Skill comparison
Use this as a shortlist, then open the skill detail page before adopting.
Decision summary
Strongest overall
mono-color
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Fastest prototype
mono-color
Best first install candidate based on install readiness and adoption.
Freshest repo
mono-color
Most recent maintenance signal among this shortlist.
| Signal | eval-design Use when the user needs to design evaluation datasets, create test cases, stratify samples, generate adversarial examples, extract eval dimensions from traces/specs, or build a labeled evaluation set. Also use when the user mentions test data design, eval coverage, difficulty stratification, synthetic data generation for eval, or "how to create good evaluation data." Outputs datasets in OpenJudge-compatible format. | mono-color Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only. |
|---|---|---|
| Quality | 70/100 Strong | 100/100 Excellent |
| Decision verdict | 81/100 Strong shortlist Shortlist this skill and compare it with close alternatives before production adoption. | 100/100 Production-ready Use this as a leading candidate, then validate the README and install path in your own agent stack. |
| Adoption | 816 stars Verified outcomes are shown on each skill page | 1.9K stars Verified outcomes are shown on each skill page |
| Freshness | Aug 3, 2026 | Sep 1, 2026 |
| Use-case fit |
| Workflow fit |
| Platform hints | Claude Code | Claude Code |
| Warnings | Minor inconsistency: SKILL.md states a 60/30/10 difficulty split, but the bundled coverage_check.py uses EXPECTED_STRATA = {'easy': 0.55, 'boundary': 0.30, 'adversarial': 0.10}. This could confuse users relying on the documented split. · No OpenAgentSkill engagement data yet | No OpenAgentSkill engagement data yet |
| Best for | Research agents workflows · Claude Code teams · teams that value GitHub adoption signals | Research agents workflows · Claude Code teams · teams that value GitHub adoption signals |
| Not ideal for | teams that need a vendor-supported SLA · production agents without a repository review | teams that need a vendor-supported SLA · high-compliance environments without internal security review |
| OpenAgentSkill engagement | 0 views 0 install copies | 0 views 0 install copies |
| Install | $ npx skills add agentscope-ai/OpenJudge --skill eval-design | $ npx skills add yanliudesign/mono-color-skill --skill mono-color |