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theory-formalism-translator

theory-formalism-translator

github.com/jurgendnProductivity1148 installsUpdated 6w ago
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About theory-formalism-translator

A mathematical reasoning skill for researchers translating concepts between formalisms. It maps structural objects, distinguishes equivalence from analogy, and identifies what is preserved, lost, or newly tractable.

Key features

  1. 01Precise description of source formalism
  2. 02Mapping of structural objects
  3. 03Separation of equivalence and analogy
  4. 04Analysis of preserved and lost structure
  5. 05Identification of newly available tools

Use cases

  1. 01Translating a model into another mathematical framework
  2. 02Checking whether two concepts share real structure
  3. 03Finding analysis tools enabled by a translation

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/jurgendn/agent-skills --skill theory-formalism-translator

After installing, type/theory-formalism-translatorin Claude Code to confirm it loaded.

Details

From the project README

Formalism Translator

Look for structural equivalence, not superficial analogy.

The goal is not to rename concepts.
The goal is to determine whether:

  • two formalisms encode the same invariant structure;
  • one formalism exposes stronger tools;
  • or the similarity is only metaphorical.

Procedure

1. State the Original Formalism

Describe the concept in its native language.

Specify:

  • objects;
  • operators;
  • constraints;
  • dynamics;
  • optimization target;
  • probabilistic interpretation if applicable.

Example:

Original formalism:
Random walk on a graph with transition matrix P,
analyzed through mixing time and spectral gap.

Avoid vague summaries such as:

Diffusion process on networks.

2. Identify Core Structural Objects

Extract the mathematical structure underneath the notation.

Look for:

  • state space;
  • energy or objective function;
  • conservation law;
  • invariant measure;
  • transition operator;
  • geometry;
  • symmetry;
  • equilibrium condition;
  • entropy;
  • curvature;
  • flow;
  • distance metric.

Example:

Core objects:
- Markov operator P
- stationary distribution π
- spectral gap γ
- diffusion dynamics
- local-to-global information propagation

This step matters more than terminology.


3. Define the Translation Target

State the target formalism explicitly.

Possible targets:

  • optimization;
  • probability;
  • information theory;
  • statistical physics;
  • dynamical systems;
  • control theory;
  • graph signal processing;
  • variational inference;
  • optimal transport;
  • spectral theory.

Example:

Target formalism:
Optimal transport interpretation of graph diffusion.

4. Map Objects Across Formalisms

Construct an explicit correspondence.

Example:

Original FormalismTarget Formalism
Random walk transitionTransport kernel
Stationary distributionEquilibrium measure
Spectral gapContraction / mixing rate
Diffusion distanceWasserstein-like geometry
Community boundaryTransport bottleneck

Do not skip mismatches.

If the mapping is only partial, say so.


5. Identify Preserved Structure

State what survives the translation.

Examples:

Preserved:
- equilibrium structure
- diffusion locality
- entropy decay
- conservation of probability mass

Distinguish:

  • exact equivalence;
  • asymptotic equivalence;
  • heuristic similarity.

6. Identify What Is Lost

Every translation discards information.

Examples:

Lost:
- discrete graph combinatorics
- exact path interpretation
- local neighborhood semantics
- node-level interpretability

Do not present the target formalism as strictly superior.


7. Identify What Becomes Easier

Ask whether the new formalism provides:

  • stronger theorems;
  • cleaner optimization;
  • better geometry;
  • sharper concentration;
  • stability analysis;
  • computational advantages;
  • alternative intuition.

Example:

Optimal transport formalism may provide:
- metric geometry tools;
- contraction analysis;
- curvature interpretation;
- continuous relaxation methods.

8. Check Whether the Translation Is Genuine

Distinguish between:

  • structural equivalence;
  • reusable machinery;
  • notation change only.

Bad translation:

Graph diffusion is basically attention.

Better:

Both can be interpreted as weighted information propagation operators,
but attention is data-adaptive and non-Markovian,
while graph diffusion is typically fixed and stochastic.

9. Suggest New Tools Enabled by the Translation

If the translation is useful, identify concrete tools now available.

Examples:

New FormalismPotential Tools
Optimal transportWasserstein geometry, Sinkhorn
Dynamical systemsStability analysis, Lyapunov functions
Information theoryEntropy bounds, mutual information
Spectral theoryEigenvalue perturbation
Control theoryControllability, observability

If no genuinely new tool appears, state that explicitly.


Rules

  • Do not claim equivalence unless the mapping is explicit.
  • Surface mismatches, not just analogies.
  • Distinguish:
    • exact equivalence;
    • asymptotic similarity;
    • heuristic resemblance.
  • Preserve assumptions during translation.
  • Do not confuse shared notation with shared structure.
  • If the translation only renames concepts, say so.
  • Prefer explicit operator/object mappings over narrative analogy.

Output Format

# Original Formalism

State the original framing precisely.

# Core Structural Objects

List the key operators, invariants, constraints, and dynamics.

# Target Formalism

State the destination framework.

# Structural Mapping

| Original | Target | Notes |
|---|---|---|
| ... | ... | ... |

# Preserved Structure

- ...

# Lost Structure

- ...

# What Becomes Easier

- ...

# Mismatches / Non-Equivalences

- ...

# New Tools Enabled

- ...

# Verdict

Choose one:
- Deep structural equivalence
- Useful reframing
- Partial analogy
- Mostly notation change

Reason:
...

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