Reads one export transaction, order or shipment description and returns a DRAFT export review pack: parties to screen with a screening checklist, an item classification worksheet, a red-flag review, licence-determination questions and open items. Use when the user asks to "prepare the export review for this order", "pre-check this shipment for export control", "who do we need to screen on this deal", "build the classification worksheet for this item" or "assemble the trade-compliance file for this technology transfer". Do not use for vendor security or due-diligence screening of a supplier's questionnaire answers; use vendor-risk-screening-brief instead. Never classifies, screens, clears, decides licence need or releases a shipment; trade compliance does. Drafts for human review; never approves, authorises or signs off.
中文简介正在准备中。当前显示原文说明。
kesslernity/awesome-copilot-agent-skills☆ 92026年9月19日 更新
Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker, pair scoring for two-stage retrieval / pair classification), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.
中文简介正在准备中。当前显示原文说明。
bg-szy/TOP-SKILLS☆ 62026年9月8日 更新
Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.
中文简介正在准备中。当前显示原文说明。
bg-szy/TOP-SKILLS☆ 62026年9月8日 更新
Implement Amazon Macie to automatically discover, classify, and protect sensitive data in S3 buckets using machine learning and pattern matching for PII, financial data, and credentials Tespit.
中文简介正在准备中。当前显示原文说明。
MustafaKemal0146/fetih☆ 52026年10月11日 更新
tespit etme (s) 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 tespit etme (or) 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-...
中文简介正在准备中。当前显示原文说明。
MustafaKemal0146/fetih☆ 52026年10月11日 更新
Detects command-and-control (C2) communications tunneled through DNS protocol including DNS tunneling tools (Iodine, dnscat2, dns2tcp, Cobalt Strike DNS beacon), domain generation algorithms (DGA), encoded payload delivery via TXT/CNAME records, and DNS beaconing patterns. Covers Shannon entropy analysis of query subdomains, statistical anomaly detection, ML-based DGA classification, passive DNS correlation, and Zeek/Suricata signature development. Activates for requests involving DNS-based C2 detection, DNS tunnel identification, suspicious DNS traffic investigation, or DGA domain classification.
中文简介正在准备中。当前显示原文说明。
micsapp/micstec-skills☆ 42026年3月20日 更新
OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.
中文简介正在准备中。当前显示原文说明。
huang-sh/DeepScience☆ 42026年7月15日 更新
End-to-end shotgun metagenomics workflow from FASTQ to taxonomic and functional profiles, orchestrating controls/host depletion, Kraken2+Bracken classification, MetaPhlAn marker profiling, and HUMAnN functional profiling. Covers the controls-first ordering, why Kraken2 read counts are not abundances and MetaPhlAn cell fractions do not equal Bracken read fractions, and the consistent-pipeline framing. Use when profiling shotgun metagenomic samples end to end, or chaining classification, abundance, and function. For resistome see metagenomics/amr-detection; for strains see metagenomics/strain-tracking; for assembly see genome-assembly/metagenome-assembly.
中文简介正在准备中。当前显示原文说明。
huang-sh/DeepScience☆ 42026年7月15日 更新
Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
中文简介正在准备中。当前显示原文说明。
huang-sh/DeepScience☆ 42026年7月15日 更新
Claude API (platform.claude.com) のプロンプトエンジニアリング・評価リファレンス。 prompt engineering, XML タグ, few-shot, system prompts, モデル別プロンプティング（Fable 5.1 / Fable 5 / Opus 5.5 / Opus 5 / Sonnet 5.5 / Sonnet 5 / Haiku 5.5）。 evals, grading（LLM-graded 等）, jailbreak・prompt leak 対策, hallucination 低減, classification, ticket routing, commerce agent 等ユースケース実装。
中文简介正在准备中。当前显示原文说明。
Fandhe-AI/agent-reference-skills☆ 42026年10月20日 更新
Use this skill when a crash has been incorrectly classified as DOT-recordable when it shouldn't be. Covers the § 390.5 criteria, common misclassifications, and challenge process.
中文简介正在准备中。当前显示原文说明。
x3allamerican/skills☆ 32026年5月17日 更新
OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.
中文简介正在准备中。当前显示原文说明。
Lord1Egypt/awesome-skill-forge☆ 22026年6月10日 更新
Detects command-and-control (C2) communications tunneled through DNS protocol including DNS tunneling tools (Iodine, dnscat2, dns2tcp, Cobalt Strike DNS beacon), domain generation algorithms (DGA), encoded payload delivery via TXT/CNAME records, and DNS beaconing patterns. Covers Shannon entropy analysis of query subdomains, statistical anomaly detection, ML-based DGA classification, passive DNS correlation, and Zeek/Suricata signature development. Activates for requests involving DNS-based C2 detection, DNS tunnel identification, suspicious DNS traffic investigation, or DGA domain classification.
中文简介正在准备中。当前显示原文说明。
aniket2348823/Vul-Agent☆ 22026年6月9日 更新
盘点现有测试及缺口，将需求、业务规则和缺陷关联到稳定的测试编号与执行结果，通过测试台账追踪哪些行为已经验证、哪些仍需检查。
- 盘点现有测试及缺口
- 检查缺陷修复的回归保护
- 核查客户端与服务端的契约验证
affaan-m/ECC☆ 28.5万2026年10月20日 更新
使用USPTO官方信息检索美国专利与商标，核查申请状态、审查文件和权利转让记录，并保存检索条件与获取日期，将研究结果整理为可核查、可复现的记录。
- 按企业、发明人、日期或分类检索专利
- 核查专利申请状态和审查记录
- 获取商标状态与相关文件
affaan-m/ECC☆ 28.5万2026年10月20日 更新
帮助医疗应用分类患者、员工及财务敏感信息，设计按医疗机构划分的行级访问控制和审计记录，并检查日志、网址及浏览器存储中的信息泄露。
- 检查处理患者记录的代码
- 设计医疗机构间的访问控制
- 记录敏感信息的查看与修改
affaan-m/ECC☆ 28.5万2026年10月20日 更新
用于开发嵌入电子病历的临床决策支持，涵盖药物相互作用、剂量验证和临床评分，并按警报严重程度设计界面行为及检测漏报警的测试。
- 实现药物相互作用与过敏检查
- 根据患者信息验证剂量
- 实现NEWS2等临床评分
affaan-m/ECC☆ 28.5万2026年10月20日 更新
使用JSON、正则表达式、Pydantic类型定义或固定选项约束LLM输出，将文字中提取的信息和分类结果整理为便于后续程序使用的格式。
- 从文字中提取企业和产品信息
- 按固定标签分类评论
- 按字段整理个人资料
NousResearch/hermes-agent☆ 25.6万2026年10月20日 更新
无需准备额外训练数据，即可用文字标签对图片分类，或按内容描述搜索图片。还支持比较图片与文字的相似度、批量处理，以及将数值表示存入向量数据库。
- 用文字标签对照片分类
- 按内容描述搜索图片
- 比较图片与说明文字的对应程度
NousResearch/hermes-agent☆ 25.6万2026年10月20日 更新
通过正则表达式和语法规则约束本地语言模型的输出，生成符合指定格式的JSON、日期和固定分类标签，并用Python构建可重复使用的多阶段生成流程。
- 生成符合指定结构的JSON
- 统一日期和电子邮箱地址格式
- 使用固定标签对文本分类
NousResearch/hermes-agent☆ 25.6万2026年10月20日 更新
将Paperclip的Storybook界面示例与实际产品及功能清单对照，整理数据、分类和引用，保留示例标识与链接，并区分当前功能示例、原型和历史探索。
- 在界面变更后维护展示示例
- 更新模拟数据与API响应
- 分类原型和历史界面示例
paperclipai/paperclip☆ 10.5万2026年10月20日 更新
按目的选择Paperclip的Runner评估或完整产品端到端评估，结合证据、执行记录、费用及失败分类进行验证、结果解读和报告。
- 选择Runner或产品端到端评估
- 无需认证信息查看评估列表
- 依据证据与执行记录分析失败
paperclipai/paperclip☆ 10.5万2026年10月20日 更新
Browse World Monitor's curated news feeds by topic or language, with duplicate stories removed. Review headlines, sources, publication dates, threat labels, and alert flags, then summarize selected headlines or translate them through the connected tools.
中文简介正在准备中。当前显示英语简介。
- Scanning current headlines across topics
- Reviewing technology or finance news by language
- Summarizing or translating selected headlines
koala73/worldmonitor☆ 8.9万2026年10月20日 更新
Help DeerFlow maintainers investigate GitHub issues and review pull requests, or proposed code changes. It prepares or posts evidence-based comments with fix strategies, risks, and validation guidance, and compares competing proposals for the same issue.
中文简介正在准备中。当前显示英语简介。
- Draft issue replies with fixes and validation steps
- Post evidence-based pull request review findings
- Review batches of issues and pull requests
bytedance/deer-flow☆ 8.4万2026年10月20日 更新