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

1. Download or clone this repository.

2. Locate the user-level Skills directory used by your AI host.

3. Copy the complete j-space/ directory into it so that the installed entry is <skills-directory>/j-space/SKILL.md.

4. Run the integrity check with an available Python 3 interpreter:

    <skills-directory>/j-space/scripts/verify_suite.py
   

   Replace `` with the Python 3 command available on the host, commonly python, python3, or py -3.

5. 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.

Use it

Invoke the Skill through the mechanism provided by your host—such as its Skill picker, /j-space, $j-space, or a direct request:

Use j-space for this task. Audit this repository, preserve its architecture,verify every finding, and keep the work consistent across all affected files.

The entry gate selects the lightest suitable pass automatically.

Operating modes

Pass	Suitable work	What loads
fast	One step, or a result checkable in one glance	Nothing extra
full	Several dependent steps and one bounded deliverable	One or two relevant modules; ship before delivery
loop	Multiple stages, files, turns, tools, or persistent state	Ledger, seams, checkpoints, register audit, and recovery

A request for brevity changes the outer response length while verification remains aligned with the task's floor. Short work stays light; long work receives durable state only when it needs it.

Core mechanisms

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