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 | hypothesis-gen Use when the user wants to generate and literature-vet a pool of novel, testable research hypotheses for a question or domain. A multi-agent loop: a Generator proposes candidate hypotheses, a LiteratureScout grounds each in real retrieved literature (already known? closest prior work? what gap does it fill?), and a Judge scores them against a fixed rubric and keeps the strong, non-duplicate ones; rounds repeat — mutating toward the open gaps — until fresh rounds stop adding keepers. Not for sharpening or decomposing a research question (no grounding/scoring there), and not for grading an existing written proposal against the literature. | 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 | 63/100 Promising | 100/100 Excellent |
| Decision verdict | 62/100 Prototype first Prototype with this skill first; keep a fallback candidate ready. | 100/100 Production-ready Use this as a leading candidate, then validate the README and install path in your own agent stack. |
| Adoption | 163 stars Verified outcomes are shown on each skill page | 1.9K stars Verified outcomes are shown on each skill page |
| Freshness | Jun 30, 2026 | Sep 1, 2026 |
| Use-case fit |
| Workflow fit |
| Platform hints | Claude Code | Claude Code |
| Warnings | The skill depends on a sibling 'literature-search' skill; if missing, it degrades to web search, but the setup instructions could be clearer about installation steps. · No OpenAgentSkill engagement data yet | No OpenAgentSkill engagement data yet |
| Best for | Research agents workflows · Claude Code teams · builders willing to evaluate younger projects | 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 gaasher/Agent-Loop-Skills --skill hypothesis-gen | $ npx skills add yanliudesign/mono-color-skill --skill mono-color |