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ClawMark
ClawMark is a living-world benchmark positioned around multi-day, multimodal coworker agents, with 100 tasks spread across professional domains and tool-backed work environments.
The official repository presents ClawMark as a benchmark for evaluating coworker-style agents across timelines, multimodal evidence, and multiple external systems. This page is a factual editorial overview for reference, not an endorsement or exhaustive review. Project terms and usage conditions can differ, so readers should review the original materials independently.
What it is
A benchmark for coworker agents
ClawMark is framed as an evaluation benchmark rather than an agent product, with the repository centered on long-running work tasks that unfold across several days and several tool environments.
Why it stands out
Multi-day and multimodal pressure
The notable angle is the combination of timelines, real tool backends, multimodal evidence, and deterministic scoring instead of a single-turn prompt or a lighter toy environment.
Availability
Repository with tasks and scoring framework
The public reference point is a GitHub repository containing the benchmark framework, task layouts, scoring setup, and environment instructions for readers who want to inspect how the evaluation is structured.
Why it matters
Why readers may notice it
ClawMark matters because many agent benchmarks still feel too narrow or too short. A benchmark built around multi-day work, changing state, and multimodal evidence gives readers a more demanding lens for judging agent capability.
What readers may want to know
Where it fits
This project fits in the benchmark and dataset layer rather than the model or app layer. It is more relevant to readers evaluating coworker-style agents and agent robustness than to readers looking for a standalone assistant product.
Reporting note
What appears notable
Based on the official materials, the main point of interest is the benchmark's combination of 100 tasks, 13 professional domains, multimodal raw evidence, cross-environment tool coordination, and deterministic rule-based scoring.
Before using
What readers may want to review
Which task domains and external environments are included in the current benchmark release.
What local setup, credentials, and Docker requirements are needed to run the full evaluation stack.
How the reported scores, runs, and token counts were defined before comparing models too directly.
Best fit
Who may find it relevant
Readers following agent benchmarks and coworker-agent evaluation work.
Builders who want a harder benchmark for multi-step, multimodal, tool-using agents.
Less relevant for readers focused only on consumer chat products or single-model demos.
Editorial note
Why it is included here
Lifehubber includes ClawMark because it appears to represent a more demanding benchmark style for agent evaluation, especially where longer timelines, multimodal evidence, and evolving work environments matter.
Source links
Original materials
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