📦

mloda

Name: mloda
Author: mloda-ai

Revisão do conteúdo r1 Risco Médio ⚙️ Comandos externos🌐 Acesso à rede

Build Declarative Data Pipelines with mloda

AI agents often generate fragile data-fetching and transformation code. This skill uses mloda feature requests, plugins, and lineage to build deterministic data workflows.

Suporta: Claude Codex Code(CC)

⚠️ 50 Ruim

Instalar com meu Agente

Copie esta solicitação para o seu Agente. Ela inclui a página canônica do Skill e o manifesto.

Solicitação do agente

Review the Skillstore skill "mloda" from https://skillstore.io/skills/mloda-ai-mloda.md and its manifest at https://skillstore.io/api/skills/mloda-ai-mloda/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Seu Agente ainda deve apresentar o plano e solicitar qualquer confirmação exigida pela política de segurança.

Recursos legíveis por agentes

Use estes links quando um agente de IA, crawler ou script precisar de contexto limpo em vez de ler a página completa.

Detalhe em Markdown GET /skills/mloda-ai-mloda.md manifest assinado GET /api/skills/mloda-ai-mloda/manifest lockfile assinado GET /api/skills/mloda-ai-mloda/lockfile

Testar

Usando "mloda". Check whether installed plugins can calculate ranked revenue by region.

Resultado esperado:

Registry check: a ranking FeatureGroup is available for Pandas. Review and pin the required community package before use.

Usando "mloda". Plan a retrieval, injection check, and PII redaction workflow.

Resultado esperado:

The workflow needs three FeatureGroups in dependency order. Validate each plugin and authorize the final request before execution.

Usando "mloda". Reduce latency for repeated customer feature requests.

Resultado esperado:

Prepare one reusable execution session, supply request data per run, and measure planning and execution latency separately.

Auditoria de segurança

Risco Médio

v2 • 01/09/2026 Abrir o relatório versionado

Most static alerts are false positives caused by Markdown backticks, fences, identifiers, and documentation URLs. Unpinned package installs and direct execution of model-generated feature requests create real supply-chain and authorization risks. No prompt injection or covert exfiltration behavior was found.

Arquivos analisados

130

Linhas analisadas

Itens de revisão

Falsos positivos ignorados

Preocupações de segurança confirmadas (2)

Médio

SKILL.md:74-94

Unvalidated Model-Generated Feature Execution

The example converts model-produced JSON directly into features and executes them without an allowlist, option validation, authorization, or data-access policy.

The documented flow directly passes parsed model output to mloda.run_all. The example shows no validation boundary before installed plugins receive the request.

Médio

SKILL.md:128

Additional Unpinned Package Installation

The reference section repeats an unpinned pip installation command without version, hash, trusted-index, or confirmation guidance.

The line explicitly instructs installation of the latest resolved package. Package installation can execute third-party code and produce non-reproducible environments.

Itens de revisão de capacidade (2)

Estas são capacidades locais reais que podem ser esperadas para esta habilidade, portanto exigem revisão, mas não são contabilizadas como comportamento malicioso confirmado.

Médio

SKILL.md:21

Ruby/shell backtick execution

plain `pip install mloda`; install what you need, e.g. `pip install mloda-community` (all) or

The line explicitly recommends unpinned pip installation commands. Following them downloads and executes third-party package installation code without version or integrity controls.

Médio

SKILL.md:22

Ruby/shell backtick execution

`pip install "mloda-community-rank[pandas]"` (one). See its

The line provides an unpinned pip command with optional package extras. Installation can execute third-party code and resolve changing dependencies without integrity verification.

Fatores de risco

Auditado por: codex Ver histórico de auditoria →

Compartilhar e citar este relatório

Compartilhe o relatório de avaliação versionado, o badge neutro, o cartão incorporável e as citações. A Skillstore apresenta evidências sem decidir se este Skill é seguro.

Abrir o relatório versionado

Copiar link do relatório

https://skillstore.io/skills/mloda-ai-mloda/audits/2?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

Badge Markdown

[![Skillstore security assessment](https://skillstore.io/badges/skills/mloda-ai-mloda/security.svg)](https://skillstore.io/skills/mloda-ai-mloda?utm_source=security_passport_badge)

Badge HTML

<a href="https://skillstore.io/skills/mloda-ai-mloda?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/mloda-ai-mloda/security.svg" alt="Skillstore security assessment" loading="lazy"></a>

Cartão incorporável

<iframe src="https://skillstore.io/embed/skills/mloda-ai-mloda.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>

Citações acadêmicas (APA · BibTeX · CFF)

Citação APA

mloda-ai. (2026). mloda security audit report (audit version 2) [Author version unspecified]. Skillstore. https://skillstore.io/skills/mloda-ai-mloda/audits/2

Citação BibTeX

@techreport{mloda-ai-mloda-ai-mloda-2026,
  author = {mloda-ai},
  title = {mloda security audit report (audit version 2)},
  institution = {Skillstore},
  year = {2026},
  number = {2},
  url = {https://skillstore.io/skills/mloda-ai-mloda/audits/2},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "mloda security audit report (audit version 2)"
version: "unspecified"
type: report
authors:
  - name: "mloda-ai"
date-released: "2026-09-01"
url: "https://skillstore.io/skills/mloda-ai-mloda/audits/2"
identifiers:
  - type: other
    value: "skillstore:mloda-ai-mloda:audit:2"
    description: "Skillstore immutable audit report identifier"

Pontuação do Skillstore

Por que esta pontuação Confiança das evidências: Médio

Arquitetura

Manutenibilidade

Conteúdo

Comunidade

Conformidade com a especificação

O que você pode criar

Reuse Existing Transformations

Inspect installed plugins before implementing aggregation, ranking, time-series, or string transformations.

Build Agent Data Tools

Translate constrained model requests into deterministic feature computations with source lineage.

Optimize Repeated Workloads

Prepare execution plans once and reuse them for serving, streaming, or dashboard requests.

Teste estes prompts

Inspect Available Plugins

Inspect the installed mloda plugins for [feature need]. List matching FeatureGroups, required packages, supported frameworks, and missing capabilities before proposing code.

Plan a Feature Request

Create a mloda request for [features] using [input source]. Use [framework], explain dependencies, and include lineage checks.

Design a RAG Chain

Design a feature-name chain for [RAG workflow]. Map each stage to an existing plugin, identify unavailable stages, and define validation boundaries.

Harden a Reusable Session

Design a reusable mloda session for [workload]. Add a strict feature allowlist, option validation, data-access boundaries, lineage verification, and latency measurements.

Boas práticas

Inspect installed plugin documentation before writing custom transformations.
Pin and review packages before installation in production.
Validate model-generated feature names and options against an explicit allowlist.

Evitar

Do not assume registry documentation matches the current environment.
Do not execute unrestricted model-generated requests against sensitive data.
Do not rebuild execution plans for every repeated request when prepare can reuse them.

Perguntas frequentes

What is mloda?

mloda is a Python plugin framework that resolves named feature requests into executable data workflows with lineage.

Does this skill install packages automatically?

No. It provides installation guidance, but package selection and installation require review in your environment.

Can it create a missing plugin?

No. This skill identifies missing coverage and directs plugin creation to separate mloda-plugins guidance.

Which compute frameworks are supported?

Examples include PandasDataFrame, PyArrowTable, and PythonDictFramework. Actual availability depends on installed plugins.

How does lineage work?

mloda records dependencies from requested features through their source data and transformation chain.

Is model-generated configuration safe to execute directly?

No. Validate feature names and options, restrict data access, and authorize requests before execution.

Detalhes do desenvolvedor

Autor

mloda-ai

Licença

MIT

Revisão da Skillstore

Aviso de versão

O autor não declarou uma versão.

Repositório

https://github.com/mloda-ai/mloda-skills/tree/1cf29f075e7828973a6f86c6238299588c37c5ab/skills/mloda

Referência

0acc95b40c367e2016e824a72b27b83b507ef020

Atualidade da manutenção

08/09/2026

Uso

1 downloads · 0 visualizações

Estrutura de arquivos

📄 SKILL.md