@aalvsz/dsh-hermes-bridge

Readme

# dsh-hermes-bridge

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**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.

## What appears in DSH

| 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.

## Differences from v0.1.0 (bridge)

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.

## Install

```sh
dsh plugin --profile web add github:aalvsz/dsh-hermes-bridge
```

## Configure

Override the package row in your profile's `cordis.patch.yml`:

```yaml
- 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
```

### Defaults

- `backgroundReview`, `curator`, and `saveTrajectories` are **off** until explicitly enabled.
- Memory and skill files use mode `0600`; directories use `0700`.
- All tools are namespaced `hermes_*` to avoid collisions with native DSH tools.

## RL trajectory pipeline

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:

1. Protected head turns (system, human, first gpt+tool) are preserved
2. Protected tail turns (last N turns) are preserved
3. Middle turns are accumulated until enough savings are achieved
4. Compressed turns are replaced with a single summary message
5. Boundary snapping prevents splitting gpt/tool pairs

This mirrors Hermes's `trajectory_compressor.py` for generating SFT/DPO-ready data.

## Development

```sh
npm install
npm test
npm run verify
```

## License

MIT.