Hi Glauco,
Please try again with new clones, I have updated some scripts.

Verifying each single proof will generate the *.mm file in the target
folder.

Verifying the whole project will generate *.mm file and source map files in
the target folder also.

```
uv run --frozen skfd verify proof-lab
```

Best,

Mingli


On Thu, Feb 12, 2026 at 5:46 AM Glauco <[email protected]> wrote:

> Hi Mingli,
>
> I've tried it in Play-With-Docker, here is the script that worked for me
> (from a clean new PWD instance):
>
> curl -LsSf https://astral.sh/uv/install.sh | sh
> source $HOME/.local/bin/env
> git clone https://github.com/epistemic-frontier/metamath-logic.git
> git clone https://github.com/epistemic-frontier/metamath-prelude.git
> git clone https://github.com/epistemic-frontier/proof-lab.git
> cd proof-lab
> uv run skfd verify prove_modus_tollens.py
>
>
> here's the output
>
> [node1] (local) [email protected] ~/proof-lab
> $ uv run --frozen skfd verify prove_modus_tollens.py
> Verifying script: prove_modus_tollens.py ...
> Found 1 proof(s): prove_modus_tollens
> Running prove_modus_tollens... OK (modus_tollens)
> Emitting 1 proofs to Metamath... OK
>
> Is the proof stored somewhere?
>
> Glauco
>
>
> Il giorno mercoledì 11 febbraio 2026 alle 21:32:18 UTC+1
> [email protected] ha scritto:
>
>> Hi Glauco,
>>
>> I saw your email and felt very inspired. So I worked late this night to
>> make the code ready.
>>
>> Now I have published metamath-prelude and metamath-logic. They are still
>> "alpha" versions and not perfect, but I think they are usable now.
>>
>> *1. Proof Lab* I made a new repo called *Proof Lab* here:
>> https://github.com/epistemic-frontier/proof-lab
>>
>> You can clone it and just follow the README. It can verify the Python
>> proofs (like your mp2 example) using the real Metamath verifier.
>>
>> *2. Original Authorship* Also, I want to say one thing. Even though I
>> use a Python interface, I try my best to keep the original author's
>> comments in the source code. For example, please check this file:
>> https://github.com/epistemic-frontier/metamath-logic/blob/main/src/logic/propositional/hilbert/lemmas.py#L222
>>
>> You can see the note from *NM (Norman Megill) on 30-Sep-1992* is kept
>> there. I think attribution is very important.
>>
>> Please try the lab. Let me know if you have any problems.
>>
>> Best,
>>
>> Mingli
>>
>> On Wed, Feb 11, 2026 at 10:33 PM Mingli Yuan <[email protected]> wrote:
>>
>>> Hi Glauco,
>>>
>>> Thanks for checking.
>>>
>>> Logically, the structure is correct: from φ and (φ → (ψ → χ)) we get (ψ
>>> → χ) by MP, then from ψ we get χ by MP again. So the *proof skeleton*
>>> is right.
>>>
>>> However, my porting work is not finished yet, the
>>> logic.propositional.hilbert and related packages are not released yet,
>>> so this code can not be verified by the community right now. There are
>>> around 20k lines of proofs in the logic part of metamath, please give me
>>> some time to finish it.
>>>
>>> Or, I may release the prelude and logic package earlier before it is
>>> fully ported, if anyone is interested.
>>>
>>> Best,
>>>
>>> Mingli
>>>
>>> On Wed, Feb 11, 2026 at 9:07 PM Glauco <[email protected]> wrote:
>>>
>>>> Hi Mingli Yuan,
>>>>
>>>> I've given Gemini your README.md and asked it to prove mp2 with your
>>>> framework. Below is the Python code that Gemini returned.
>>>> Is it completely hallucinated, or is it close?
>>>>
>>>> from logic.propositional.hilbert import System
>>>> from logic.propositional.hilbert.lemmas import Proof, ProofBuilder
>>>>
>>>> def prove_mp2(sys: System) -> Proof:
>>>>     """
>>>>     Double Modus Ponens: φ, ψ, (φ → (ψ → χ)) ⊢ χ
>>>>     """
>>>>     # 1. Initialize the builder with the system and theorem name
>>>>     lb = ProofBuilder(sys, "mp2")
>>>>
>>>>     # 2. Define Hypotheses (mp2.1, mp2.2, mp2.3)
>>>>     h_phi = lb.hyp("mp2.1", "φ")
>>>>     h_psi = lb.hyp("mp2.2", "ψ")
>>>>     h_chi_nested = lb.hyp("mp2.3", "φ → (ψ → χ)")
>>>>
>>>>     # 3. Apply the first Modus Ponens (Step 4 in .mmp)
>>>>     # This uses h_phi and h_chi_nested to get (ψ → χ)
>>>>     step4 = lb.mp("s1", h_phi, h_chi_nested, note="Step 4: MP h_phi,
>>>> h_chi_nested")
>>>>
>>>>     # 4. Apply the second Modus Ponens (Step 5 in .mmp)
>>>>     # This uses h_psi and step4 to get χ
>>>>     step5 = lb.mp("s2", h_psi, step4, note="Step 5: MP h_psi, s1")
>>>>
>>>>     # 5. Return the build targeting the final result
>>>>     return lb.build(step5)
>>>>
>>>> BR
>>>> Glauco
>>>>
>>>> Il giorno mercoledì 11 febbraio 2026 alle 08:05:05 UTC+1
>>>> [email protected] ha scritto:
>>>>
>>>>> Dear Metamath Community,
>>>>>
>>>>> I am writing to share an open-source project we have been working on
>>>>> called ProofScaffold (
>>>>> https://github.com/epistemic-frontier/proof-scaffold).
>>>>>
>>>>> Our team has deep respect for set.mm and the rigorous foundation this
>>>>> community has built. However, as we explore the intersection of Metamath
>>>>> and Large Language Models (LLMs), we’ve encountered a persistent 
>>>>> challenge:
>>>>> feeding a 48MB monolithic file to an AI often leads to context dilution,
>>>>> hallucinated imports, and noisy proof searches.
>>>>>
>>>>> To solve this, we built ProofScaffold. It acts as a package manager
>>>>> and linker (written in Python) for Metamath. It allows us to split massive
>>>>> databases into composable, compilable, and independently verifiable
>>>>> packages with explicit dependencies and exports—much like Cargo or NPM, 
>>>>> but
>>>>> for formal math.
>>>>>
>>>>> Crucially, the Trust Computing Base (TCB) does not change. Python acts
>>>>> strictly as an untrusted builder/linker. The final output is a standard,
>>>>> flattened .mm transient monolith that is verified by metamath-exe or
>>>>> metamath-knife.
>>>>>
>>>>> We believe this modularity is the key to unlocking AI's true potential
>>>>> in formal mathematics:
>>>>>
>>>>> - Targeted Retrieval: By scoping the context to a specific package
>>>>> (e.g., just fol or zf), we drastically reduce noise for the LLM.
>>>>>
>>>>> - Controlled Semantic Boundaries: Explicit exports provide the AI with
>>>>> a strict subset of permissible symbols and axioms. This prevents
>>>>> hallucinated reasoning, such as an AI accidentally employing the Axiom of
>>>>> Choice in a strict ZF-only context.
>>>>>
>>>>> - Verifiable Incremental Loops: An AI can generate a proof, verify it
>>>>> locally within the package, and map any verifier errors directly back to
>>>>> the specific package contract (e.g., missing $f or label conflicts). This
>>>>> makes AI self-correction much more reliable.
>>>>>
>>>>> - Curriculum Alignment: Modular packages naturally form a curriculum
>>>>> (axioms → definitions → lemmas → theorems), providing high-quality 
>>>>> gradient
>>>>> data for training models, rather than overwhelming them with the entire
>>>>> set.mm at once.
>>>>>
>>>>> We have successfully ported a prelude and are currently porting the
>>>>> logic package (aligned with the |- conventions of set.mm). Our next
>>>>> step is to further subdivide logic into prop, fol, eq, and setvar 
>>>>> packages,
>>>>> and to generate machine-readable interface manifests to help AI planners.
>>>>>
>>>>> A Question for the Community regarding PyPI Naming:
>>>>>
>>>>> To make this ecosystem easily accessible for AI researchers and
>>>>> engineers, we plan to publish these modularized databases as Python
>>>>> packages on PyPI.
>>>>>
>>>>> I would like to ask if the community is comfortable with us using the
>>>>> metamath- prefix for these packages (e.g., metamath-logic, metamath-zfc). 
>>>>> I
>>>>> want to be entirely respectful of the Metamath trademark/legacy and ensure
>>>>> this doesn't cause confusion with official tools. If the community prefers
>>>>> we use a different namespace (e.g., proof-scaffold-logic), we will gladly
>>>>> do so.
>>>>>
>>>>> I would love to hear your thoughts, feedback, or critiques on this
>>>>> approach.
>>>>>
>>>>> Best regards,
>>>>>
>>>>> Mingli Yuan
>>>>>
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>>>>
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