No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by foryourhealth111-pixel · Codex Skill · ★ 85
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is research-innovation-explorer safe to install? View the security audit →
Research Innovation Explorer A host-neutral, search-first skill for literature-grounded idea discovery, theory framing, and polished Markdown reporting. 中文文档 Why This Exists Most research-idea workflows fail in one of three ways: they rely on vague intuition instead of systematic search they generate combinations but cannot explain why the combination matters they stop at analysi
| Stars | 85 |
| Forks | 6 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 67.9787047773463/100 |
| Last Updated | 2026-08-14 |
| Created | 2026-04-20 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~16k |
These tools work well together with research-innovation-explorer for enhanced workflows:
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research-innovation-explorer is The automated approach leverages the cross-combination of high-quality papers from top conferences to uncover feasible research and innovation ideas. Through multi-level verification and convergence s. It is categorized as a Codex Skill with 85 GitHub stars.
research-innovation-explorer is primarily written in Python. It covers topics such as academic, agent-skills, ai-science.
You can find installation instructions and usage details in the research-innovation-explorer GitHub repository at github.com/foryourhealth111-pixel/research-innovation-explorer. The project has 85 stars and 6 forks, indicating an active community.
research-innovation-explorer is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to research-innovation-explorer on Agent Skills Hub include asta-skill, paper-fetch, semanticscholar-skill. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: