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Agent skill · Audited from source

Symbolic Regression For Constants Using Pysr

skill · ECNU-ICALK/AutoSkillLicence not declaredAudited 14 Oct 2026 · source 04e912cff54b

★ 602pushed 157 days agoversion 0.1.0
✓SAFEGrade B · 89/100 · 14 Oct 2026 · 04e912c
602repo stars · whole ECNU-ICALK/AutoSkill
InstallView source on GitHub ↗

Is it safe? Yes — SAFE, grade B (89/100). We read this skill's source at commit 04e912cff54b and found no filesystem, network or shell access and no hard-coded secrets. What it does: Generates Python code using PySR to find mathematical expressions approximating a target constant (like the Fine Structure Constant) using mathematical or dimensionless physical constants as input features.

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ModelsAtlas. “Symbolic Regression For Constants Using Pysr (ECNU-ICALK/AutoSkill)”. Retrieved 14 Oct 2026. https://modelsatlas.com/skills/ecnu-icalk/symbolic-regression-for-constants-using-pysr

Trust audit

Audit 0.4.1 · source read 14 Oct 2026

Trust score89/100✓SAFE B
0 critical0 high0 medium0 low0 info

SAFE means nothing in the source contradicts what the skill says it does. Grade B is shared by 16,667 of 17,602 audited skills.

What the source does

  • FilesystemNone observedNo reads or writes in the source
  • NetworkNone observedNo outbound calls in the source
  • ShellNone observedNo commands run in the source
  • DependenciesPinnedNothing floats
  • SecretsNone foundNo keys in source

Five audit layers

LayerWhat we checkResultFindings
L0 ProvenanceRepo owner, licence, archive status, activityPASS0
L1 Static sourceSecrets, path traversal, supply chain, hidden codeNA0
L2 InstructionsPrompt injection, authority framing, hidden directivesPASS0
L3 InventoryThe files and declarations match the sourcePASS0
L4 SandboxRun in isolation and watch behaviourSKIPPED why0

Gates applied: no behavioural pass.

Findings (0)

No findings at any severity. We re-audit when the source changes.

Audit 0.4.1 · source sha 04e912cff54b · 14 Oct 2026Report an issue or request a re-scan

Provenance

Who published it, and what the verdict is pinned to

What it tells the agent

Full source on GitHub ↗

Its description: Generates Python code using PySR to find mathematical expressions approximating a target constant (like the Fine Structure Constant) using mathematical or dimensionless physical constants as input features.

---
id: "0aa6d2b5-92bb-4447-817f-2f95bc5a65fc"
name: "Symbolic Regression for Constants using PySR"
description: "Generates Python code using PySR to find mathematical expressions approximating a target constant (like the Fine Structure Constant) using mathematical or dimensionless physical constants as input features."
version: "0.1.0"
tags:
  - "PySR"
  - "Symbolic Regression"
  - "Physics Constants"
  - "Python"
  - "Fine Structure Constant"
triggers:
  - "Use PySR to find a formula for alpha"
  - "Symbolic regression for constants"
  - "Find expression for fine structure constant"
  - "PySR mathematical constants"
  - "Generate synthetic dataset for symbolic regression"
---

# Symbolic Regression for Constants using PySR

Generates Python code using PySR to find mathematical expressions approximating a target constant (like the Fine Structure Constant) using mathematical or dimensionless physical constants as input features.

## Prompt

# Role & Objective
You are a Symbolic Regression specialist. Your task is to formulate and implement a PySR-based solution to express a target constant (e.g., the Fine Structure Constant) as a function of a set of input constants.

# Operational Rules & Constraints
1.  **Target Definition**: Define the target constant value with the requested precision (e.g., 10 decimals).
2.  **Dataset Generation**: Create a synthetic dataset. The target vector `y` should be an array filled with the target constant value. The feature matrix `X` should contain the input constants (mathematical or dimensionless physical combinations).
3.  **Constant Integration**: Integrate a set of mathematical constants (e.g., pi, e, phi) or dimensionless combinations of physical constants as features.
4.  **PySR Configuration**: Configure `PySRRegressor` with `extra_sympy_mappings` to map constant names to their values. Use `model_selection="best"` to prioritize accuracy.
5.  **Dimensional Consistency**: If physical constants are used, ensure they are combined into dimensionless ratios before inclusion to maintain dimensional consistency.

# Anti-Patterns
- Do not use dimensionful physical constants directly without ensuring the result is dimensionless.
- Do not use varying data inputs for the features if the goal is to find a constant relation; the features should be the constant values themselves.

## Triggers

- Use PySR to find a formula for alpha
- Symbolic regression for constants
- Find expression for fine structure constant
- PySR mathematical constants
- Generate synthetic dataset for symbolic regression

Excerpt, first 2,563 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

Install

Pinned to the audited commit 04e912c

Download folder ↗
git clone https://github.com/ECNU-ICALK/AutoSkill
git -C AutoSkill checkout 04e912cff54b

The author documents no install command, so this uses the repository directly. Our verdict covers commit 04e912cff54b only.

Works in

The author has not said which agents this skill supports, and we have not tested it in any.

Audit history

Every audit, every source change

  1. ✓SAFE B 89 / 100

    first audit · source 04e912cff54b

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Questions about Symbolic Regression For Constants Using Pysr

What does the Symbolic Regression For Constants Using Pysr skill do?
AutoSkill: Experience-Driven Lifelong Learning via Skill Self-Evolution
Is Symbolic Regression For Constants Using Pysr safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can Symbolic Regression For Constants Using Pysr access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (04e912cff54b), read on 2026-10-14. The repository is watched, and a new audit runs when it changes — this is the first audit.

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