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Linalg — DSH Plugin for DeepSeek Harness
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dsh-linalg

Linalg

Linear algebra toolbox for DeepSeek Harness (dsh): matrix multiply, determinant, inverse, trace, transpose, RREF, linear system solver with unique/infinite/none classification, and vector ops (dot/cross/norm/projection/angle) — zero runtime dependencies

The plugin will be installed here. Keep web if you are unsure.

npx -y @deepseek-ai/dsh plugin --profile web add github:TYEclipse/dsh-linalg#6167204286846f40249dbd1e90384fb135f37124
READMECompatibilityVersions

Compatibility and provenance

Linalg is published as dsh-linalg and currently resolves to version 0.1.3. The Hub verifies its manifest and preserves the exact installation source for reproducible installs.

DSH compatibility
*
Runtime surfaces
any
Release source
github
Registry updated
9/10/2026

Versions

0.1.3stable
9/10/2026
0.1.0stable
9/7/2026
Latest
0.1.3
DSH
*
HMR
Process restart
Tree shaking
Safe tree shaking not declared
Unpacked size
Unavailable
Files
Unavailable
Surface
any
License
MIT
Source
github
GitHub
★ 0
Weekly downloads
0
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README

dsh-linalg

Linear algebra toolbox for DeepSeek Harness (dsh): matrix multiply, determinant, inverse, trace, transpose, reduced row echelon form, a linear system solver that classifies unique / infinitely many / no solutions, and vector operations (dot / cross / norm / projection / angle).

Zero runtime dependencies, pure local arithmetic — no network, no processes, no eval.

Why

Language models frequently make arithmetic errors on matrix multiplication, determinants, inverses and linear systems. This plugin hands those computations to exact local code with Gaussian elimination and partial pivoting, so results are deterministic and correct to 10 decimal places.

Install

dsh plugin --profile web add github:TYEclipse/dsh-linalg

Replace web with your profile name. (Requires pnpm on your PATH.)

Tools

ToolWhat it does
matrix_multiplyMultiply two matrices A·B with dimension checking
matrix_computeOne operation per call: transpose, determinant, inverse, trace, rref
solve_linearSolve Ax = b; classifies unique / infinite (particular + nullspace basis + free-variable count) / no solution
vector_opsdot, cross (3D), norm, projection, angle (degrees)

Input matrices are plain JSON arrays of arrays, e.g. [[1, 2], [3, 4]]. Maximum dimension is 20×20 by default (configurable).

Examples

Determinant:

matrix_compute(matrix=[[2,1,1],[1,3,2],[1,0,0]], op="determinant")  →  -1

Inverse:

matrix_compute(matrix=[[4,7],[2,6]], op="inverse")
→ [[0.6,-0.7],[-0.2,0.4]]

Solve 2x+3y=8, x−y=1:

solve_linear(a=[[2,3],[1,-1]], b=[8,1])
→ unique solution: [2.2, 1.2]

Under-determined system (1 equation, 3 variables) reports the full parametric form:

solve_linear(a=[[1,1,1],[1,2,3]], b=[6,14])
→ kind=infinite, particular=[-2,8,0], nullspaceBasis=[[1,-2,1]], freeVariableCount=1

Cross product:

vector_ops(op="cross", a=[1,2,3], b=[4,5,6])  →  [-3,6,-3]

Angle between [3,4] and the x-axis:

vector_ops(op="angle", a=[3,4], b=[4,0])  →  53.1301023542  (degrees)

Numerical behaviour

  • Gaussian elimination with partial pivoting (largest-magnitude pivot per column).
  • All output numbers are rounded to 10 decimal places (configurable roundPlaces); values below 5e-12 are snapped to exactly 0, so -0 never appears.
  • Pivot tolerance is 1e-12: entries below that are treated as zero, so numerically singular matrices are reported as singular instead of producing garbage.
  • Singular inverse requests fail cleanly with an error message; inconsistent systems return kind: "none" with the reducing row equation.

Configuration

- name: 'github:TYEclipse/dsh-linalg'
  config:
    maxDimension: 20   # max rows/columns (1–50)
    roundPlaces: 10    # decimals in every output number (1–15)

Development

pnpm install
pnpm build
pnpm test
pnpm lint

License

MIT