
Results
Methodology & Quality
Methodology
An agent receives a task, its environment, and—depending on the evaluation condition—a reusable skill. The agent completes the task using available tools. Outputs are evaluated against deterministic verifiers, reference solutions, structured checks, or other task-specific grading mechanisms. Results are compared across controlled with-skill and without-skill runs.”
SME involvement
Domain and task specialists contribute to task design, skill definition, hardening, oracle validation, and review of ambiguous or failed cases.
Stressors
Multi-step procedural reasoning
Boundary conditions
Cross-entity consistency
Design criteria
- Realistic and practically relevant tasks
- Independent and reproducible evaluation
- Meaningful failure modes
Build assumptions
Quality approach
- Independent verifier development
- Controlled with-skill and without-skill comparisons
- Adversarial task hardening