Compute principal component analysis of a 2D point set without NumPy

baltor_pca_2d

Compute principal component analysis of a 2D point set without NumPy: mean, covariance, eigenvalues and unit eigenvectors, principal angle, explained variance, scores and first-component reconstruction, with decomposition self-checks.

Files, names only

LICENSE 1.0 KB
SKILL.md 1.5 KB
contracts/input.schema.json 990 bytes
contracts/output.schema.json 4.0 KB
examples/example.expected.json 937 bytes
examples/example.input.json 146 bytes
references/README.md 3.1 KB
references/provenance.json 4.1 KB
scripts/tool.py 11.0 KB
verification/test_tool.py 8.8 KB

Full files in your harness.

What it takes and gives

Command: python3 -B scripts/tool.py < examples/example.input.json
Input fields: points, normalization, include_projections
Test: python3 -B -m unittest discover -s verification -p 'test_*.py' -v
Files: SKILL.md, contracts/input.schema.json, contracts/output.schema.json, scripts/tool.py
Placed for: Claude Code, Codex, OpenCode, Pi

Details

Kind: Skill
Licence: MIT
Declared effects: reads files, runs commands
Placed for: Claude Code, Codex, OpenCode, Pi
Source: docs/architecture/NORTH-STAR.md
Size: 10 files, 35.6 KB
Identity: baltor_pca_2d
Version: e80c7977438f00e723ad999356fb38ac0cb013494f8168103aaec3c876fcf738

The version is the SHA-256 of the item your harness downloads; every file in it names its own checksum.

Where it is shown

Algorithms that show their workDeterministic skills

Related components

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