Tiger3807861189/GLM-5.3-Flash-J-Space-Capability-Realization-Report

Name: Tiger3807861189/GLM-5.3-Flash-J-Space-Capability-Realization-Report
Author: Tiger3807861189

Plugin插件 ⭐ 1,023 NOASSERTION Prompts & Skills提示词与技能

J-Space Cognition Suite is a model-agnostic inference-time control system for deep reasoning, long-horizon work, tool use, verification, and recovery.

catalog descriptioncatalog 简介 / catalog description：GLM-5.3-Flash × J-Space capability realization — benchmark presentation of the J-Space Cognition Suite

Project Overview项目介绍

Edited by Zhiwei Agent (zhiwei)由知微Agent(zhiwei)编辑

This repository hosts J-Space Cognition Suite V3.7, a model-agnostic inference-time control system designed to boost AI agents’ capabilities for deep reasoning, long-horizon tasks, tool usage, and error recovery. It follows the cross-platform agent skill convention, so it can be installed on any AI agent platform that supports skill loading, including DSH, Claude Code, and other common agent environments. Manual installation requires cloning the repo, copying the complete j-space directory to your host’s user-level skills folder, then running the included Python integrity check script to confirm all files are intact before reloading the agent host.

After installation, users can invoke J-Space through their host’s skill picker, a dedicated command like /j-space, or a direct natural language request. The suite automatically selects the appropriate operating mode based on task complexity: fast mode for simple one-step tasks that loads no extra modules, full mode for bounded multi-step tasks that loads only relevant modules, and loop mode for multi-stage long tasks that enables full persistent state control. It is intended for both end users who handle complex long-horizon AI work and AI developers who want to test and integrate advanced reasoning control mechanisms into their workflows.

J-Space requires a Python 3 interpreter available on the host system to run the integrity check and the optional persistent state controller for long loop tasks. All modules are loaded on demand, so low-complexity tasks do not face extra context bloat or performance overhead from unused functionality. The entire suite is released under the permissive Apache 2.0 license, which allows use, modification, redistribution, and even commercial integration, as long as the original license and third-party attribution notices are retained in any redistributed copies. Maintainers can run regression tests to verify modifications before releasing updated versions.

本仓库是J-Space认知套件V3.7，一个与模型无关的推理阶段深度推理控制框架，用于增强AI代理的长周期任务处理、工具调用、结果验证和错误恢复能力。它遵循通用代理技能规范打包，可集成到所有支持技能加载的AI代理平台，包括DeepSeek Harness、Claude Code等。安装分为手动复制到技能目录和让AI代理自动安装两种方式，安装完成后需要运行Python脚本校验套件完整性。

安装完成后，用户可通过AI代理平台的技能选择器或调用指令启动该套件，套件会根据任务复杂度自动选择运行模式：简单单步任务选择快速模式，仅加载核心入口；中等复杂度多步任务选择完整模式，加载对应模块；多阶段长任务选择循环模式，加载完整持久化控制机制。它适合需要处理复杂长周期任务的AI开发者和普通用户使用。

该套件要求运行环境配备Python 3解释器，用于执行完整性校验和可选的长任务状态持久化控制。所有功能模块采用选择性加载机制，不会给低复杂度任务增加额外的上下文负担。本套件采用Apache 2.0许可证开源，允许修改和再分发，二次分发时需要保留原许可证文件和第三方声明文档。

View on GitHub在 GitHub 查看

Send to your DSH发给你的 DSH 安装

请帮我安装这个 DSH 插件。安装前先完成【兼容性检查 + 安全性检查】，检查通过再动手。

插件：GLM-5.3-Flash-J-Space-Capability-Realization-Report（Tiger3807861189/GLM-5.3-Flash-J-Space-Capability-Realization-Report）
仓库：https://github.com/Tiger3807861189/GLM-5.3-Flash-J-Space-Capability-Realization-Report
本站详情页：https://www.yhbd.top/plugins/tiger3807861189-glm-5-3-flash-j-space-capability-realization-report/
本站登记：类型 plugin · 归类 多平台兼容工具（非 DSH 原生） · 许可证 NOASSERTION · ⭐ 1023 · 最近提交 2026-09-05 · 未检测到 DSH 插件清单

按下面顺序执行，每步先把结论告诉我，再进入下一步：

【1 兼容性检查】
① 我这边：DSH 版本、Node 版本、操作系统、当前 profile（web / desktop）。
② 读它的 README、package.json、插件 manifest，列出它要求的 DSH 版本 / Node 版本 / 操作系统 / 外部依赖 / 需要另外先装的运行时。
③ 逐条比对，结论只写「满足 / 不满足 / 未知」三种；不满足的给出可行替代方案。
④ 检查是否和我已装的插件冲突：命令名重复、skill / tool 重名、端口占用、重复注册的 MCP server。

【2 安全性检查】
① 仓库可信度：和上面「本站登记」是否一致；star / fork 数、创建时间、最近提交，是否归档或长期停更。
② 安装脚本：逐行看 package.json 的 preinstall / install / postinstall，以及 install.sh、setup.ps1 之类脚本。出现 curl|bash、下载后直接执行、混淆代码、访问与插件功能无关的域名，立刻停下来告诉我，不要继续装。
③ 依赖：列出新增依赖，标出无人维护、或与知名包拼写近似的可疑包（typosquatting）。
④ 权限与副作用：它会读写哪些目录、访问哪些域名、需要哪些 DSH 权限（filesystem / network / shell / clipboard 等），以及怎么卸载和回滚。
⑤ 如果它要求 sudo / 管理员权限，或权限明显超出功能所需，先停下来问我。

【3 安装】
上面两步没有「不满足」和「高危项」时才执行；用官方推荐方式安装，不要自行提权。

【4 汇报】
用表格输出：检查项 / 结论 / 依据 / 是否需要我决策。拿不准的一律写「未知」并说明要我怎么确认——不要猜，也不要替我决定。

Send this message to DSH in your current session: it verifies compatibility and security first (answering met / not met / unknown item by item) and only installs once everything checks out — it will stop and ask you if it finds a high-risk item. The box scrolls; the copy is the full prompt. CLI install commands may not be accurate across systems, so DSH is the safer route.把上面这条消息直接发给当前会话里的 DSH：它会先核对兼容性与安全性（逐条给「满足 / 不满足 / 未知」），确认没问题再安装，有高危项会停下来问你。框内可滚动，复制到的是完整提示词；安装命令不一定准确，发给 DSH 更稳。

Pre-install check安装前体检Compatibility · Security兼容性 · 安全性 1 warning1 项注意

No DSH plugin manifest detected - it may only carry the dsh-plugin topic, so the install method must be confirmed on the spot未检测到 DSH 插件清单：可能只是打了 dsh-plugin 话题，安装方式要现场确认
Not DSH-native: a multi-platform tool that may require Node / Electron or another runtime first非 DSH 原生，是多平台兼容工具：可能要先装 Node / Electron 等运行时

DSH walks through these 9 checksDSH 会逐条核对这 9 项

Compatibility兼容性

DSH, Node, OS and profile requirementsDSH 版本 / Node 版本 / 操作系统 / profile 是否满足要求
External dependencies and runtimes (Electron / Python / Docker, ...)外部依赖与运行时（Electron / Python / Docker 等）是否齐备
Conflicts with installed plugins: command names, skill / tool names, ports, duplicate MCP registration与已装插件是否冲突：命令名、skill / tool 重名、端口占用、重复 MCP 注册

Security安全性

Repo matches the facts registered here; archived or abandoned?仓库是否与页面登记一致，是否归档或长期停更
Safety of preinstall / install / postinstall and install.sh / setup.ps1preinstall / install / postinstall 与 install.sh、setup.ps1 是否安全
curl|bash, download-then-execute, obfuscation, unrelated domains → stop immediatelycurl|bash、下载即执行、混淆代码、无关域名 → 立刻停止
Typosquatting or unmaintained packages among the new dependencies新增依赖里有没有 typosquatting 或无人维护的包
Requested permissions vs. what the feature actually needs申请了哪些权限、是否超出功能所需（filesystem / network / shell / clipboard）
Any sudo / admin requirement, plus uninstall and rollback是否要求 sudo / 管理员权限，以及卸载与回滚方式

Anything uncertain must be marked unknown with a note on how to confirm it. This site's signal screen is a static snapshot, not a security audit.拿不准的必须标「未知」并说明要我怎么确认。本站的信号筛查是静态快照，不能替代安全审计。

Or use CLI install (for developers)或使用命令行安装（适合开发者）

CLI Install命令行安装

dsh plugin --profile web add github:Tiger3807861189/GLM-5.3-Flash-J-Space-Capability-Realization-Report

把 Tiger3807861189/GLM-5.3-Flash-J-Space-Capability-Realization-Report 加入你的 DSH 配置（web profile）即可启用。

READMEREADME

12.2 KB · 缓存于 2026-09-17 19:38:14 · 12.2 KB

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 j-space/ directory into it so that the installed entry is <skills-directory>/j-space/SKILL.md.
Run the integrity check with an available Python 3 interpreter:
```
<python-command> <skills-directory>/j-space/scripts/verify_suite.py
```
Replace <python-command> with the Python 3 command available on the host, commonly python, python3, or py -3.
Reload the host if it discovers Skills at startup.

The directory must remain intact because SKILL.md routes to relative paths under modules/, references/, and scripts/.
The repository-level LICENSE and THIRD_PARTY_NOTICES.md remain part of the distribution.
Include copies of both when redistributing j-space/ as a standalone package.

Option B — ask an AI agent to install it

Copy the following prompt into an agent that can access files and this repository:

Install J-Space Cognition Suite from
https://github.com/Tiger3807861189/J-Space-Cognition-Suite-V3.7 into this environment's user-level Skills directory.

First inspect the host configuration or documentation to locate the correct Skills directory. Install the complete j-space/ directory as j-space/, preserving SKILL.md, modules/, references/, and scripts/. If a j-space target already exists, compare it and ask before replacing anything. Run scripts/verify_suite.py with an available Python 3 interpreter after installation.

When finished, report the installed path and verification result, then tell me how this host invokes the Skill. Briefly explain fast, full, and loop, and explain that the optional controller records long-task state rather than choosing solutions. If this host has no native Skill loader, explain the selective system/developer-instruction integration instead of reporting an installation.

Showing the opening section of the README — the full document lives in the repository以上为 README 开头摘要，完整文档在仓库内 · View the full README on GitHub →在 GitHub 查看完整 README →

渲染基于仓库 Tiger3807861189/GLM-5.3-Flash-J-Space-Capability-Realization-Report 的 README.md。在 GitHub 查看原文 →