Hugging Face
AICommonly used with
Skills using Hugging Face (145)
geniml
Use Geniml for audited local genomic-interval workflows: validate BED and universe contracts, plan Region2Vec or scEmbed runs, inspect model/tokenizer compatibility, and assess consensus universes.
esm
Use when working directly with the `esm` Python SDK, ESM3 or ESMC model IDs, Forge/Biohub inference clients, or ESMFold2 folding workflows.
hugging-science
Use when the user is doing AI/ML work in a scientific domain such as biology, chemistry, physics, astronomy, climate, genomics, materials, medicine, ecology, energy, engineering, math, drug discovery, protein design, weather modeling, theorem proving, single-cell, or PDE solving. Hugging Science is a curated catalog of scientific datasets, models, blog posts, and interactive Spaces. This skill helps discover and use resources via `datasets`, `transformers`, the HF Inference API, `gradio_client`, and methodology citations.
academic-aio
Medical AI paper optimization for AI search engines (Perplexity, ChatGPT web, Elicit, Consensus, SciSpace) and RAG-based literature tools. Applies when drafting or reviewing titles, abstracts, structured summary boxes (Key Points / Research in Context / Plain-Language Summary), manuscripts for high-impact medical AI journals (Lancet Digital Health, Radiology, Radiology-AI, npj Digital Medicine, Nature Medicine), preprints (medRxiv/arXiv), GitHub README + CITATION.cff + Zenodo archives, and Hugging Face model/dataset cards. Integrates TRIPOD+AI, CLAIM 2024, STARD-AI, TRIPOD-LLM, DECIDE-AI reporting requirements with generative engine optimization (GEO) principles. Produces a visible pass/fail checklist.
model-card
Generate the documentation an engineer-built medical-imaging model must carry — a Model Card (Mitchell et al. 2019), a Datasheet for its dataset (Gebru et al. 2021), and a METRIC-informed data-quality pass — filled from user-supplied facts, then verify every required section is present and non-empty before the card ships to a repo, Hugging Face card, or manuscript supplement. Never fabricates numbers, provenance, consent, or licence; unfilled fields stay flagged. Ships a deterministic completeness gate. Model Card and Datasheet are documentation standards vendored here as templates, not counted reporting checklists.
model-sourcing
Vet the concrete third-party model a study will be built on — this repository, this revision, this checkpoint — not the architecture family. Records a model dossier (source and version pin, licence and the file it was read from, intended use, pretrained-weight provenance, model task vs study task, reported validation, what the model was developed on, your evaluation arms) and gates it deterministically. Catches what a licence check and a citation count cannot: an evaluation arm sitting on the benchmark the model was developed or tuned on, so the arm reads like validation while being closer to a training-set score. Also an evaluation set inside a pretraining corpus, an unstated or use-incompatible licence, an unpinned revision, and a hardware claim never executed. It vets an artifact; it never downloads or runs one.
ideogram-ultra
Build Ideogram 4 (Ideogram Ultra) txt2img and img2img workflows — local open-weights model, dual conditional/unconditional models with DualModelGuider, Qwen3-VL text encoder, and structured JSON ("compositional deconstruction") prompts for strong text rendering and layout control
audiocraft-audio-generation
AudioCraft: MusicGen text-to-music, AudioGen text-to-sound.
guidance
Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework
huggingface-accelerate
Run PyTorch training across GPUs with minimal changes.
huggingface-tokenizers
Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.
outlines
Outlines: structured JSON/regex/Pydantic LLM generation.
llamafactory
Fine-tune LLMs with LlamaFactory — register datasets, train via YAML configs, merge LoRA adapters and serve the result.
ollama
Deploy and serve local models with Ollama — pull and run them, then expose the OpenAI-compatible endpoint to apps and agents.
vllm
Deploy and serve LLMs with vLLM behind an OpenAI-compatible endpoint, with tool calling enabled for agent workloads.
00-andruia-consultant
Arquitecto de Soluciones Principal y Consultor Tecnológico de Andru.ia. Diagnostica y traza la hoja de ruta óptima para proyectos de IA en español.
007
Security audit, hardening, threat modeling (STRIDE/PASTA), Red/Blue Team, OWASP checks, code review, incident response, and infrastructure security for any project.
10-andruia-skill-smith
Ingeniero de Sistemas de Andru.ia. Diseña, redacta y despliega nuevas habilidades (skills) dentro del repositorio siguiendo el Estándar de Diamante.
20-andruia-niche-intelligence
Estratega de Inteligencia de Dominio de Andru.ia. Analiza el nicho específico de un proyecto para inyectar conocimientos, regulaciones y estándares únicos del sector. Actívalo tras definir el nicho.
2slides-ppt-generator
AI-powered presentation generation via the 2slides API — create slides from text, match a reference image style, summarize documents into decks, add AI voice narration, and export pages/audio. Use for any "make slides", "create a deck", or "slides from this document" request.
3d-web-experience
Expert in building 3D experiences for the web - Three.js, React Three Fiber, Spline, WebGL, and interactive 3D scenes. Covers product configurators, 3D portfolios, immersive websites, and bringing depth to web experiences.
ab-test-setup
Structured guide for setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.
ab-testing
When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this,"...
acceptance-orchestrator
Use when a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.
accesslint-audit
Find and fix WCAG 2.2 accessibility issues. Two modes — report (sweep a codebase or page, produce a prioritized written report, no edits) and fix (audit→edit→verify loop on a target). Prefers direct-CDP live-DOM auditing; falls back to a browser-MCP composition or HTML-string audits.
accesslint-diff
Diff a live page's accessibility violations against a baseline — by default compares uncommitted changes (stash-based), or pass --branch [<name>] to diff against a branch. Reports only new violations introduced, violations fixed, and pre-existing count. Use `scan` for a full audit with no diffing.
active-directory-attacks
Provide comprehensive techniques for attacking Microsoft Active Directory environments. Covers reconnaissance, credential harvesting, Kerberos attacks, lateral movement, privilege escalation, and domain dominance for red team operations and penetration testing.
activecampaign-automation
Automate ActiveCampaign tasks via Rube MCP (Composio): manage contacts, tags, list subscriptions, automation enrollment, and tasks. Always search tools first for current schemas.
ad-creative
Create, iterate, and scale paid ad creative for Google Ads, Meta, LinkedIn, TikTok, and similar platforms. Use when generating headlines, descriptions, primary text, or large sets of ad variations for testing and performance optimization.
add-app-clip
Add an iOS App Clip target to an Expo app. Use when the user mentions App Clip, AASA, apple-app-site-association, appclips, smart app banner, or wants to ship a lightweight iOS Clip invoked from a URL alongside their parent app.
adhx
Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or browser required.
advanced-evaluation
This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.
advogado-criminal
Advogado criminalista especializado em Maria da Penha, violencia domestica, feminicidio, direito penal brasileiro, medidas protetivas, inquerito policial e acao penal.
advogado-especialista
Advogado especialista em todas as areas do Direito brasileiro: familia, criminal, trabalhista, tributario, consumidor, imobiliario, empresarial, civil e constitucional.
aegisops-ai
Autonomous DevSecOps & FinOps Guardrails. Orchestrates Gemini 3 Flash to audit Linux Kernel patches, Terraform cost drifts, and K8s compliance.
agent-evaluation
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks
agent-framework-azure-ai-py
Build persistent agents on Azure AI Foundry using the Microsoft Agent Framework Python SDK.
agent-memory
A hybrid memory system that provides persistent, searchable knowledge management for AI agents.
agent-memory-mcp
A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
agent-memory-systems
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them.
model-registry
Curated download URLs and target directories for every model the comfyui-mcp skills reference — checkpoints, VAEs, text encoders, LoRAs — organized by family (Flux, WAN, LTX, Qwen, Z-Image, SD15/SDXL). Use when downloading models with download_model / download_civitai_model, when a workflow fails with a missing-model error, or when setting up a new machine.
detecting-ai-model-prompt-injection-attacks
Detects prompt injection attacks targeting LLM-based applications using a multi-layered defense combining regex pattern matching for known attack signatures, heuristic scoring for structural anomalies, and transformer-based classification with DeBERTa models. The detector analyzes user inputs before they reach the LLM, flagging direct injections (system prompt overrides, role-play escapes, instruction hijacking) and indirect injections (encoded payloads, multi-language obfuscation, delimiter-based escapes). Based on the OWASP LLM Top 10 (LLM01:2025 Prompt Injection) and Simon Willison's prompt injection taxonomy. Activates for requests involving prompt injection detection, LLM input sanitization, AI security scanning, or prompt attack classification.
accessibility-compliance-accessibility-audit
You are an accessibility expert specializing in WCAG compliance, inclusive design, and assistive technology compatibility. Conduct audits, identify barriers, and provide remediation guidance.
accesslint-scan
Audit a live page for accessibility issues, locate each WCAG violation precisely, and return a selector-grounded fix worklist without editing.
accint-commitments
Triage acc's open promises and close them with honest real-world verdicts via acc_act(runtime="outcome").
accint-frames
Drain acc's deliberation queue — open/waiting brain_frames checkpointed by headless runs — via acc_act(runtime="continue").
accint-solve
Route a goal through acc's scored-memory loop via acc_act(runtime="solve"); deliberate any returned brain_frame and submit via continue.
address-github-comments
Use when you need to address review or issue comments on an open GitHub Pull Request using the gh CLI.
agent-manager-skill
Manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
algorithmic-art
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
canvas-design
Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
claude-api
Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST
docx
Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.
frontend-design
Guidance for distinctive, intentional visual design when building new UI or reshaping an existing one. Helps with aesthetic direction, typography, and making choices that don't read as templated defaults.
mcp-builder
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.
pptx
Use this skill any time a .pptx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx file (even if the extracted content will be used elsewhere, like in an email or summary); editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates, layouts, speaker notes, or comments. Trigger whenever the user mentions "deck," "slides," "presentation," or references a .pptx filename, regardless of what they plan to do with the content afterward. If a .pptx file needs to be opened, created, or touched, use this skill.
slack-gif-creator
Knowledge and utilities for creating animated GIFs optimized for Slack. Provides constraints, validation tools, and animation concepts. Use when users request animated GIFs for Slack like "make me a GIF of X doing Y for Slack."
theme-factory
Toolkit for styling artifacts with a theme. These artifacts can be slides, docs, reportings, HTML landing pages, etc. There are 10 pre-set themes with colors/fonts that you can apply to any artifact that has been creating, or can generate a new theme on-the-fly.
web-artifacts-builder
Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for complex artifacts requiring state management, routing, or shadcn/ui components - not for simple single-file HTML/JSX artifacts.
webapp-testing
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
xlsx
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
brand-guidelines
Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.
doc-coauthoring
Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.
internal-comms
A set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever asked to write some sort of internal communications (status reports, leadership updates, 3P updates, company newsletters, FAQs, incident reports, project updates, etc.).
skill-creator
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
transformers-convert
Use this skill when converting custom PyTorch models to Hugging Face Transformers format. Helps with: (1) Creating PretrainedConfig and PreTrainedModel classes, (2) Writing ImageProcessor/Tokenizer, (3) Compatibility testing, (4) Hub upload preparation. Use when the user wants to make their model compatible with transformers library.
forecasting-reverso
Zero-shot univariate time series forecasting using the Reverso foundation model (NumPy/Numba CPU-only inference). Activate when users provide time series data and request forecasts, predictions, or extrapolations. Supports Reverso Small (550K params). Triggers on "forecast", "predict", "time series", "Reverso", or when tabular data with a temporal dimension needs future-value estimation.
evaluating-code-models
Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.
evaluating-llms-harness
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
onescience-infer
OneScience 科学模型推理执行技能。用于用户希望运行或构建 HuggingFace 模型、官方文档模型、本地 checkpoint 或项目原生 runner 的推理工作流时，覆盖气象/地球系统、生信、材料、流体和通用科研模型等领域，包括模型卡与配置发现、输入数据准备、checkpoint 加载、推理入口生成或复用、执行、结果验证、可视化和 baseline 对比。接收格式与 onescience-orchestrator 调用执行技能时的 step_handoff 保持一致，返回 execution_result。需要时协调交接给 onescience-coder、onescience-runtime 以及数据准备类技能。
algo-nlp-ner
"Implement Named Entity Recognition to identify and classify entities in text. Use this skill when the user needs to extract people, organizations, locations, dates, or custom entities from documents — even if they say 'extract names from text', 'find companies mentioned', or 'entity extraction'.".
agent-research-radar
Радар фундаментальных публикаций про автономных AI-агентов в блогах AI-лабораторий и продуктовых команд (Anthropic, Claude, OpenAI, Cursor, LangChain, Amp, Cognition). Собирает ленты и HTML-индексы за окно дат, отсеивает продуктовые анонсы и релизы моделей, оставляет глубокие посты про архитектуру агентов: память и контекст, long-running циклы, self-improvement, оркестрация, экономика прогона, эвалы, риски длинных горизонтов. Отдаёт дайджест с сутью каждого поста и тем, что забрать в свою работу; состояния между прогонами нет: одно окно — один и тот же результат. Используй когда пользователь хочет узнать, что нового вышло в блогах про AI-агентов, собрать дайджест за неделю, отследить исследования про агентную память. Триггерится на: "радар", "дайджест", "что нового в блогах", "публикации про агентов", "agent research", "что вышло за неделю", "блог Anthropic", "cursor blog", "langchain blog", "long-running agents", "research digest". НЕ для: корпоративных вики и трекеров; arXiv; доставки сообщений.
voice-to-text-config
Set up Telegram voice message transcription — checks faster-whisper installation, downloads the Whisper model, and verifies the hook works end-to-end. Use when the user asks to "set up voice transcription", "configure whisper", "fix voice messages", or when the transcription hook reports that whisper is missing or the model isn't downloaded.
local-rag-builder
本地 RAG 系统搭建技能，支持环境检测修复、嵌入模型多源下载、5种切分策略 + GuardStack + 后处理 + 插件注册、多知识库管理 + 自动分类规则、可调 Prompt、Web 可视化配置 + 极客模式 + 模板管理
kronos-agent
Financial time-series forecasting using the Kronos foundation model (MIT, NeoQuasar). Takes OHLC candles, returns predicted future candles with configurable horizon. Infrastructure skill — called by trader agents or scheduled ingestion, not directly invoked by users or the model.
jinja-expert
Author, read, and debug Jinja2 templates across the three places Jinja lives in 2026 — HuggingFace `chat_template.jinja` (rendered by `apply_chat_template` for vLLM / sglang), Ansible playbooks + `.j2` files, and Jinja-adjacent Kubernetes workflows (`values.yaml.j2`, `kubernetes.core.k8s + template`, Helm post-renderers). Companion to the `helm` skill — Helm charts are Go `text/template` + Sprig, not Jinja, and this skill makes that disambiguation explicit.
open-webui-embeddings
Wire HuggingFace embedding + reranker models (BGE-M3, BGE-Reranker-v2-m3, etc.) into Open WebUI's RAG pipeline via LiteLLM proxying HuggingFace Text Embeddings Inference (TEI). Covers the exact wire shapes Open WebUI sends (URL auto-append on embed but NOT rerank; payload + response shapes for both modes), the LiteLLM-TEI gotchas (encoding_format=null trap, HF-driver task_type misdetection, openai vs huggingface driver tradeoffs), TEI config cliffs (max-client-batch-size 422 under hybrid search, max-batch-tokens AS the auto-truncate boundary, arch-specific Docker images), and the end-to-end production config. BGE-M3 + BGE-Reranker-v2-m3 are worked examples; patterns generalise to any TEI encoder.
sglang-model-gateway
SGLang Model Gateway (`sgl-model-gateway`, formerly `sgl-router`) — Rust router fronting vLLM and SGLang inference workers on Kubernetes. Covers first-class vLLM gRPC backend plus HTTP transparent-proxy for vanilla vLLM, the policy set (six `--policy` values, `cache_aware` default), tokenizer-format dispatch (`tokenizer.json` HF-fast vs `tiktoken.model` BPE — including when neither is required because `cache_aware` is text-based), air-gapped recipe (gateway ignores `HF_ENDPOINT`, mount tokenizer files on PVC only when actually needed), K8s manifests with `model_id` labels and per-model RBAC, three HA mitigations (single + PDB, `sessionAffinity: ClientIP`, `--enable-mesh` CRDT sync), and a pitfall catalog covering the Dec 2025 `sgl-router` → `sgl-model-gateway` rename and over-engineered tokenizer init-container traps.
transformers-config-tokenizers-expert
Preflight reference for HuggingFace snapshots — what vLLM, sglang, and transformers.generate see at runtime. Covers config-file precedence (tokenizer.json, tokenizer_config.json, generation_config.json, chat_template.jinja), transformers v5 tokenizer-class taxonomy (TokenizersBackend, PythonBackend, MistralCommonBackend, TikTokenTokenizer), special-token discovery (all_special_ids, added_tokens_decoder, extra_special_tokens, backend_tokenizer.get_added_tokens_decoder), chat-template Jinja contract (ImmutableSandboxedEnvironment, loopcontrols, raise_exception, strftime_now, tojson, add_generation_prompt), and engine knobs (skip_special_tokens, trust_request_chat_template, chat_template_kwargs allowlist, adjust_request, incremental detokenizer, EOS merge). Ships verified 2026 hall-of-shame for Kimi-K2.6, GLM-5.1, Gemma-4, Qwen3, DeepSeek-V3, plus drop-in Python for resolving markers to IDs, detecting turn-primer-as-EOS leaks, and cross-referencing tokenizer.json vs tokenizer_config.json.
vllm-configuration
Configure vLLM completely — YAML config file format, CLI arg precedence, full VLLM_*/HF_*/TRANSFORMERS_* env-var catalog, end-to-end recipe for air-gapped environments (internal HF mirrors, hf-mirror.com, ModelScope, HF_HUB_OFFLINE with pre-seeded cache, gated models offline, trust_remote_code supply-chain implications). VLLM_HOST_IP vs API-host confusion, Kubernetes-service-named-`vllm` env-var poisoning, usage-stats triple opt-out, YAML precedence surprises.
hugging-face-papers
Read and analyze Hugging Face paper pages or arXiv papers with markdown and papers API metadata.
hugging-face-jobs
This skill should be used when users want to run any workload on Hugging Face Jobs infrastructure. Covers UV scripts, Docker-based jobs, hardware selection, cost estimation, authentication with tokens, secrets management, timeout configuration, and result persistence. Designed for general-purpose compute workloads including data processing, inference, experiments, batch jobs, and any Python-based tasks. Should be invoked for tasks involving cloud compute, GPU workloads, or when users mention running jobs on Hugging Face infrastructure without local setup.
hugging-face-model-trainer
This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Should be invoked for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
package-release-sniffer
Load when tracking newly published package or model-package releases across package registries and release feeds for AI/developer-tool monitoring; do not load for ordinary docs lookup, broad GitHub trend scanning, or implementing package clients.
ai-infrastructure-huggingface-inference
Hugging Face Inference SDK patterns for TypeScript/Node.js — InferenceClient setup, chat completion, text generation, streaming, embeddings, image generation, audio transcription, translation, summarization, and Inference Endpoints
aimhooman
Keep AI tooling artifacts out of Git. Use whenever staging, committing, or pushing changes: block AI session/state files (.claude/session.json, .codex/history, .copilot, .cursor/session, .aider.*, .specstory, .agent) and unwanted AI attribution in commit messages (Co-authored-by an AI, "Generated with AI"). Also use when the user says "aimhooman", "ship it like a hooman", or asks to clean AI residue before a commit.
paper
Write submission-grade research papers end to end inside a Rockie lab, the way a careful human researcher does — not generic LLM filler. Three entry points. /lit-review pulls and ranks a corpus and persists a human reading list Note plus a machine-readable index Note. /paper-draft produces a brief, a page-budgeted outline, per-section drafts, an adversarial review gauntlet (attack, defense, rebuttal, style, format), and a final AI-vs-human detector gate. /publish assembles a downloadable bundle, lands it as a lab Note, and optionally exports to GitHub or Hugging Face. Triggers on "write a paper", "lit review", "literature review", "draft the paper", "review my paper", "run the gauntlet on this draft", "publish the paper", "submit to <venue>", "/lit-review", "/paper-draft", "/publish".
ml-security
Model artifact loading (pickle vs safetensors), model & data poisoning, PII in training data, secrets in notebooks, model provenance / lineage — Applies to: when generating code that loads ML models from disk / Hub / S3; when generating data pipelines that ingest user content for training / fine-tuning; when generating ML notebooks or training / evaluation scripts
keel-research
Opt-in external research — scan GitHub/articles/LinkedIn/HuggingFace/web and save cited findings per platform under research/.
hf-mcp
Use Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF MCP server.
hugging-face-cli
Execute Hugging Face Hub operations using the `hf` CLI. Use when the user needs to download models/datasets/spaces, upload files to Hub repositories, create repos, manage local cache, or run compute jobs on HF infrastructure. Covers authentication, file transfers, repository creation, cache operations, and cloud compute.
hugging-face-dataset-viewer
Use this skill for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet URLs, and read size or statistics.
hugging-face-datasets
Create and manage datasets on Hugging Face Hub. Supports initializing repos, defining configs/system prompts, streaming row updates, and SQL-based dataset querying/transformation. Designed to work alongside HF MCP server for comprehensive dataset workflows.
hugging-face-evaluation
Add and manage evaluation results in Hugging Face model cards. Supports extracting eval tables from README content, importing scores from Artificial Analysis API, and running custom model evaluations with vLLM/lighteval. Works with the model-index metadata format.
hugging-face-paper-publisher
Publish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles.
hugging-face-tool-builder
Use this skill when the user wants to build tool/scripts or achieve a task where using data from the Hugging Face API would help. This is especially useful when chaining or combining API calls or the task will be repeated/automated. This Skill creates a reusable script to fetch, enrich or process data.
hugging-face-trackio
Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.
disk-space-cleaner
Reclaim disk space by finding and deleting regenerable build/dependency directories (node_modules, target, .next, dist, build, .gradle, .turbo) and optionally running `cargo clean` and Docker prune. Dry-run by default, age-gated, and protects git-locked worktrees plus recently-touched work so active sessions are never clobbered. Use when disk is low, when `df` shows the volume near full, or when the user says "clean disk", "free up space", "disk-space-cleaner", or "what's eating my disk".
nlp-pretraining
Best practices for language model pretraining and fine-tuning. Use when generating or reviewing NLP training code.
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