scd13150/dsh-cognition

# DSNLE — A project memory for your DSH agent

DSNLE gives your DeepSeek Harness agent a memory of your project.

New tasks start with what you already did: edits from similar past tasks surface as precedents, out-of-scope edits get blocked, and knowledge keeps accumulating across sessions — enforced by DSH’s native mechanisms (per-tool gates, kernel-level version guards, the skills knowledge layer, session event sourcing), not by prompt tricks.

Honest status: a research prototype with a working implementation — 47 real-repo fixes verified by upstream test suites, a gold-free 20-task continuous run (20/20), 245 deterministic regression assertions — and openly documented boundaries (docs/research/).

中文

Quick start(3 steps)

Requires: Node.js ≥ 22 + a running DeepSeek Harness.

git clone https://github.com/scd13150/dsh-cognition.git && cd dsnle
node install-dsnle.mjs --set-default    # creates the dsnle preset and makes it DSH's default (backs up your config)

Then restart DSH (or open a new session) — the new session should show:

• nle_suggest / nle_focus / nle_mutate / nle_select / nle_guard … in your tools
• nle-learned-* and nle-reflections in your skill catalog

Verify: run nle_check (no args) in the new session — ok: true means installed.

Other install modes / uninstall / troubleshooting

• Keep your global default untouched: omit --set-default, then set the default preset in DSH settings (agent-presets → default), or pick dsnle per session
• Inject into an existing preset: node install-dsnle.mjs --into <preset-id> (backs up agent.cordis.yml)
• Dry-run any command with --dry-run
• Uninstall: delete ~/.dsh/.agent-presets/dsnle/ (+ restore settings.yaml.bak-* if you changed the default); injected mode: restore agent.cordis.yml.bak-*
• No tools in a new session? The session must use the dsnle preset (check settings); running sessions never pick up new presets — always open a fresh session

怎么工作

每个任务走一条可审计的 ritual 链(强制,乱序改码会被 deny):

nle_suggest(定位检索)→ nle_focus(锁 scope)→ [nle_impact(高影响声明)]→ 编辑
→ [nle_reorient(证据推翻 scope)]→ nle_select(对账收尾)→ nle_guard(总闸)

完整工具说明:调用 nle_suggest 等工具的 schema 即自带描述;机制规格见 docs/research/DSH_NLE_SPEC.md。

语义通道(可选)

语义检索(embedding 重排)是可选增强,不启用时自动降级为 BM25 + 符号/先例/共改检索,主链路不受影响。

启用:运行独立 helper 进程(transformers.js 首次运行自动下载模型,~90MB,缓存于 HF 缓存目录):

node nle-semantic/server.mjs --spool <工作区>/.nle-semantic

• 模型:Xenova/all-MiniLM-L6-v2(quantized),384 维,不随本仓库分发,由 transformers.js 自动下载或从 HF 缓存复用
• 离线环境:预先在有网机器跑一次 node nle-semantic/server.mjs --selfcheck 生成缓存再迁移;或干脆不用语义通道
• 自检:node nle-semantic/server.mjs --selfcheck → {"ok":true,"dim":384}

For DSH plugin developers: this repo also ships docs/DSH_DEV_EXPERIENCE.md — battle-tested deep-dive notes (dynamic-plugin sandbox traps, fs five-arg contract, preset generations, sessionQuery deployment differences, tool-pipeline events) distilled from building DSNLE on dsh. For DSH 插件开发者:仓库另附 docs/DSH_DEV_EXPERIENCE.md ——从零到 85KB 持久插件的全部踩坑提炼(动态插件沙箱陷阱、fs 五参契约、preset 代际语义、 sessionQuery 部署差异、工具管线事件),与 NLE 本体无关,可独立阅读。

已知边界(诚实声明)

• “长期”是初步证据:连续 20 卡(单仓库单日)累积曲线有实证;跨仓库、跨月、有害累积无数据——这正是开源想从真实使用中获得的
• 中文任务对英文代码库召回弱(learned/alias 兜底)
• 语义通道依赖独立 helper 进程;FTS 全文检索若部署禁用(openAt: "never"),precedent 走字面扫描降级
• 插件注册为 agent-plane 工具,不提供安全隔离;作用域锁是 workflow 约束而非权限边界

研究附录(docs/research/)

开发

nle-plugin/
  nle-rules-core.mjs      ← 仓库根(规则唯一源,插件经 import 引入)
  dsnle-plugin.mjs        ← [生成] 持久插件(preset 挂载入口,勿手改)
  frag-apply-*.mjs ×4     ← apply 函数体碎片(改插件逻辑改这里)
  build-dsnle.mjs         ← 纯拼接构建器
nle-semantic/server.mjs   ← 语义/git 信号 helper(可选)
install-dsnle.mjs         ← 安装脚本(standalone / --into 两模式)

改代码 → node nle-plugin/build-dsnle.mjs → 重跑安装脚本(覆盖 preset 内副本)→ 新会话生效。 规则层回归:node smoke-test.mjs(无文件依赖纯函数断言)。 实验装置完整回归(fixture-gate 245 断言)依赖实验仓库,不在本仓库分发。

License

MIT