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dsh-harness2

Verified Skills & agents ★ 12 0.1.0

Harness² (recursive agent harnessing) for DeepSeek Harness: Propose–Probe–Compose self-improvement as a DSH plugin.

What it is

Harness² (recursive agent harnessing) for DeepSeek Harness: Propose–Probe–Compose self-improvement as a DSH plugin.

How to install

Run this in the DSH web profile, then refresh the page.

$ dsh plugin --profile web add 'git+https://github.com/Asiu-x/dsh-harness2.git#bd9dfd077a0e6607197000c3f8706d1efe1cf257'

DSH versions accepted

Undeclared

Author and traction

Asiu-x · ★ 12 · Updated 2026-10-28

About this plugin

dsh-harness2

Harness² (recursive agent harnessing) for [DeepSeek Harness](https://github.com/deepseek-ai/deepseek-harness).

This is a DSH plugin that lets an agent improve its own harness from execution evidence: it runs a representative task under the current harness, proposes invariant and domain edits, probes them on the same task, composes an improved harness, selects an output anonymously, and stores the adopted harness plus the lesson in a per-domain library.

It ports the method of Google Research's [Harness²](https://github.com/google-research/harness2) ([paper](https://arxiv.org/abs/2610.14224), Apache-2.0) onto DSH's native agent and prompt surfaces. It does not re-run the paper's benchmarks.

> Status: 0.1.0, early release. Tested with DSH 0.1.5-rc.1 and Node.js 20+. See [Verification](#verification) and [Limitations](#limitations).

Install

From GitHub (public plugin package):

dsh plugin --profile web add github:Asiu-x/dsh-harness2

Restart DSH (or start dsh web) afterwards. The package declares a DSH bundle patch, so it is added to the profile's plugin tree automatically.

Local development:

git clone https://github.com/Asiu-x/dsh-harness2.git
dsh plugin --profile web add /absolute/path/to/dsh-harness2

Check that the plugin is in the composed tree:

dsh --profile web --dump-config | grep -A2 harness2

Quick start

The plugin registers three model tools:

| Tool | Purpose |

|---|---|

| harness2_refine | Run one Harness² improvement loop on a representative task. |

| harness2_status | Show the adopted harness, its components, the library, and the latest run. |

| harness2_reset | Archive the current harness and restore the seed. Requires confirm: true. |

Ask the agent to use harness2_refine on a task:

Use harness2_refine on this representative task:
"Read data/orders.csv, compute the total of the amount column, and write result.md containing exactly 'Total: ' on one line."
domain: data-analysis
k: 1

A k=1 sequential run is roughly nine child-agent runs: one base probe, two proposals, two candidate probes, one composition, one final probe, one selector, and one reflection. Start with k=1 and a small workspace. Model calls are billed by your DSH provider.

The human-facing command:

/harness2            status
/harness2 library    list the current domain's library
/harness2 reset      archive and restore the seed harness

Once a harness is adopted, ordinary DSH agents in that workspace receive its always-on text and recent lessons as a prompt section. A fresh session sees it at startup; the session that ran the refinement receives an adoption context message on its next step.

What it does

task ──► base rollouts ──► PROPOSE (INV + DOM) ──► PROBE ──► COMPOSE ──► final probe
                                                                 │
                                                                 ├─► anonymous SELECT
                                                                 └─► REFLECT ──► library
                                                                             │
                                                                             └─► next task
  • Propose — two proposers edit the harness: INV changes how the agent works; DOM adds what a practitioner of the domain knows. Proposals are the edited files, not patch descriptions.
  • Probe — each candidate runs the same task in an isolated copy of the workspace. The evidence is the trajectory, the artifact, and whether the run delivered.
  • Compose — one composer merges, adopts, or keeps the base, with a specific supporting moment for each decision.
  • Select — an anonymous selector compares rendered deliverables, not harnesses. Base is included as a conservative check; a no-op winner is not adopted.
  • Reflect — writes what each edit did and at most one transferable lesson into .dsh-harness2/library//.

Read [docs/method.md](docs/method.md) for the full method, the eight components, the DSH mapping, and the differences from the reference release.

The editable harness

The harness lives at .dsh-harness2/harness/:

| File | Role | Cost |

|---|---|---|

| system.md | role and procedure | always on |

| guardrails.md | standing rules | always on |

| memory.md | verified workspace facts | always on |

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