Safely monitor ransomware group Tor-hosted data leak sites (DLS) to collect and extract structured victim posting data, track group activity trends over time, and produce sector- and geography-specific ransomware risk assessments. Use when performing threat intelligence gathering on active ransomware groups or building proactive defense reporting from double-extortion leak-site activity.
日本語の概要は準備中です。原文の説明を表示しています。
mukul975/Anthropic-Cybersecurity-Skills☆ 3.4万2026年8月31日 更新
Detect sensitive information disclosure via escalating dialogue probes. Covers system prompt extraction, credential/API key leakage, PII, and internal configuration exposure.
日本語の概要は準備中です。原文の説明を表示しています。
Tencent/AI-Infra-Guard☆ 6,8512026年10月9日 更新
Detects and prevents data leakage in machine learning and mathematical modeling. Use after ML tasks involving data cleaning, feature engineering, data augmentation, algorithm development, normalization, missing value imputation, dimensionality reduction, feature selection, or time series modeling. Checks if features/statistics would be available at prediction time.
日本語の概要は準備中です。原文の説明を表示しています。
foryourhealth111-pixel/Vibe-Skills☆ 3,6732026年8月31日 更新
Extract sensitive training data and internal capabilities from AI models through repeated programmatic querying. Use this skill when testing LLMs and ML models for data privacy leaks, intellectual property exposure, or unintended memorization. Covers membership inference, prompt extraction, training data reconstruction, and API boundary testing.
日本語の概要は準備中です。原文の説明を表示しています。
ShulkwiSEC/bb-huge☆ 242026年7月11日 更新
Monitor and analyze ransomware group data leak sites (DLS) to track victim postings, extract threat intelligence on group tactics, and assess sector-specific ransomware risk for proactive defense.
日本語の概要は準備中です。原文の説明を表示しています。
andycungkrinx91/konoha☆ 92026年10月9日 更新
Reviews the instructions text and skills of a declarative agent, with its stated capabilities, knowledge sources and audience, for prompt-injection exposure, data-leakage paths, over-broad permissions and missing refusals, and returns an attack-surface map, a findings table with severity, quoted evidence and attack path, one pasteable fix per finding, a probe pack the owner can run and a refusal-coverage table. Use when the user asks to "red-team this agent", "check these instructions for prompt injection", "could this agent leak data", "review the permissions on my agent" or "what should this agent refuse". Do not use for checking a SKILL.md against the format rules, use skill-file-reviewer instead; do not use for designing the launch test set, use agent-evaluation-plan instead. Drafts for human review; never approves, authorises or signs off.
日本語の概要は準備中です。原文の説明を表示しています。
kesslernity/awesome-copilot-agent-skills☆ 82026年9月19日 更新
Implement API schema validation using OpenAPI specifications and JSON Schema to enforce input/output contracts and prevent injection, data exposure, and mass assignment attacks.
日本語の概要は準備中です。原文の説明を表示しています。
MustafaKemal0146/fetih☆ 52026年10月11日 更新
Monitor and analyze ransomware group data leak sites (DLS) to track victim postings, extract threat intelligence on group tactics, and assess sector-specific ransomware risk for proactive defense.
日本語の概要は準備中です。原文の説明を表示しています。
micsapp/micstec-skills☆ 42026年3月20日 更新
Rigorous methodology for evaluating ML models on established benchmarks. Covers proper train/val/test splits, baseline verification from original papers, exact metric formula discrepancies, data-leak detection checklist, multi-seed robustness, and honest reporting templates. Use when claiming to beat published baselines, writing methods papers, or auditing existing results.
日本語の概要は準備中です。原文の説明を表示しています。
huang-sh/DeepScience☆ 42026年7月15日 更新
Validates predictive models on omics and biomedical data with nested cross-validation, group/batch/temporal-aware splits, the full data-leakage taxonomy, probability calibration, decision-curve net benefit, optimism correction, sample-size planning, and TRIPOD+AI reporting. Use when estimating model performance honestly, choosing a CV scheme, detecting leakage, or judging whether reported discrimination means the model is actually useful. For feature selection itself see machine-learning/biomarker-discovery; for confirmatory-trial inference see clinical-biostatistics/trial-reporting.
日本語の概要は準備中です。原文の説明を表示しています。
huang-sh/DeepScience☆ 42026年7月15日 更新
Monitor and analyze ransomware group data leak sites (DLS) to track victim postings, extract threat intelligence on group tactics, and assess sector-specific ransomware risk for proactive defense.
日本語の概要は準備中です。原文の説明を表示しています。
aniket2348823/Vul-Agent☆ 22026年6月9日 更新