Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ArabelaTso/Skills-4-SE --skill program-to-model-extractorgit clone --depth 1 https://github.com/ArabelaTso/Skills-4-SEWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/arabelatso/skills-4-se/program-to-model-extractor)<a href="https://agentmods.dev/skills/arabelatso/skills-4-se/program-to-model-extractor"><img src="https://agentmods.dev/badge/skills/arabelatso/skills-4-se/program-to-model-extractor/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/arabelatso/skills-4-se/program-to-model-extractor"><img src="https://agentmods.dev/badge/skills/arabelatso/skills-4-se/program-to-model-extractor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00107 | $0.01503 |
| Opus 5 | $0.00053 | $0.00751 |
| Sonnet 5 | $0.00021 | $0.00301 |
| Haiku 4.5 | $0.00011 | $0.00150 |
Grade A, and why
program-to-model-extractor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Program-to-Model Extractor
Extract high-level mathematical models from functional code for formal reasoning in Isabelle/HOL.
Overview
This skill transforms functional programs (Haskell, OCaml, F#) into abstract mathematical models suitable for formal verification in Isabelle/HOL. The extraction focuses on the algorithm's mathematical essence—capturing core properties, invariants, and structural patterns while abstracting away language-specific implementation details.
Extraction Workflow
1. Analyze the Source Code
Identify key elements:
- Data structures: Algebraic types, lists, trees, custom types
- Core functions: Main computational logic
- Recursion patterns: Structural, tail, mutual recursion
- Properties: What should be true about inputs/outputs?
2. Extract Data Types
Convert source language types to Isabelle datatypes:
-- Haskell
data Tree a = Leaf | Node a (Tree a) (Tree a)
(* Isabelle *)
datatype 'a tree = Leaf | Node "'a" "'a tree" "'a tree"
3. Model Functions
Choose the appropriate Isabelle construct:
For primitive recursion (terminates obviously):
fun length :: "'a list ⇒ nat" where
"length [] = 0" |
"length (x # xs) = 1 + length xs"
For general recursion (needs termination proof):
function gcd :: "nat ⇒ nat ⇒ nat" where
"gcd m n = (if n = 0 then m else gcd n (m mod n))"
by pat_completeness auto
termination by (relation "measure snd") auto
For non-recursive definitions:
definition compose :: "('b ⇒ 'c) ⇒ ('a ⇒ 'b) ⇒ ('a ⇒ 'c)" where
"compose f g = (λx. f (g x))"
4. State Properties
Extract and formalize key properties as lemmas:
lemma length_append: "length (xs @ ys) = length xs + length ys"
lemma quicksort_permutes: "mset (quicksort xs) = mset xs"
lemma quicksort_sorted: "sorted (quicksort xs)"
5. Identify Invariants
For stateful or accumulator-based functions, state what holds during computation:
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 185 lines · 107 tokens per session scan A 6fac8170106f
program-to-model-extractor is a skill published in the GitHub repository ArabelaTso/Skills-4-SE (252 stars, last pushed 22d ago), licensed Apache-2.0. It adds 107 tokens to every session and 1,503 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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