Apache-2.0 inference-time cognition Skill for DeepSeek Harness — install j-space/ into the host Skills directory, verify with verify_suite.py, and get selective deep-reasoning, verification and recovery control.
DSH integration
Compatible
Author-claimed
Safety audit
Unaudited
Last verified
2026-08-21
License
Apache-2.0
01What can it help you accomplish?
Add deep reasoning, verification and recovery control to DeepSeek Harness tasks
A managed inference-time workspace (one entry, nine selectively loaded modules, three references) that routes fast / full / loop passes over deep reasoning, long-horizon work, tool use, verification, and recovery
Developers running DeepSeek Harness or any Skill-capable coding agent who want stronger reasoning and self-verification on complex tasks
Keep durable state across multi-stage, long-horizon agent tasks
A `.jspace/` ledger in the task workspace with goal / next-action notes, hub entries, checkpoints, questions, seams and resume, managed by the optional standard-library controller
Teams running multi-stage workflows (multiple files, turns, tools) where context must survive long gaps and interruptions
Verify that a Skill package installs intact before using it
An integrity check result from `verify_suite.py` run with any Python 3 interpreter, confirming SKILL.md and its relative module / reference / script paths are intact
Anyone installing the suite into a host's Skills directory who wants a quick post-install sanity check
02How to install into DeepSeek Harness
Prerequisites
- An AI host with a user-level Skills directory (or a chat / API environment where SKILL.md can be provided as a system- or developer-level instruction)
- A Python 3 interpreter on the host (commonly `python`, `python3`, or `py -3`) for the integrity check and the optional controller
Installation steps
- 01
Download or clone this repository
- 02
Locate the user-level Skills directory used by your AI host
- 03
Copy the complete `j-space/` directory into it so that the installed entry is `<skills-directory>/j-space/SKILL.md`
- 04
Run the integrity check: `<python-command> <skills-directory>/j-space/scripts/verify_suite.py`
- 05
Reload the host if it discovers Skills at startup
Verify the integration
- Run `<python-command> <skills-directory>/j-space/scripts/verify_suite.py` — the README's integrity check for the installed package
03DSH integration and capability boundaries
Model-agnostic Skill (SKILL.md) installed into the host's user-level Skills directory; DeepSeek Harness loads it as a Skill and its evaluations were configured from the official DeepSeek Harness minimal-mode setup
Selective workspace loading with fast / full / loop passes
any task request via the host's Skill mechanism (Skill picker, `/j-space`, `$j-space`, or a direct request)→the entry gate selects the lightest suitable pass: fast (nothing extra), full (one or two modules, ship before delivery), or loop (ledger, seams, checkpoints, register audit, recovery)
Optional loop controller for durable task state
`jspace.py` CLI commands (note, seam, ship, resume) run by resolved Skill path with the task workspace as current directory→externalized loop state under `.jspace/`: goals, next actions, hub entries, checkpoints, questions, and resume after long gaps
writes working state under the task's `.jspace/` directory (Python standard library only)Verification and recovery mechanisms
in-flight reasoning, stalled derivations, or outgoing deliverables→bridge-before-conclusion intermediates, bounded empirical tests with a named verifier and coverage, metacognitive routing of confidence / inconsistency / failure signals, and `ship` register-leakage audits
Cross-model, host-agnostic integration
`j-space/SKILL.md` — installed directly in hosts with a native Skill loader, or provided as a system- / developer-level instruction in chat / API environments→the same protocol (workspace loading, selective routing, state externalization, verification, recovery) across DeepSeek, Qwen, GLM, GPT, and Claude model families
04Who is it for? When not to use it?
Good for
- Developers running DeepSeek Harness or any Skill-capable coding agent who want stronger reasoning and self-verification on complex tasks
- Teams running multi-stage workflows (multiple files, turns, tools) where context must survive long gaps and interruptions
- Anyone installing the suite into a host's Skills directory who wants a quick post-install sanity check
Not for
- The installed `j-space/` directory must remain intact: SKILL.md routes to relative paths under `modules/`, `references/`, and `scripts/`, so partial copies or moved files break the Skill.
- Effect size varies with base capability, context policy, tool harness, sampling configuration, and benchmark implementation; reported benchmarks were collected in the project's own evaluation environment, so scores are not directly comparable across harnesses.
05Compatibility, maintenance and safety notes
- The installed `j-space/` directory must remain intact: SKILL.md routes to relative paths under `modules/`, `references/`, and `scripts/`, so partial copies or moved files break the Skill.
- Integrity checks and the optional controller require a Python 3 interpreter on the host; hosts without one can install the Skill but cannot run `verify_suite.py` or `jspace.py`.
- Effect size varies with base capability, context policy, tool harness, sampling configuration, and benchmark implementation; reported benchmarks were collected in the project's own evaluation environment, so scores are not directly comparable across harnesses.
Apache-2.0 · actively maintained (latest release v3.6.1, 2026-08-19)
06Frequently asked questions
How does J-Space integrate with DeepSeek Harness?
It is packaged as a Skill: copy the complete `j-space/` directory into the user-level Skills directory used by your AI host so the entry is `<skills-directory>/j-space/SKILL.md`, then invoke it through the host's Skill mechanism (Skill picker, `/j-space`, `$j-space`, or a direct request). Its DeepSeek evaluations were configured with reference to the official DeepSeek Harness minimal-mode setup.
What prerequisites do I need?
A host with a user-level Skills directory and a Python 3 interpreter (commonly `python`, `python3`, or `py -3`) — Python is needed for the integrity check and the optional loop controller, which uses only the standard library.
How do I verify the installation?
Run `<python-command> <skills-directory>/j-space/scripts/verify_suite.py` after copying the directory, and 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/`.
Does the controller write anything to disk?
The optional `jspace.py` controller externalizes loop state into `.jspace/` in the current task workspace. It records and reports state only — solution choice remains with the model — and uses the Python standard library.
What if my host has no native Skill loader?
Provide `j-space/SKILL.md` as a system- or developer-level instruction and expose `modules/` and `references/` through file or retrieval tools, retrieving selected files on demand — selective loading is part of the operating design.
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08Data and sources
It is packaged as a Skill for cross-platform use, selective loading, and low-friction integration.
The J-Space evaluations on DeepSeek were configured with reference to the official DeepSeek Harness minimal-mode setup,…
This page is generated from the project’s public documentation, repository metadata and a structured parse of DSH Plugins; last verified on 2026-08-21. Found an error? Submit a correction.
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