According to AlphaMoat, as of 2026-06, 龙虾量化研究公司 has 1,153 downloads and 1 stars — ranked #9,432 of 63,926 Claude skills overall, and #1,780 of 10,757 in AI Agent.
龙虾量化研究公司 multi-agent 架构。一键召唤完整的量化投研团队，包含直属、研究、执行、内容四大部门共17名专家。触发词：召唤团队、公司架构、龙虾公司、量化团队、研究团队。
| Month | Downloads | MoM | Stars | Installs |
|---|---|---|---|---|
| 2026-03 | 576 | — | 0 | 268 |
| 2026-05 | 1,060 | +84.0% | 0 | 355 |
| 2026-06 | 1,153 | +8.8% | 1 | 355 |
A skill where the agent logs it's own findings for self-improvement
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express...
A fast Rust-based headless browser automation CLI with Node.js fallback that enables AI agents to navigate, click, type, and snapshot pages via structured co...
Self-reflection + Self-criticism + Self-learning + Self-organizing memory. Agent evaluates its own work, catches mistakes, and improves permanently. Use when...
Security-first skill vetting for AI agents. Use before installing any skill from ClawdHub, GitHub, or other sources. Checks for red flags, permission scope, and suspicious patterns.
Typed knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linkin...
Transform AI agents from task-followers into proactive partners that anticipate needs and continuously improve. Now with WAL Protocol, Working Buffer, Autonomous Crons, and battle-tested patterns. Part of the Hal Stack 🦞
Headless browser automation CLI optimized for AI agents with accessibility tree snapshots and ref-based element selection
Data month: 2026-06 · Downloads, stars and installs are aggregated monthly from public skill registries (ClawHub, SkillHub). See methodology.