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 Tiger3807861189 · Codex Skill · ★ 3.0k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is J-Space-Cognition-Suite-V3.7 safe to install? View the security audit →
J-Space Cognition Suite V3.7 简体中文 J-Space Cognition Suite is a model-agnostic inference-time control system for deep reasoning, long-horizon work, tool use, verification, and recovery. It is packaged as a Skill for cross-platform use, selective loading, and low-friction integration. The suite organizes an agent's accessible working representations into a deliberately managed workspace. It operates through a single entry, nine selectively loaded modules, four supporting references, and an optional standard-library controller for durable task state. J-Space operates at inference time. Model weights and training remain unchanged. Quick start Option A — manual installation Download or clone this repository. Locate the user-level Skills directory used by your AI host. Copy the complete directory into it so that the installed entry is . Run the integrity check with an available Python 3 interpreter: Replace with the Python 3 command available on the host, commonly , , or . Reload the host if it discovers Skills at startup. The directory must remain intact because routes to relative paths under , re
| Stars | 2,998 |
| Forks | 216 |
| Language | Python |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 65.2411119292021/100 |
| Last Updated | 2026-09-02 |
| Created | 2026-07-22 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~16k |
These tools work well together with J-Space-Cognition-Suite-V3.7 for enhanced workflows:
Looking for a J-Space-Cognition-Suite-V3.7 alternative? If you're comparing J-Space-Cognition-Suite-V3.7 with other codex skill tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Open-source desktop AI agent workspace with one-click Claude Code, Codex, OpenClaw, Hermes Agent setup and cus
Enterprise-grade, local-first Agent Workbench for people and agent teams. A unified multi-engine workspace for
Agent OS: the agent gets smarter on its own. We just hold the line: Interview-gated, staged evaluation, budget
A persistent, unified memory layer for all your AI agents (e.g. Claude Code, Codex, DSH), backed by Markdown a
Local-first desktop widget for tracking token usage, costs, and limits across 37+ AI coding tools—including Cl
收录最全、更新最快的技能Skills商店：精选原创技能包（涵盖文档处理、内容创作、编程开发、机器学习、自动化工作流），全部打包好可直接安装使用！同时自动抓取GitHub上万个Skills项目，按分类、更新时间、Star数
Explore other popular codex skill tools:
J-Space-Cognition-Suite-V3.7 is J-Space Cognition Suite V3.7 - AI cognitive-enhancement Skills based on Anthropic's J-space global workspace research. | 哔哩哔哩：Tiger380 (UID 3494375382321675) — https://space.bilibili.com/3494375382321. It is categorized as a Codex Skill with 3.0k GitHub stars.
J-Space-Cognition-Suite-V3.7 is primarily written in Python. It covers topics such as agent-skills, ai, ai-agent.
You can find installation instructions and usage details in the J-Space-Cognition-Suite-V3.7 GitHub repository at github.com/Tiger3807861189/J-Space-Cognition-Suite-V3.7. The project has 3.0k stars and 216 forks, indicating an active community.
J-Space-Cognition-Suite-V3.7 is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to J-Space-Cognition-Suite-V3.7 on Agent Skills Hub include wesight, iPolloWork, ouroboros. 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: