BenchClaw：面向智能体环境的Skill-first基准测试构建框架

BenchClaw is a benchmark manufacturing framework designed for agent environments like OpenCode, adopting the Skill-first methodology. It provides a complete standardized process from conception to evaluation, supporting reproducible and auditable benchmark construction. Key features include standardized workflows, Skill-first design, and traceability. This framework addresses the challenges of traditional benchmarks (lack of standardization, poor reproducibility) and adapts to the complexity of agent systems.

Original authors/maintainers: EurecaMoment; Source platform: GitHub; Original link: https://github.com/EurecaMoment/BenchClaw; Update time: 2026-05-31T17:15:14Z

In AI, benchmarks are core to measuring model capabilities, but traditional ones lack standardized processes—leading to irreproducible results, hard comparisons, and high maintenance costs. For agent systems (with non-deterministic, complex behaviors), static benchmarks are ineffective. BenchClaw was created to solve these issues: it's not an executable app or Python package, but a Skill-first framework for agent environments, offering a full workflow for benchmark building, evaluation, and maintenance (developed by EurecaMoment team for OpenCode etc.)

BenchClaw's design focuses on "standardization, reproducibility, auditability" via:

1. Skill-first methodology: Starts with skill definition (not datasets) using SKILL.md contracts (input, output, evaluation criteria, pass conditions) for interpretability and maintainability.
2. Phased execution rules: Breaks the process into stages with clear I/O and rules for transparency and control.
3. Capability cards & quality gates: Describes system capabilities with quality gates to ensure test results meet standards before proceeding.
4. Traceability & rollback: Manages benchmark lineage (full chain from data to results) and supports rollback to stable states.

Key components:

1. SKILL.md contract: Defines each skill (description, input/output specs, evaluation methods, pass standards) for unified comparison/combination.
2. DAG execution engine: Models the process as a directed acyclic graph (nodes = steps, edges = data dependencies) for parallel execution and efficiency.
3. Validation scripts: Checks data quality, result consistency, output compliance—runnable in CI/CD for reliability.
4. Fixed workspace layout: Standardized directory structure for data collection, evidence compilation, benchmark packaging—easy navigation for teams.

BenchClaw applies to:

1. Academic research: Quick benchmark building for specific tasks, standardized outputs for comparison.
2. Industrial evaluation: Internal model assessment systems for consistent results across teams/time; audit function meets compliance needs.
3. Agent capability assessment: Adapts to OpenCode to evaluate agent performance in code generation, debugging, refactoring etc.

Steps to build benchmarks with BenchClaw:

1. Conception: Define goals/scope, write initial SKILL.md.
2. Data generation: Generate/collect test data based on skill definitions.
3. Evaluation: Run the system under test and collect outputs.
4. Report: Generate reports (success rate, error distribution etc.).
5. Diagnosis: Analyze failures to identify system weaknesses.
6. Skill refinement: Adjust skill definitions or test data based on diagnosis.

BenchClaw fills the tool gap in agent benchmarking. Its standardized process and reproducible methods improve industry evaluation quality. As LLMs and agents evolve, demand for high-quality benchmarks grows—BenchClaw's Skill-first and audit-focused design provides valuable references. Being open-source, the community can contribute new skills/validation methods to form a positive ecosystem.