DSH Routing Suite
yjh051108
Three components, three install paths: a restart-free runtime plugin injector, routing agent presets, and a graded task protocol.
Use cases
Architecture awareness, drift checks and evidence-based review passes.
This directory lists 6 DeepSeek Harness plugins for Code Review. Their verification levels run L1 0, L2 3, L3 3, L4 0, L5 0: no listing here has been installed or run in a sandbox, so none claims L4 or L5. 0 have passed an automated security scan, and the other 6 are marked unverified rather than safe. Declared compatibility targets are unstated 3, ^0.1.1-rc.2 || ^0.1.2-rc.1 1, >=0.1.0-rc.6 <0.2.0 1, >=0.1.5-rc.1 <0.1.6-0 1. Together these 6 listings carry 15,120 GitHub stars, led by DSH Routing Suite at 7,194. Licences are mainly MIT 5, Apache-2.0 1, and the code is written mostly in JavaScript, Python, TypeScript. Every entry records its source repository, licence, declared harness target and a copy-ready install command; the most recently checked is Aegis (2026-09-18). A verification level measures how far the install path was exercised — it is not a safety guarantee, so read the source before you install.
Review plugins aim to make an agent a better reviewer: establishing a baseline of intended architecture, checking whether a change drifted from it, requiring evidence before a claim of success, and summarizing risk in a diff. They do not replace a human on a merge decision; they make the first pass harder to bluff.
Prefer plugins that produce a reviewable artifact — a written report, a failing check, an approval prompt — over ones that only assert the code looks fine. A review that cannot fail is not a review. The strongest listings in this category treat “done” as a claim that needs evidence, not a sentence the model is allowed to utter.
Look for three mechanisms. Baseline: does the plugin record what the system was supposed to look like before the change? Drift: can it show what moved away from that baseline without you re-reading the whole tree? Gate: does the agent have to attach a check, a test result, or a human approval before a task is marked complete?
Also check scope. A review layer that only scores the model’s own summary is theatre. One that reads the diff, the failing tests, or an architecture map is closer to the job.
A strong review plugin is explicit about what it measured. It names the contracts or architecture nodes it compared. It refuses to stamp success when evidence is missing. It keeps a durable record you can open later — not only a chat message that scrolls away. Task boards and multi-agent orchestrators often sit next to this use case: they help when work is genuinely parallel, and add ceremony when the task is a one-file fix.
If two plugins overlap, prefer the one that can show a failing path. “The agent said it reviewed the code” is not a control.
The common failure is a green badge driven by the same model that wrote the change. The second is a baseline that is never updated, so every later diff looks like drift. The third is reviewing only the last message instead of the working tree — the model’s narrative of the edit, not the edit itself.
Prefer a code-review plugin before another general-purpose agent bundle. Make diffs and suggestions reviewable before you allow automated commits. Ungated auto-fix will change the repo while you are away.
yjh051108
Three components, three install paths: a restart-free runtime plugin injector, routing agent presets, and a graded task protocol.
Q00
A spec-first agent OS for AI coding — Socratic interview, immutable Seed, staged evaluation, MCP server.
GanyuanRan
A method pack that makes agents plan against your project baseline, prove completion with fresh evidence, and leave simple tasks on the fast path.
shengsheng90
A local SQLite project taskboard for DSH with a native Web UI and eight agent tools, where only a human can accept work as done.
GraySilver
Four recomposable controls in the composer — working state, reasoning strategy, quality gate, and a self-evolution loop that proposes rules but never applies one without your review.
toolclub
Persistent multi-model workflow teams in Settings → Teams — per-member model/tools, bounded DAG with retries, Run Center and provider Token insights.
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