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pysr

Use when fitting equations to data with PySR or SymbolicRegression.jl, when a user wants an interpretable formula, symbolic model, scaling law, or empirical relation discovered from numeric data, or when debugging a PySR search that is slow, stuck, or giving poor equations.

72

Quality

89%

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SKILL.md
Quality
Evals
Security

Quality

Content

78%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is highly actionable and well-sequenced with executable examples and a clear recommended workflow, but it is a long single-file monolith that inlines several advanced topics which would benefit from being split into one-level-deep reference files.

Suggestions

Split the deepest advanced sections (Custom value types: TypeSpec, complexity_mapping, PDE discovery, Dimensional constraints) into separate reference files under references/ and link to them from SKILL.md with one-line summaries, so the main file stays a lean overview.

Break up the densest run-on paragraphs (notably the guesses insertion and TypeSpec configuration passages) into shorter bullets or labeled sub-steps to improve scannability and reduce token cost per concept.

Add an explicit validate→fix→retry checkpoint in the Recommended workflow (e.g., after the brief debug run: inspect equations_, confirm loss is sensible, then only proceed to the long-budget run) to make the workflow feedback loop explicit.

DimensionReasoningScore

Conciseness

The body is dense with genuinely non-obvious PySR/Julia operational gotchas and largely avoids explaining concepts Claude already knows, but at ~350 lines several run-on passages (e.g., the guesses and TypeSpec sections) pack multiple gotchas into single sentences that could be trimmed or split for readability.

4 / 5

Actionability

Every major section ships copy-paste-ready, executable code with concrete parameters and exact signatures — Quick start, custom operators, loss_function, TemplateExpressionSpec, TypeSpec, and complexity_mapping all give complete runnable examples covering the common cases.

5 / 5

Workflow Clarity

The "Recommended workflow" is a clear 6-step numbered sequence with concrete guidance and implicit checkpoints ("Debug briefly" before a long run, monitor hall_of_fame.csv), and the Troubleshooting table provides error-recovery feedback; it stops short of explicit validate→fix→retry loops but the task is not destructive, so no cap applies.

4 / 5

Progressive Disclosure

The file is well-sectioned with clear headers, but it is a ~350-line monolith with no bundle/reference files, and substantial advanced material (TypeSpec, complexity_mapping, PDE discovery, dimensional constraints) is inlined that would more naturally live one level deep in separate reference files.

3 / 5

Total

16

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is third-person, concise, and explicitly pairs a concrete "what" with an explicit "Use when" trigger covering multiple natural phrasings and synonyms. It is distinct to its niche with negligible conflict risk.

DimensionReasoningScore

Specificity

Names multiple concrete actions — "fitting equations to data", discovering "interpretable formula, symbolic model, scaling law, or empirical relation", and "debugging a PySR search that is slow, stuck, or giving poor equations" — with comprehensive coverage of the domain's actions.

5 / 5

Completeness

An explicit "Use when ..." clause gives concrete triggers (fitting equations, wanting an interpretable formula, debugging a slow/stuck/poor search) while the same sentence states what the skill does, clearly answering both what and when.

5 / 5

Trigger Term Quality

Covers the natural phrases a user would say ("interpretable formula", "scaling law", "symbolic model", "empirical relation", "fitting equations to data") plus synonyms and the tool names PySR/SymbolicRegression.jl; no meaningful natural term is missing for this domain.

5 / 5

Distinctiveness Conflict Risk

The triggers are tightly bound to a specific niche (symbolic regression via PySR/SymbolicRegression.jl), making conflict with unrelated skills minimal and the niche clearly distinct.

5 / 5

Total

20

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

relative_links

Relative link issues: 1 suspicious

Warning

Total

15

/

16

Passed

Repository
astroautomata/PySR
Reviewed

Table of Contents

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