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Polaris

Workflow Updated 2026.08.16

Run the following command in DeepSeek Harness:

dsh plugin install existyay/Polaris

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

Install the plugin in DeepSeek Harness by running the install command; the source repository is https://github.com/existyay/Polaris .

About this plugin

Tooling fragmentation is a daily drag for developers: DSH plugins, third-party MCP/Skills, and emerging desktop utilities each speak their own dialect, forcing you to manually select, verify, and dispatch for every new intent. Polaris turns that chore into a single general-purpose aggregation and evolution layer. It continuously sniffs heterogeneous sources, normalizes them into a unified Polaris IR contract, benchmarks and validates them through a dual-track arena (hot-start smoke tests, cold-release contract checks, fuzzing, benchmarks, SBOM verification, and adversarial replay), seals provenance with Ed25519 signatures, and pins everything in an immutable content-addressed index. A deterministic semantic router then maps one developer intent to the currently best implementation, with the underlying dispatch fully transparent.

After installation and a profile restart, the seven atomic primitives register simultaneously as model tools. During dependency changes, authentication rewrites, bounded smoke execution, coverage-gated testing, symbol retrieval, and license compliance checks, your development agent invokes the appropriate capability automatically-hands-off, no manual commands required. Slash commands remain available for explicit human operations. Each primitive is independently toggleable, stateless, single-responsibility, composable, and verifiable; every pipeline stage declares file-count, size, timeout, and output ceilings, keeping costs naturally bounded.

Polaris is purpose-built for STEM domains (mathematics, physics, chemistry, engineering, numerical methods, simulation) and code-optimization workflows (performance, refactoring, static analysis, test quality, benchmarking, debugging). It is a strong fit for engineers and researchers who switch between research and high-performance coding and need an auditable, swappable toolchain that gets out of the way.

Use Cases

  • Automatically dispatching the best numerical-simulation and benchmark tools in a research project
  • Triggering dependency audits and coverage-gated tests after authentication or dependency changes
  • Transparently routing a single developer intent to the optimal implementation across a multi-plugin ecosystem

Best For

  • Engineers working in mathematics, physics, chemistry, engineering, and other STEM research domains
  • Practitioners with high demands on performance, refactoring, static analysis, and test quality
  • Teams managing multi-plugin ecosystems who need auditable, swappable toolchains