J-Space Harness

  

简体中文

@spy2006/dsh-jspace-harness packages the J-Space Cognition Suite V3.6 as a DeepSeek Harness agent preset. It adapts the upstream protocol to the Harness runtime the right way: Harness-native scoped prompt sections, per-agent state isolation, request-cache-stable turn checkpoints, and durable V3.6/Harness verification — instead of guessing provider payloads or mutating the conversation transcript the way the pi adapter does.

What this preset gives you

Install

Via DSH Plugin Market (dshmarket v1) — recommended

The package is published on npm: @spy2006/dsh-jspace-harness@0.1.1.

1. Make the catalog entry visible: - DSH 1024Store (auto-collected): this repository carries the dsh-plugin topic and its default branch already satisfies the 1024Store static check (package.json with dsh.bundle.patch + cordis.patch.yml in the same tree), so it is picked up by the pipeline's incremental scan. Search jspace in the 1024 Store tab of Plugin Market. - Custom standard source: deploy market/worker.mjs (npx wrangler deploy), then register https://<worker>/catalog-source.json in Plugin Market → Sources. 2. Open the entry in DSH Plugin Market, Preview (the host re-validates the exact npm identity, repository backlink, lifecycle scripts, engine, and DSH bundle evidence), confirm, and restart DSH Desktop. 3. On first launch index.js copies preset/ into $DSH_HOME/.agent-presets/jspace-harness. An existing user-owned preset is deliberately not overwritten, so local customizations survive. 4. Start a new session and pick the J-Space Harness preset.

Manual install

git clone git@github.com:2006spy/jspace-harness.git
# copy the preset directory into the DSH user preset root:
robocopy preset %USERPROFILE%\.dsh\.agent-presets\jspace-harness /E   # Windows
cp -R preset ~/.dsh/.agent-presets/jspace-harness                     # macOS/Linux

Restart DSH and select J-Space Harness in the new-session preset picker.

Usage

In any session on the preset, /jspace controls the runtime:

State is stored per session in $DSH_HOME/j-space/<session-id>.json (override with JSPACE_STATE_DIR). The bundled skill (skills/j-space) provides the optional jspace.py controller (note / seam / resume / ship) and its regression tests.

Benchmarks (measured on this machine)

Method: Python 3.12.10, cold subprocess timings, fresh temporary workspace per controller lifecycle, median of N rounds. Raw data lives in [benchmarks/](benchmarks/).

Controller lifecycle (upstream V3.6 vs this preset)

Full ledger lifecycle (note goal/core/open/close + seam + ship with a Chinese covered verification claim), 20 rounds:

This preset is ~2.4% faster on the median lifecycle despite carrying the V3.6 controller logic, strict verifier, and Chinese claim/coverage vocabulary.

Adapter cold parse (pi adapter vs this preset)

node --check of each adapter entry, 20 rounds:

Same-model protocol smoke comparison (10 tasks, blind-scored)

Same model, no tools, one fixed rubric (0–2 correctness + 0–1 verification per task, max 30). Upstream generic protocol vs this preset's forced gate/ledger:

Verdict: tie — Harness forcing does not regress correctness or verification coverage on short deterministic tasks. The task set is intentionally too short to claim a capability separation.

Verification suite

• Upstream V3.6 regression tests: 18/18 pass
• verify_suite.py (upstream anchors + strict frontmatter/routing): clean
• verify_harness.py (Harness adapter contract): clean

Reproduce

python benchmarks/run_jspace_bench.py        # engineering timings -> engineering-results.json
python preset/skills/j-space/scripts/verify_suite.py
python preset/skills/j-space/scripts/verify_harness.py

Scope of these numbers

Engineering measurements describe local controller/adapter behavior, not an LLM capability score. The same-model comparison is a 10-task protocol smoke test. Provider-level prompt-cache hit rate and long-horizon durability need a fixed provider/model, temperature/seed, task corpus, and token telemetry.

Discovery in DSH Plugin Market

• DSH 1024Store (auto-collected): the pipeline scans GitHub repositories

carrying the dsh-plugin topic and validates package.json + dsh.bundle.patch + patch file on the default branch. This repository satisfies all checks, and the published npm package with a matching repository backlink makes the entry installable through the managed path. See https://github.com/imsai-sh/awesome-deepseek-harness-plugins.

• Custom standard source: deploy market/worker.mjs and register

https://<worker>/catalog-source.json. See market/README.md.

• dshfind is a third-party read-only index; listing there is controlled by

dshfind, not by this repository.

Development checks

python preset/skills/j-space/scripts/verify_suite.py
python preset/skills/j-space/scripts/verify_harness.py
node --check index.js
node --check preset/j-space.mjs
npm pack --dry-run

Project layout

jspace-harness/
├── index.js                # plugin entry: copies preset/ into .agent-presets on first launch
├── cordis.patch.yml        # DSH bundle patch (market installer mounts the plugin row)
├── package.json            # npm package: dsh.bundle.patch, no lifecycle scripts
├── preset/                 # the agent preset (agent.cordis.yml + j-space.mjs + skills/j-space)
│   └── skills/j-space/     # SKILL.md, 9 modules, references, jspace.py, dual verifiers
├── market/                 # deployable Cloudflare Worker standard-source catalog
├── benchmarks/             # reproducible engineering + protocol comparison data
└── .github/workflows/      # CI: verifiers + syntax + npm pack

Attribution

The bundled J-Space skill derives from J-Space Cognition Suite V3.6. See THIRD_PARTY_NOTICES.md and LICENSE.