dsh-zai-coding-models
DeepSeek Harness 桥接插件：在内置的 pi-ai 目录更新之前，通过 zai-coding-cn 路由提供最新的 Zhipu/Z.AI 编程计划模型（例如 glm-5.3）。
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:auuduu/dsh-zai-coding-models#f09d69a7eaa4016b6db9c24069ebd7fa989d0823Zai Coding Models 以 dsh-zai-coding-models 发布，当前版本为 0.1.0。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。
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A DeepSeek Harness (DSH) bridge
plugin that exposes the newest Zhipu / Z.AI coding-plan models — currently
GLM-5.3 — on the stock zai-coding-cn provider route, before the pi-ai
catalog bundled with your DSH release catches up.
If you bought a Zhipu (智谱) coding plan / token plan, added the API key in the
DSH Models page, and the model picker only offers models one generation behind
(no GLM-5.3), this plugin is for you: your token plan works with the newest
models the moment Zhipu ships them — no DSH upgrade, no restart dance, no
hand-editing of settings.yaml.
DSH resolves each provider's model list from the pi-ai catalog compiled into
its dependency tree. When Zhipu ships a new model (GLM-5.3 went live on the
coding endpoint in August 2026) the catalog lags behind by a release cycle or
two. DSH has a first-class escape hatch — a provider profile's models list
declared in configuration — and this plugin is exactly that escape hatch,
packaged so dsh plugin add carries it for you:
zai-coding-cn models untouched (glm-4.5-air,
glm-4.7, glm-5-turbo, glm-5.1, glm-5.2, glm-5v-turbo);thinkingFormat: zai,
supportsReasoningEffort: true — without the latter the effort parameter is
silently dropped);dsh plugin --profile web add dsh-zai-coding-models
(replace web with tui/headless/your profile). Then restart DSH once —
plugin bundles are read at boot. Your ZAI_CODING_CN_API_KEY (stored through
the Models page) is picked up as-is; GLM-5.3 appears in the model picker under
the existing Z.AI Coding CN provider.
To remove: dsh plugin --profile web remove dsh-zai-coding-models.
The package declares a dsh.bundle.patch layer that targets the stock
llm-pi-ai composition row and supplies a base provider profile:
- id: llm-pi-ai
config:
providers:
zai-coding-cn:
models:
- id: glm-4.5-air # id-only entries inherit
- id: glm-4.7 # everything from the
- id: glm-5-turbo # installed catalog
- id: glm-5.1
- id: glm-5.2
- id: glm-5v-turbo
- id: glm-5.3
name: GLM-5.3
contextWindow: 1000000
maxTokens: 131072
reasoningEfforts:
low: low
medium: high
high: high
max: max
compat:
thinkingFormat: zai
supportsReasoningEffort: true
DSH merges your user settings per-provider on top of this base, so a profile
that only stores apiKeyEnv: ZAI_CODING_CN_API_KEY (what the Models page
writes) merges cleanly. The same YAML works standalone in
~/.dsh/settings.yaml if you prefer not to install anything — see
the manual recipe.
models list replaces the route's catalog — that is DSH semantics,
not a choice. The list is pinned per plugin release, so when pi-ai ships
glm-5.4, this plugin must release a new version to carry it (and if it
already bundles the model you wanted, uninstall the plugin to return to the
catalog). Watch releases, or just check the picker after DSH upgrades.settings.yaml already declares models for
zai-coding-cn, your layer wins over the plugin's. Keep exactly one source.llm-pi-ai row; the stock row
ships config-less, but if a future DSH release puts config there, this
plugin may need a bump.Add to ~/.dsh/settings.yaml (hot-reloaded, no restart needed):
llm-pi-ai:
providers:
zai-coding-cn:
apiKeyEnv: ZAI_CODING_CN_API_KEY # keep whatever you have
models:
- id: glm-4.5-air
- id: glm-4.7
- id: glm-5-turbo
- id: glm-5.1
- id: glm-5.2
- id: glm-5v-turbo
- id: glm-5.3
name: GLM-5.3
contextWindow: 1000000
maxTokens: 131072
reasoningEfforts:
low: low
medium: high
high: high
max: max
compat:
thinkingFormat: zai
supportsReasoningEffort: true
pi (the project behind pi-ai) already carries glm-5.3 on main
(packages/ai, generated from the models.dev database); the moment a pi-ai
release ships and a DSH release adopts it, this plugin becomes redundant for
that model. It is intentionally a bridge, following the contribution path DSH
recommends for ecosystem work.
MIT