deepseekplugins

DeepSeek Harness plugin

dsh-plugin-mlquant-benchmark

DeepSeek Harness tools for reproducing the ml-quant-trading protocol v1 benchmark.

Tools & Capabilities
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Source facts

Repository
initial-d/dsh-plugin-mlquant-benchmark
Latest update
Aug 20, 2026
Category
Tools & Capabilities
GitHub stars
1
Format
plugin
Catalog evidence
Upstream dsh.bundle evidence
Evidence path
package.json#dsh.bundle
Checked against
0.1.0-rc.8
Upstream check date
2026-08-20

This evidence comes from the upstream catalog. This site has not installed, run, or security-reviewed the plugin.

Install

Start with a prompt that asks an agent to review the GitHub repository and source. Switch to the command if you want to install it yourself.

Copy this prompt into DSH, Codex, or another agent and ask it to review the GitHub repository and source first.

Do not install or run any commands yet. Read this plugin's GitHub repository, README, and relevant source code. Then answer the questions below clearly and directly so I can decide whether it fits my needs:

1. What is this plugin, and what problem does it solve?
2. Who is it for, and what are its typical use cases?
3. How is it used after installation? Include one minimal example.
4. What known limitations or privacy, security, compatibility, or maintenance risks does it have?
5. Give a clear recommendation: recommend, conditionally recommend, or do not recommend, with reasons.

Distinguish statements documented by the repository, inferences from source code, and unknowns. If evidence is insufficient, say so explicitly. Do not guess or simply repeat the README.

GitHub: https://github.com/initial-d/dsh-plugin-mlquant-benchmark
Plugin: dsh-plugin-mlquant-benchmark
Author: initial-d

Check the source files

Read the README and other files from this plugin directory before installing.

File explorer3 files
README.mdSource · read only

dsh-plugin-mlquant-benchmark

![CI](https://github.com/initial-d/dsh-plugin-mlquant-benchmark/actions/workflows/ci.yml)

DeepSeek Harness tools for reproducing the initial-d/ml-quant-trading protocol v1 CPU benchmark.

The point is narrow: make a DSH agent able to run the existing benchmark, read the machine-readable artifact, validate it against the benchmark protocol, and draft an issue-ready report. This plugin does not add a trading agent, does not call market data APIs, and does not configure any model provider.

Why this exists

ml-quant-trading is a good reproducibility target for agent harnesses:

  • deterministic synthetic benchmark input;
  • fixed protocol v1 command, seed, panel size, repetitions, and thread counts;
  • JSON artifact suitable for automated checking;
  • public issue template for DeepSeek Harness benchmark reports;
  • explicit boundary that benchmark throughput is not trading performance.

Challenge: can DeepSeek Harness reproduce a quant benchmark end to end, preserve the evidence bundle, and avoid turning runtime numbers into alpha claims?

Tools

This package registers four DSH tools:

ToolPurpose
mlquant_benchmark_v1_cpuRun the fixed protocol v1 CPU benchmark and write artifacts/benchmark-v1.json.
mlquant_read_benchmark_jsonRead the JSON artifact and render a compact Markdown result table.
mlquant_validate_benchmark_jsonCheck protocol v1 fields, expected cases, fixed parameters, and variance warnings.
mlquant_draft_github_issueDraft a DeepSeek Harness benchmark issue body from the JSON artifact. It does not post to GitHub.

Install

Install the package in a DeepSeek Harness profile or preset environment:

dsh plugin --profile web add github:initial-d/dsh-plugin-mlquant-benchmark

The package declares a dsh.bundle manifest that inserts:

- id: mlquant-benchmark
  name: dsh-plugin-mlquant-benchmark

If you use a local checkout while developing, add the same row manually:

- id: mlquant-benchmark
  name: file:/path/to/dsh-plugin-mlquant-benchmark

This package is intentionally not published to npm yet. GitHub distribution is enough for the first DSH-facing benchmark reports; npm can come later if there is real usage.

Suggested DSH prompt

Read AGENTS.md, docs/benchmarking.md, and docs/reality_check.md.
Use the mlquant benchmark tools to run the protocol v1 CPU benchmark, validate
and read the JSON artifact, and draft a DeepSeek Harness benchmark report. Keep
the result as an engineering reproducibility benchmark, not a trading-performance
claim.

Public report path

Post the drafted report through the main repository's dedicated template:

<https://github.com/initial-d/ml-quant-trading/issues/new?template=deepseek_harness_benchmark.yml>

Seed example:

<https://github.com/initial-d/ml-quant-trading/issues/61>

Development

npm install
npm test

The test loads the plugin with a mock ctx.tools.register, verifies that the four tools register, reads and validates sample artifacts, and drafts an issue body.

Non-goals

  • No investment advice.
  • No backtest-performance claim.
  • No hidden model provider configuration.
  • No posting to GitHub from the tool.
  • No private data or API keys in artifacts.
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