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What you can do with J Space Cognition Suite

J-Space Cognition Suite is a model-agnostic set of Python-backed skill files that help an AI host keep long engineering, verification, and security tasks tied to saved state and source evidence.

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Tiger3807861189/J-Space-Cognition-Suite on GitHub

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Authorization notice. This tool is dual-use. Only use it on software, sites and systems you own or have explicit permission to test, and follow the relevant licenses and terms of service.

What do you want to do?

5 use cases

  • Repair a repository issue with a verified, source-backed map

    Run a high-level pass that maps the code, makes the fix, and gates delivery on evidence.

    View prompt
    Use the j-space skill at high level to repair the issue described in <issue file> in the repository at <repo path>. Keep a source-backed repo map, run the relevant tests, and finish with evidence against each acceptance requirement. Verify that `check --stage ship` exits with status 0 before you report completion.

  • Get an independent review of a risky integration

    Assign a real child agent to review an integration contract and resolve disagreements with checks.

    View prompt
    Use the j-space skill at xhigh for the integration in <repo path>. Assign an independent contract review to a real child agent, require two review rounds with evidence files, and have root reproduce any material finding before accepting it. Verify that every active agent has run its own read and repo view before the final ship check.

  • Confirm one configuration constraint without expanding scope

    Use the low level for a fast, evidence-based check of a single setting.

    View prompt
    Use the j-space skill at low level to check whether the configuration change in <config file> preserves the timeout unit. State the conclusion and its evidence, and do not expand the task. Verify that the cited line in <config file> supports your answer.

  • Ship a small API example with a lightweight ledger

    Track goal, open questions, and checks for a bounded deliverable using jspace.py.

    View prompt
    Use the j-space skill at medium level to update the API example in <doc file> so it matches the implementation in <source file>. Keep a ledger of the goal, open question, and observed checks, then write answer.md. Verify that the example runs against the implementation and that the ledger records the actual outcome.

  • Track authorized security findings to a verified disposition

    Record candidate, confirmed, rejected, and fixed security items with reproduction evidence.

    View prompt
    Use the j-space skill at high level for the authorized security review of <target repo>. Track each candidate, confirm or reject it with a reproducible check, and record evidence for each disposition. Verify that no candidate remains open before running the ship gate.

How to use J Space Cognition Suite

  1. Check you have what it needs: A host that can load a local SKILL.md and retrieve its supporting files; Python 3.10 or later, using the python, python3, or py -3 command for your system; A copy of the complete j-space/ directory placed in the host's Skills directory.
  2. Open Tiger3807861189/J-Space-Cognition-Suite on GitHub and read the README and license.
  3. Copy the complete j-space/ directory into your host's Skills directory and run /scripts/verify_suite.py with your Python 3.10+ command, replacing the path prefix with the installed skill directory.
  4. Initialize the task with /scripts/control.py init --goal "Acceptance criteria" --next "Inspect entry points" --level high --module modules/repository.md, then run /scripts/control.py read --agent root.
  5. Write repo-map.json from files you actually inspected, then run /scripts/control.py repo sync --map repo-map.json and /scripts/control.py repo view --agent root.
  6. Make the change, run the relevant tests, and record real results in evidence/root.txt and a separate checklist in evidence/completion.txt.

These steps come from our top use case, Repair a repository issue with a verified, source-backed map, which also has a copy-paste agent prompt.

J Space Cognition Suite alternatives · Best AI repos for Software and DevOps

About this repo

Requirements

  • A host that can load a local SKILL.md and retrieve its supporting files
  • Python 3.10 or later, using the python, python3, or py -3 command for your system
  • A copy of the complete j-space/ directory placed in the host's Skills directory
  • Access to the target project as a task directory, and permission for the host to run local commands
  • For xhigh work, a host that can actually launch child agents

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Questions about J Space Cognition Suite

What is J Space Cognition Suite?

J-Space Cognition Suite is a model-agnostic set of Python-backed skill files that help an AI host keep long engineering, verification, and security tasks tied to saved state and source evidence. It is an open-source project on GitHub: https://github.com/Tiger3807861189/J-Space-Cognition-Suite.

Who is J Space Cognition Suite for?

Engineers and teams who run long, multi-file AI-assisted work in a host that can load a local SKILL.md and launch its own tools or agents.

What can I do with J Space Cognition Suite?

5 use cases so far, for example: Repair a repository issue with a verified, source-backed map; Get an independent review of a risky integration; Confirm one configuration constraint without expanding scope. Each has steps and a copy-paste agent prompt.

What do I need to run J Space Cognition Suite?

A host that can load a local SKILL.md and retrieve its supporting files; Python 3.10 or later, using the python, python3, or py -3 command for your system; A copy of the complete j-space/ directory placed in the host's Skills directory; Access to the target project as a task directory, and permission for the host to run local commands.

What are alternatives to J Space Cognition Suite?

Other hot AI repos in Software and DevOps: Omarchy Local AI, Hermes Plan Mode, Agent 2, Agnes Harness, Skills.

Is J Space Cognition Suite safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.