eigenhelm 0.6.0

Software Poet Conscience — language-agnostic code aesthetic evaluation

eigenhelm

A conscience for agents, not an agent — language-agnostic code aesthetic evaluation sidecar.

Eigenhelm scores agent-generated code against mathematical aesthetic metrics derived from information theory, complexity science, and a PCA eigenspace trained on curated elite corpora. It runs alongside code-generating agents as a real-time quality signal.

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Quick Start

Install

pip install eigenhelm

Or with uv (no venv required):

uv tool install eigenhelm

Evaluate a file

A bundled model is included — no setup needed.

eh evaluate path/to/file.py --classify

Evaluate a directory

eh evaluate src/ --rank

Ranks all files best-to-worst and highlights the bottom performers.

What the scores mean

• accept (score < 0.4): Structurally sound. Move on.
• marginal (score 0.4-0.6): Fine. No action needed.
• reject (score > 0.6): Worth reviewing. Read the directives for guidance.

Scores are relative to elite open-source training corpora. Most production code scores marginal or reject — that's normal, not a problem.

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Agent Integration

Eigenhelm ships with a skill file that teaches AI agents how to use it correctly.

# Install via skills registry (recommended)
npx skills add metacogdev/skills

# Or install directly from eigenhelm CLI
eh skill --install

The skill encodes a tested workflow contract: evaluate after tests pass, two passes maximum, never sacrifice correctness for score. In controlled benchmarks across 3 project types, agents using the skill produced code rated 46% higher on design, robustness, and spec compliance by an independent reviewer, with no correctness regressions.

Full agent integration guide

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CLI Reference

All commands are available as eigenhelm <command> or eh <command>:

Command	Description
eh evaluate	Evaluate source files against the aesthetic manifold
eh train	Train a new eigenspace model from a corpus directory
eh inspect	Inspect a saved model's metadata
eh serve	Run the evaluation HTTP server
eh harness	Run a statistical comparison harness across two code sets
eh skill	Install the agent skill file
eh init	Generate a starter .eigenhelm.toml configuration
eh corpus	Manage training corpora (sync from manifest)

Run eh --help or eh <command> --help for details.

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HTTP API

Endpoint	Method	Description
/health	GET	Liveness probe
/evaluate	POST	Evaluate a code unit
/evaluate/batch	POST	Evaluate multiple code units

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Supported Languages

Trained models: Python, JavaScript, TypeScript, Go, Rust.

Parser support (feature extraction available, bring your own model): Java, C, C++, Ruby, Kotlin.

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Development Setup

git clone https://github.com/metacogdev/eigenhelm.git
cd eigenhelm
uv sync --extra dev --extra serve
uv run pytest
uv run ruff check .

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Architecture

eigenhelm/
├── virtue_extractor.py   — Tree-sitter + Lizard → FeatureVector (69 dimensions)
├── critic/               — AestheticCritic: 5-dim scoring (drift, alignment, entropy, compression, NCD)
├── declarations/         — Declaration-aware scoring (type defs, barrel files, data tables)
├── regions/              — Test/production code region detection
├── eigenspace/           — EigenspaceModel: PCA projection, drift scoring
├── attribution/          — Score attribution and directive generation
├── training/             — PCA training, calibration, exemplar selection
├── helm/                 — DynamicHelm: threshold-calibrated evaluation + PID steering
├── config/               — .eigenhelm.toml loader and models
├── output/               — SARIF 2.1.0 and JSON formatters
├── scoring/              — Per-repo scorecard (M1-M5, Q1-Q5)
├── harness/              — Statistical evaluation harness (Mann-Whitney U)
└── serve/                — FastAPI HTTP evaluation server

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Current Status

• 5-dim scoring: manifold drift, alignment, entropy, compression, NCD exemplar distance
• 5 languages: Python, JavaScript, TypeScript, Go, Rust — all discriminating (Cohen's d > 0.5)
• Human correlation: Spearman rho = 0.54 overall (n = 92, 5 languages), 0.66 Python-only (n = 52)
• Declaration-aware: Automatically detects type-definition and data-table files, adjusts scoring and directives
• Agent-tested: Skill contract validated in controlled arena (3 scenarios, 46% quality improvement)

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License

eigenhelm is licensed under the GNU Affero General Public License v3.0.

Commercial Licensing

Looking to use eigenhelm in a proprietary SaaS or enterprise product without AGPL-3.0 obligations? A commercial license is available.

Contact us at [e-mail] to discuss terms.