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 RipeMangoBox · Codex Skill · ★ 61
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is BITE safe to install? View the security audit →
BITE Bibliographic Intelligence for Thought Emergence 让每一个 idea 都有 出处，让每一个判断都有 锚点。 中文 | English 本项目前身为 ResearchFlow，现已更名为 BITE（Bibliographic Intelligence for Thought Emergence）。 🔥 BITE 社区交流 | [💬 微信交流 / BITE微信交流群](./WECHATCN
| Stars | 61 |
| Forks | 14 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 62.6494135492042/100 |
| Open Issues | 1 |
| Last Updated | 2026-08-13 |
| Created | 2026-04-08 |
| Platforms | claude-code, cli, codex, python |
| Est. Tokens | ~15k |
These tools work well together with BITE for enhanced workflows:
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BITE is Semi-automated research assistant and local knowledge base for paper analysis, ideation, coding, experiments, writing, and publication workflows.. It is categorized as a Codex Skill with 61 GitHub stars.
BITE is primarily written in Python. It covers topics such as academic-research, claude-code, codex-cli.
You can find installation instructions and usage details in the BITE GitHub repository at github.com/RipeMangoBox/BITE. The project has 61 stars and 14 forks, indicating an active community.
BITE is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to BITE on Agent Skills Hub include ai-research-skills, De-Anthropocentric-Research-Engine, claude-research. 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: