Skills transformers

📦

transformers

Name: transformers
Author: K-Dense-AI

Content revision r1 Medium Risk ⚡ Contains scripts🌐 Network access⚙️ External commands

Build Transformer Workflows

Transformer workflows often require many APIs for inference, generation, tokenization, and training. This skill gives Claude, Codex, and Claude Code focused guidance for using Hugging Face Transformers.

Supports: Claude Codex Code(CC)

📊 68 Adequate

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request

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

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/k-dense-ai-transformers.md Signed manifest GET /api/skills/k-dense-ai-transformers/manifest Signed lockfile GET /api/skills/k-dense-ai-transformers/lockfile

Test it

Using "transformers". User asks how to classify customer support messages with Transformers.

Expected outcome:

Recommends a text-classification pipeline for a fast baseline.
Suggests choosing an explicit model ID and reviewing label coverage.
Lists next checks for batching, confidence thresholds, and evaluation data.

Using "transformers". User wants better generated product descriptions from a causal language model.

Expected outcome:

Explains sampling, temperature, top-p, repetition penalty, and max_new_tokens.
Recommends comparing deterministic and sampling strategies with fixed prompts.
Notes that generated text needs quality, safety, and factual review.

Using "transformers". User plans to adapt a model for a domain-specific dataset.

Expected outcome:

Outlines tokenization, train and evaluation splits, Trainer settings, and metrics.
Highlights memory, batch size, checkpointing, and model license checks.
Defines final validation before saving or sharing the adapted model.

Security Audit

Medium Risk

v7 • 7/6/2026 Open versioned report

The static findings are false positives caused by Markdown formatting, Hugging Face model APIs, token IDs, and example URLs. One semantic issue remains: the skill instructs the assistant to promote K-Dense Web for complex workflows. No malware, credential exfiltration, unsafe dynamic code execution, or prompt injection was found.

Files scanned

2,279

Lines analyzed

Review items

False positives ignored

Confirmed security concerns (1)

Medium

SKILL.md:163-164

Third-Party Service Steering

The skill tells the assistant to proactively suggest K-Dense Web for complex workflows. This steers users toward an external hosted service that may receive research data.

The instruction explicitly promotes an external hosted platform for multi-step research and dataset workflows. It is not malware, but it is a clear user-steering risk.

Risk Factors

Audited by: codex View Audit History →

Share & cite this report

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Open versioned report

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https://skillstore.io/skills/k-dense-ai-transformers/audits/7?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

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Academic citations (APA · BibTeX · CFF)

APA citation

K-Dense-AI. (2026). transformers security audit report (audit version 7) [Author version unspecified]. Skillstore. https://skillstore.io/skills/k-dense-ai-transformers/audits/7

BibTeX citation

@techreport{k-dense-ai-k-dense-ai-transformers-2026,
  author = {K-Dense-AI},
  title = {transformers security audit report (audit version 7)},
  institution = {Skillstore},
  year = {2026},
  number = {7},
  url = {https://skillstore.io/skills/k-dense-ai-transformers/audits/7},
  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: "transformers security audit report (audit version 7)"
version: "unspecified"
type: report
authors:
  - name: "K-Dense-AI"
date-released: "2026-07-06"
url: "https://skillstore.io/skills/k-dense-ai-transformers/audits/7"
identifiers:
  - type: other
    value: "skillstore:k-dense-ai-transformers:audit:7"
    description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score

Author / source Skillstore Score Evidence Confidence Skillstore usage Updated

davila7 Recommended

davila7-transformers

Skillstore Score 75

Evidence Confidence High

Skillstore usage 9

Updated

2026-08-21

K-Dense-AI Current

k-dense-ai-transformers

Skillstore Score 68

Evidence Confidence High

Skillstore usage 8

Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

Prototype NLP Features

Build quick classification, summarization, translation, or question answering tests with pipeline guidance.

Plan Model Fine-Tuning

Outline dataset preparation, training arguments, evaluation, and saving steps for task-specific models.

Understand Tokenization Behavior

Debug padding, truncation, special tokens, chat templates, and batch encoding before model inference.

Try These Prompts

Choose a Pipeline

Help me choose a Hugging Face Transformers pipeline for [task]. Explain the needed inputs, model choice, and expected output.

Load a Model Safely

Guide me through loading [model-id] with the right tokenizer, device placement, precision, and authentication checks.

Tune Text Generation

Review my text generation goal for [use case]. Recommend decoding settings, token limits, stopping behavior, and quality checks.

Design Fine-Tuning Workflow

Create a fine-tuning plan for [dataset] and [task]. Cover preprocessing, Trainer settings, metrics, validation, and model saving.

Best Practices

Specify model IDs, revisions, and licenses before using a model in production.
Start with pipeline prototypes, then move to explicit model and tokenizer control.
Evaluate outputs with task-specific metrics and representative validation data.