Apache-2.0 授權的推理時認知控制技能套件，將 j-space/ 安裝進 DeepSeek Harness 的 Skills 目錄並以 verify_suite.py 校驗，即可獲得按需載入的深度推理、驗證與復原控制。
DSH 整合
相容
作者聲明
安全稽核
未稽核
最後核實
2026-08-21
授權條款
Apache-2.0
01它能幫你完成什麼？
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
02如何將外掛接入 DeepSeek Harness？
先決條件
- 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
安裝步驟
- 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
驗證整合成功
- Run `<python-command> <skills-directory>/j-space/scripts/verify_suite.py` — the README's integrity check for the installed package
03DSH 整合程度與能力邊界
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
04適合誰？何時不該用？
適合
- 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
不適合
- 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.
05相容性、維護與安全提醒
- 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)
06常見問題
J-Space 如何接入 DeepSeek Harness？
它以外掛 Skill 形式散布：將完整的 `j-space/` 目錄複製到 AI 宿主的使用者級 Skills 目錄，使入口為 `<skills-directory>/j-space/SKILL.md`，再透過宿主的技能機制呼叫（技能選擇器、`/j-space`、`$j-space` 或直接請求）。其 DeepSeek 評測正是參照官方 DeepSeek Harness minimal-mode 設定完成的。
安裝需要什麼前置條件？
需要一個帶有使用者級 Skills 目錄的宿主，以及一個 Python 3 直譯器（常見為 `python`、`python3` 或 `py -3`）。Python 用於完整性檢查與選配的循環控制器，後者僅相依於標準函式庫。
如何驗證安裝成功？
複製目錄後執行 `<python-command> <skills-directory>/j-space/scripts/verify_suite.py`，若宿主在啟動時探索技能則重新載入。目錄必須保持完整，因為 SKILL.md 透過 `modules/`、`references/`、`scripts/` 下的相對路徑路由。
選配控制器會往磁碟寫資料嗎？
選配的 `jspace.py` 控制器會把 loop 狀態外部化到目前任務工作區的 `.jspace/` 目錄。它只記錄並回報狀態，方案選擇仍由模型決定，且只使用 Python 標準函式庫。
如果宿主沒有原生技能載入器怎麼辦？
可以把 `j-space/SKILL.md` 作為系統級或開發者級指令提供，並透過檔案或檢索工具暴露 `modules/` 與 `references/`，按需檢索所選檔案——選擇性載入本身就是該套件的設計原則。
07相關的 DSH 工作流程
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08資料與來源
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,…
此頁面根據專案公開文件、儲存庫中繼資料與 DSH Plugins 的結構化解析所產生；最後核實於 2026-08-21。發現錯誤？提交更正。
最佳 DeepSeek Harness 外掛
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