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dsh-operating-context

Model Inference Updated 2026.08.20

Run the following command in DeepSeek Harness:

dsh plugin install AIMFllyYS/dsh-operating-context

Paste the following prompt into your AI chat to install this plugin:

Run dsh plugin install AIMFllyYS/dsh-operating-context in the terminal to install; the source code is hosted at https://github.com/AIMFllyYS/dsh-operating-context .

About this plugin

When running multiple model services under DeepSeek Harness, context window sizes are scattered across the catalog, per-model lists, and defaultContextWindow, with no intuitive single place to keep them consistent. Hand-editing settings.yaml is error-prone, and patching adapter config directly risks wiping endpoints, credentials, and other critical fields.

dsh-operating-context adds a Working Window option to the Settings page. All writes go through settings.mutate in the user layer: it does not mount a second compaction engine or alter adapter internals. It reads each model's native ceiling from the installed catalog, guaranteeing the chosen window never exceeds what a model can actually hold. When the selected value is at or below a model's ceiling, the plugin removes any existing capacity override so the catalog native value takes effect again, making the operation idempotent and reversible. Cross-namespace batch writes report the exact number of completed batches on partial failure and reload authoritative state rather than presenting a partial write as a rollback.

Ideal for users managing multiple providers (DeepSeek, Kimi, Anthropic, OpenCode, and beyond) in Harness who need a unified context window size and want to avoid manual YAML edits. Catalog routes, routes with explicit model lists, and hand-declared routes are all supported through the same settings page.

Screenshots

Use Cases

  • Unify context window size across multiple model services
  • Cap the selected window at each model native ceiling
  • Avoid manual YAML edits or risky adapter config patching

Best For

  • Users running multiple providers under DeepSeek Harness
  • Teams needing centralized context window control without manual YAML upkeep
  • Developers who want to adjust model capacity without touching adapter internals