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

Capability	Harness-native behavior
Cognitive protocol injection	Registers one scoped systemPrompt.section() evaluated per assembly with the calling agent's context (dsh-plan-mode/dsh-persona pattern). No transcript pollution, no provider-payload mutation.
Gate (fast / full / loop)	Classified once per turn, persisted to a per-session ledger file, and surfaced in the prompt section.
Per-agent state isolation	Ledger keyed by session.id under $DSH_HOME/j-space/<session-id>.json with a per-agent WeakMap cache; sessions never share state.
Cache stability	The system-prompt section stays byte-identical across a turn's steps: pass is classified once per turn and checkpoint advances only at turn boundaries. The stable prefix [system + history] can be reused by the request cache; only the new tail is re-encoded.
Prompt construction cost	compact and reminder protocol text is built once at load; full reads SKILL.md lazily and caches it (no re-read per assembly).
State write resilience	Ledger writes are non-fatal, use a unique temporary file (<pid>.<ts>.tmp), and never interrupt an agent turn on disk failure.
Verification	Upstream V3.6 anchors (PREMISE, INVARIANTS), strict frontmatter/routing checks, and Chinese claim/coverage vocabulary — enforced by two durable verifiers.
Runtime control	/jspace command: status, on, off, compact, full, reminder.

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:

Command	Effect
/jspace or /jspace status	Show enabled state and mode
/jspace on / /jspace off	Enable / disable the cognitive layer
/jspace compact	Default: ~1.5k-char protocol block; modules are read on demand
/jspace full	Inject the whole SKILL.md (truncated at 18 000 chars); use sparingly
/jspace reminder	Lightest: one gate line only, best for long sessions

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/.

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:

Condition	Median	Mean	Min	Max
Upstream V3.6 jspace.py	800.299 ms	814.206 ms	774.202 ms	914.101 ms
This preset's jspace.py	781.112 ms	780.362 ms	749.431 ms	836.777 ms

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:

Condition	Median	Mean	Bytes
pi adapter (tonyxu721/pi-j-space)	206.535 ms	200.709 ms	12 175
This preset's preset/j-space.mjs	205.755 ms	212.285 ms	10 852 (-10.9%)

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:

Condition	Correctness	Verification	Total
Upstream generic protocol	20	10	30/30
Harness forced protocol	20	10	30/30

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.