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 cxcscmu/SkillLearnBench --skill python-scala-functionalgit clone --depth 1 https://github.com/cxcscmu/SkillLearnBenchWrote 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/cxcscmu/skilllearnbench/python-scala-functional)<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/python-scala-functional"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/python-scala-functional.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00027 | $0.00301 |
| Opus 5 | $0.00014 | $0.00151 |
| Sonnet 5 | $0.00005 | $0.00060 |
| Haiku 4.5 | $0.00003 | $0.00030 |
Grade A, and why
python-scala-functional 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 3d 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.
What it actually says
Python to Scala Functional Patterns
Functor / Monad Simulation -> Proper Scala Types
// Python simulates functors; Scala has them natively via map/flatMap
class TokenFunctor[A](val get: A) {
def map[B](f: A => B): TokenFunctor[B] = new TokenFunctor(f(get))
def flatMap[B](f: A => TokenFunctor[B]): TokenFunctor[B] = f(get)
def getOrElse(default: => A): A = if (get != null) get else default
}
Option Handling
// Python: return None -> Scala: Option[T]
// Python: if x is None -> Scala: x match { case None => ... case Some(v) => ... }
// Python: x or default -> Scala: x.getOrElse(default)
Higher-Order Functions
// Python: Callable[[T], Token] -> Scala: T => Token
// Python: Callable[[T], Token | None] -> Scala: T => Option[Token]
Lazy Evaluation
// Python: yield (generator) -> Scala: Iterator via .iterator.map
def tokenizeBatch(values: Iterable[T]): Iterator[Token] =
values.iterator.map(tokenize)
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.
- 3d ago First seen · 37 lines · 27 tokens per session scan A b072163e2ff3
python-scala-functional is a skill published in the GitHub repository cxcscmu/SkillLearnBench (83 stars, last pushed 1mo ago), licensed MIT. It adds 27 tokens to every session and 301 once invoked, about $0.0001 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.
Other skills, from other repositories
hotpath_init
Configure hotpath profiling in a Rust project. Adds the hotpath dependency with feature-gated setup, instruments main with hotpath::main, functions with measure/measureall, and wraps channels, mutexes, rwlocks, streams, futures, reqwest clients, axum routers and byte-level I/O with hotpath macros. Use when the user…
rdkit
Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom…
maven-plugin-configuration
Use when configuring Maven plugins, setting up common plugins like compiler, surefire, jar, or creating custom plugin executions.
pcap-analysis
Guidance for analyzing network packet captures (PCAP files) and computing network statistics using Python, with tested utility functions.
erlang-otp-behaviors
Use when oTP behaviors including genserver for stateful processes, genstatem for state machines, supervisors for fault tolerance, genevent for event handling, and building robust, production-ready Erlang applications with proven patterns.
maven-build-lifecycle
Use when working with Maven build phases, goals, profiles, or customizing the build process for Java projects.