@aalvsz/dsh-hermes-bridge
DeepSeek Harness 的原生自适应智能层：记忆、技能、/learn、后台审查和策展器——无需 Hermes 或 Python 依赖。
插件会安装到这里；不确定时保持 web。
npx -y @deepseek-ai/dsh plugin --profile web add github:aalvsz/dsh-hermes-bridge#ccb73e5198bcd059b820eba36adfa183e8ca2fdfHermes Bridge 以 @aalvsz/dsh-hermes-bridge 发布，当前版本为 0.2.1。Plugin Hub 会校验它的 manifest，并保存精确安装来源，便于复现安装结果。
点击下方 Badge 复制 Markdown，粘贴到 README 即可。
验证 package.json 声明的 GitHub 仓库，即可管理这个公开页面。认领后，Hub 会优先安排当前版本的安全扫描，并在通过后公开展示结果。
认领这个 Plugin →Native adaptive intelligence for DeepSeek Harness — no Hermes or Python dependency.
dsh-hermes-bridge reimplements Hermes's adaptive intelligence layer as a
standalone DSH plugin in JavaScript. It provides what Hermes provides on top
of the base agent loop — persistent memory, reusable skills, skill authoring,
background review, and curator — without requiring a separate Hermes installation.
| Capability | DSH surface |
|---|---|
| Persistent memory (MEMORY.md / USER.md) | hermes_memory + frozen system-prompt snapshot |
| Reusable skills (SKILL.md catalog) | hermes_skills_list, hermes_skill_view, hermes_skill_manage |
| Skill authoring | /hermes-learn |
| Background memory/skill review | optional DSH subagent fork |
| Curator | optional due-checked integration |
| RL trajectory capture | hermes_trajectory_save + automatic turn/end capture |
| Trajectory compression | hermes_trajectory_compress (protected regions + LLM summarization) |
| Capability diagnostics | hermes_status |
DSH already provides the agent loop, tool calling, providers, subagents, sessions, approvals, file/bash/web tools, and model routing. This plugin adds the adaptive layer that DSH lacks natively — including the RL trajectory collection and compression pipeline for generating fine-tuning data.
v0.1.0 was a bridge that connected to a real Hermes Python installation.
v0.2.0 is a native reimplementation — no Hermes, no Python, no subprocess.
Memory and skills are pure JavaScript with standard fs operations.
dsh plugin --profile web add github:aalvsz/dsh-hermes-bridge
Override the package row in your profile's cordis.patch.yml:
- id: dsh/hermes-bridge
config:
enabled: true
namespace: hermes
backgroundReview: false
curator: false
memoryCharLimit: 2200
userCharLimit: 1375
memoryNudgeInterval: 10
skillNudgeInterval: 10
saveTrajectories: false
model: null
trajectoryTargetMaxTokens: 15250
trajectorySummaryTargetTokens: 750
backgroundReview, curator, and saveTrajectories are off until explicitly enabled.0600; directories use 0700.hermes_* to avoid collisions with native DSH tools.When saveTrajectories: true, every completed conversation is converted to
ShareGPT trajectory format ({from, value} with <execute>, <result>, and
<think> XML tags) and appended to trajectory_samples.jsonl.
Use hermes_trajectory_compress to compress trajectories within a token budget:
This mirrors Hermes's trajectory_compressor.py for generating SFT/DPO-ready data.
npm install
npm test
npm run verify
MIT.