[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6H8VCABXZX83BAXNK8CGDMB":3,"skill-same:01M6H8VCABXZX83BAXNK8CGDMB":53},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":24,"linkOnly":25,"markdownContent":26,"markdownBody":27,"frontMatter":28,"refName":31,"commitSha":32,"skillDirectory":33,"forksCount":14,"repositoryHtmlUrl":34,"repositoryHomepageUrl":6,"ownerHtmlUrl":35,"ownerBlogUrl":36,"ownerDescription":37,"updatedAt":38,"install":39,"resources":52},"01M6H8VCABXZX83BAXNK8CGDMB","ai-ethics",null,"AI ethics principles and implementation","NeuralBlitz\u002FMito","NeuralBlitz","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F79354388?v=4","01M6H8T2TFH477HRS0KTSTKR2E","David Riggs","01M6H8T2YQQDXPYM092P670Z21",0,"MIT","https:\u002F\u002Fgithub.com\u002FNeuralBlitz\u002FMito\u002Fblob\u002Fmain\u002F.opencode\u002Fskills\u002Fai-ethics\u002FSKILL.md",[],[19,20,21,22,23],"ethics","ai ethics","opencode","implementation","principles","2026-11-10 10:12:30",false,"---\nname: ai-ethics\ndescription: AI ethics principles and implementation\nlicense: MIT\ncompatibility: opencode\nmetadata:\n  audience: developers\n  category: ethics\n---\n\n## What I do\n- Identify ethical issues in AI systems\n- Ensure fairness and bias mitigation\n- Implement transparency and explainability\n- Design for accountability\n- Protect privacy in ML systems\n\n## When to use me\nWhen building AI\u002FML systems that make decisions affecting people.\n\n## Ethical AI Framework\n\n### Fairness\n```python\nfrom typing import List, Dict\nimport numpy as np\n\nclass FairnessAuditor:\n    \"\"\"Audit AI systems for fairness\"\"\"\n    \n    def __init__(self):\n        self.protected_attributes = [\"race\", \"gender\", \"age\", \n                                      \"disability\", \"religion\"]\n    \n    def calculate_disparate_impact(self, predictions: np.ndarray,\n                                   sensitive_attr: np.ndarray,\n                                   threshold: float = 0.8) -> Dict:\n        \"\"\"Calculate disparate impact ratio\"\"\"\n        groups = np.unique(sensitive_attr)\n        \n        if len(groups) \u003C 2:\n            return {\"error\": \"Need multiple groups\"}\n        \n        positive_rates = {}\n        for group in groups:\n            mask = sensitive_attr == group\n            positive_rates[group] = predictions[mask].mean()\n        \n        # Calculate impact ratio\n        reference_rate = positive_rates[groups[0]]\n        ratios = {}\n        \n        for group, rate in positive_rates.items():\n            if reference_rate > 0:\n                ratios[group] = rate \u002F reference_rate\n        \n        return {\n            \"positive_rates\": positive_rates,\n            \"ratios\": ratios,\n            \"fails_disparate_impact\": any(r \u003C threshold for r in ratios.values())\n        }\n    \n    def calculate_equalized_odds(self, predictions: np.ndarray,\n                               labels: np.ndarray,\n                               sensitive_attr: np.ndarray) -> Dict:\n        \"\"\"Check equalized odds (true positive rate equality)\"\"\"\n        groups = np.unique(sensitive_attr)\n        \n        tpr_by_group = {}\n        fpr_by_group = {}\n        \n        for group in groups:\n            mask = sensitive_attr == group\n            tp = ((predictions == 1) & (labels == 1) & mask).sum()\n            fp = ((predictions == 1) & (labels == 0) & mask).sum()\n            tn = ((predictions == 0) & (labels == 0) & mask).sum()\n            fn = ((predictions == 0) & (labels == 1) & mask).sum()\n            \n            tpr_by_group[group] = tp \u002F (tp + fn) if (tp + fn) > 0 else 0\n            fpr_by_group[group] = fp \u002F (fp + tn) if (fp + tn) > 0 else 0\n        \n        return {\n            \"true_positive_rates\": tpr_by_group,\n            \"false_positive_rates\": fpr_by_group,\n            \"equalized\": self._check_equalized(tpr_by_group, fpr_by_group)\n        }\n    \n    def mitigate_bias(self, model, X: np.ndarray, \n                     sensitive_attr: np.ndarray,\n                     method: str = \"reweighting\") -> np.ndarray:\n        \"\"\"Apply bias mitigation technique\"\"\"\n        if method == \"reweighting\":\n            return self._reweighting_mitigation(X, sensitive_attr)\n        elif method == \"threshold\":\n            return self._threshold_adjustment(X, sensitive_attr)\n        else:\n            raise ValueError(f\"Unknown method: {method}\")\n    \n    def _reweighting_mitigation(self, X, sensitive_attr) -> np.ndarray:\n        \"\"\"Reweighting-based fairness intervention\"\"\"\n        # Compute weights to equalize outcomes\n        return X  # Simplified\n```\n\n### Transparency and Explainability\n```python\nclass ModelExplainer:\n    \"\"\"Explain model predictions\"\"\"\n    \n    def __init__(self, model):\n        self.model = model\n    \n    def explain_prediction(self, instance: np.ndarray,\n                          method: str = \"shap\") -> Dict:\n        \"\"\"Explain individual prediction\"\"\"\n        if method == \"shap\":\n            return self._shap_explanation(instance)\n        elif method == \"lime\":\n            return self._lime_explanation(instance)\n        elif method == \"feature_importance\":\n            return self._feature_importance_explanation(instance)\n    \n    def _feature_importance_explanation(self, instance: np.ndarray) -> Dict:\n        \"\"\"Global feature importance explanation\"\"\"\n        if hasattr(self.model, \"feature_importances_\"):\n            importances = self.model.feature_importances_\n            return {\n                \"method\": \"feature_importance\",\n                \"importances\": importances.tolist(),\n                \"top_features\": np.argsort(importances)[-5:][::-1].tolist()\n            }\n        return {\"error\": \"Model does not support feature importance\"}\n    \n    def generate_model_cards(self, model, training_data: Dict,\n                            performance: Dict) -> Dict:\n        \"\"\"Generate model documentation (model card)\"\"\"\n        return {\n            \"model_name\": model.__class__.__name__,\n            \"overview\": \"Description of model purpose\",\n            \"owner\": \"Team responsible\",\n            \"training_data\": {\n                \"dataset\": training_data.get(\"name\"),\n                \"size\": training_data.get(\"size\"),\n                \"sources\": training_data.get(\"sources\")\n            },\n            \"performance\": {\n                \"metrics\": performance.get(\"metrics\"),\n                \"evaluation_data\": performance.get(\"test_size\")\n            },\n            \"fairness\": {\n                \"evaluated_groups\": [],\n                \"known_biases\": []\n            },\n            \"limitations\": \"Known model limitations\",\n            \"use_cases\": \"Intended use cases\",\n            \"caveats\": \"Additional caveats\"\n        }\n```\n\n### Accountability\n```python\nclass AIAccountability:\n    \"\"\"Ensure AI system accountability\"\"\"\n    \n    def __init__(self):\n        self.audit_trail = []\n    \n    def log_decision(self, decision_id: str, input_data: Dict,\n                    prediction: Any, model_version: str,\n                    user_id: str = None):\n        \"\"\"Log AI decision for accountability\"\"\"\n        entry = {\n            \"decision_id\": decision_id,\n            \"timestamp\": datetime.now().isoformat(),\n            \"input_data_hash\": hash(str(input_data)),\n            \"prediction\": prediction,\n            \"model_version\": model_version,\n            \"user_id\": user_id,\n            \"ip_address\": get_client_ip()\n        }\n        \n        self.audit_trail.append(entry)\n        self._write_to_immutable_store(entry)\n    \n    def generate_audit_report(self, start_date: datetime,\n                             end_date: datetime) -> Dict:\n        \"\"\"Generate audit report for date range\"\"\"\n        relevant_entries = [\n            e for e in self.audit_trail\n            if start_date \u003C= e[\"timestamp\"] \u003C= end_date\n        ]\n        \n        return {\n            \"total_decisions\": len(relevant_entries),\n            \"unique_users\": len(set(e[\"user_id\"] for e in relevant_entries)),\n            \"model_versions\": set(e[\"model_version\"] for e in relevant_entries),\n            \"predictions_by_outcome\": self._count_outcomes(relevant_entries)\n        }\n    \n    def implement_human_oversight(self, prediction: Any,\n                                 threshold: float = 0.8) -> Dict:\n        \"\"\"Determine if human review is needed\"\"\"\n        confidence = prediction.get(\"confidence\", 1.0)\n        \n        return {\n            \"requires_human_review\": confidence \u003C threshold,\n            \"confidence\": confidence,\n            \"threshold\": threshold,\n            \"review_reason\": \"Low confidence\" if confidence \u003C threshold else None\n        }\n```\n\n### Privacy Protection\n```python\nclass PrivacyProtection:\n    \"\"\"Privacy-preserving ML techniques\"\"\"\n    \n    def apply_differential_privacy(self, gradients: np.ndarray,\n                                   epsilon: float = 1.0) -> np.ndarray:\n        \"\"\"Add noise for differential privacy\"\"\"\n        sensitivity = np.max(np.linalg.norm(gradients, axis=1))\n        noise = np.random.laplace(0, sensitivity \u002F epsilon, gradients.shape)\n        return gradients + noise\n    \n    def anonymize_data(self, data: pd.DataFrame,\n                      quasi_identifiers: List[str],\n                      sensitive_attributes: List[str]) -> pd.DataFrame:\n        \"\"\"Apply k-anonymity\"\"\"\n        # Generalize quasi-identifiers\n        for col in quasi_identifiers:\n            data[col] = self._generalize(data[col], col)\n        \n        return data\n    \n    def _generalize(self, series, col_name):\n        \"\"\"Generalize values for k-anonymity\"\"\"\n        if col_name == \"age\":\n            return pd.cut(series, bins=[0, 18, 30, 45, 60, 100], \n                        labels=[\"0-17\", \"18-29\", \"30-44\", \"45-59\", \"60+\"])\n        return series\n```\n","## What I do\n- Identify ethical issues in AI systems\n- Ensure fairness and bias mitigation\n- Implement transparency and explainability\n- Design for accountability\n- Protect privacy in ML systems\n\n## When to use me\nWhen building AI\u002FML systems that make decisions affecting people.\n\n## Ethical AI Framework\n\n### Fairness\n```python\nfrom typing import List, Dict\nimport numpy as np\n\nclass FairnessAuditor:\n    \"\"\"Audit AI systems for fairness\"\"\"\n    \n    def __init__(self):\n        self.protected_attributes = [\"race\", \"gender\", \"age\", \n                                      \"disability\", \"religion\"]\n    \n    def calculate_disparate_impact(self, predictions: np.ndarray,\n                                   sensitive_attr: np.ndarray,\n                                   threshold: float = 0.8) -> Dict:\n        \"\"\"Calculate disparate impact ratio\"\"\"\n        groups = np.unique(sensitive_attr)\n        \n        if len(groups) \u003C 2:\n            return {\"error\": \"Need multiple groups\"}\n        \n        positive_rates = {}\n        for group in groups:\n            mask = sensitive_attr == group\n            positive_rates[group] = predictions[mask].mean()\n        \n        # Calculate impact ratio\n        reference_rate = positive_rates[groups[0]]\n        ratios = {}\n        \n        for group, rate in positive_rates.items():\n            if reference_rate > 0:\n                ratios[group] = rate \u002F reference_rate\n        \n        return {\n            \"positive_rates\": positive_rates,\n            \"ratios\": ratios,\n            \"fails_disparate_impact\": any(r \u003C threshold for r in ratios.values())\n        }\n    \n    def calculate_equalized_odds(self, predictions: np.ndarray,\n                               labels: np.ndarray,\n                               sensitive_attr: np.ndarray) -> Dict:\n        \"\"\"Check equalized odds (true positive rate equality)\"\"\"\n        groups = np.unique(sensitive_attr)\n        \n        tpr_by_group = {}\n        fpr_by_group = {}\n        \n        for group in groups:\n            mask = sensitive_attr == group\n            tp = ((predictions == 1) & (labels == 1) & mask).sum()\n            fp = ((predictions == 1) & (labels == 0) & mask).sum()\n            tn = ((predictions == 0) & (labels == 0) & mask).sum()\n            fn = ((predictions == 0) & (labels == 1) & mask).sum()\n            \n            tpr_by_group[group] = tp \u002F (tp + fn) if (tp + fn) > 0 else 0\n            fpr_by_group[group] = fp \u002F (fp + tn) if (fp + tn) > 0 else 0\n        \n        return {\n            \"true_positive_rates\": tpr_by_group,\n            \"false_positive_rates\": fpr_by_group,\n            \"equalized\": self._check_equalized(tpr_by_group, fpr_by_group)\n        }\n    \n    def mitigate_bias(self, model, X: np.ndarray, \n                     sensitive_attr: np.ndarray,\n                     method: str = \"reweighting\") -> np.ndarray:\n        \"\"\"Apply bias mitigation technique\"\"\"\n        if method == \"reweighting\":\n            return self._reweighting_mitigation(X, sensitive_attr)\n        elif method == \"threshold\":\n            return self._threshold_adjustment(X, sensitive_attr)\n        else:\n            raise ValueError(f\"Unknown method: {method}\")\n    \n    def _reweighting_mitigation(self, X, sensitive_attr) -> np.ndarray:\n        \"\"\"Reweighting-based fairness intervention\"\"\"\n        # Compute weights to equalize outcomes\n        return X  # Simplified\n```\n\n### Transparency and Explainability\n```python\nclass ModelExplainer:\n    \"\"\"Explain model predictions\"\"\"\n    \n    def __init__(self, model):\n        self.model = model\n    \n    def explain_prediction(self, instance: np.ndarray,\n                          method: str = \"shap\") -> Dict:\n        \"\"\"Explain individual prediction\"\"\"\n        if method == \"shap\":\n            return self._shap_explanation(instance)\n        elif method == \"lime\":\n            return self._lime_explanation(instance)\n        elif method == \"feature_importance\":\n            return self._feature_importance_explanation(instance)\n    \n    def _feature_importance_explanation(self, instance: np.ndarray) -> Dict:\n        \"\"\"Global feature importance explanation\"\"\"\n        if hasattr(self.model, \"feature_importances_\"):\n            importances = self.model.feature_importances_\n            return {\n                \"method\": \"feature_importance\",\n                \"importances\": importances.tolist(),\n                \"top_features\": np.argsort(importances)[-5:][::-1].tolist()\n            }\n        return {\"error\": \"Model does not support feature importance\"}\n    \n    def generate_model_cards(self, model, training_data: Dict,\n                            performance: Dict) -> Dict:\n        \"\"\"Generate model documentation (model card)\"\"\"\n        return {\n            \"model_name\": model.__class__.__name__,\n            \"overview\": \"Description of model purpose\",\n            \"owner\": \"Team responsible\",\n            \"training_data\": {\n                \"dataset\": training_data.get(\"name\"),\n                \"size\": training_data.get(\"size\"),\n                \"sources\": training_data.get(\"sources\")\n            },\n            \"performance\": {\n                \"metrics\": performance.get(\"metrics\"),\n                \"evaluation_data\": performance.get(\"test_size\")\n            },\n            \"fairness\": {\n                \"evaluated_groups\": [],\n                \"known_biases\": []\n            },\n            \"limitations\": \"Known model limitations\",\n            \"use_cases\": \"Intended use cases\",\n            \"caveats\": \"Additional caveats\"\n        }\n```\n\n### Accountability\n```python\nclass AIAccountability:\n    \"\"\"Ensure AI system accountability\"\"\"\n    \n    def __init__(self):\n        self.audit_trail = []\n    \n    def log_decision(self, decision_id: str, input_data: Dict,\n                    prediction: Any, model_version: str,\n                    user_id: str = None):\n        \"\"\"Log AI decision for accountability\"\"\"\n        entry = {\n            \"decision_id\": decision_id,\n            \"timestamp\": datetime.now().isoformat(),\n            \"input_data_hash\": hash(str(input_data)),\n            \"prediction\": prediction,\n            \"model_version\": model_version,\n            \"user_id\": user_id,\n            \"ip_address\": get_client_ip()\n        }\n        \n        self.audit_trail.append(entry)\n        self._write_to_immutable_store(entry)\n    \n    def generate_audit_report(self, start_date: datetime,\n                             end_date: datetime) -> Dict:\n        \"\"\"Generate audit report for date range\"\"\"\n        relevant_entries = [\n            e for e in self.audit_trail\n            if start_date \u003C= e[\"timestamp\"] \u003C= end_date\n        ]\n        \n        return {\n            \"total_decisions\": len(relevant_entries),\n            \"unique_users\": len(set(e[\"user_id\"] for e in relevant_entries)),\n            \"model_versions\": set(e[\"model_version\"] for e in relevant_entries),\n            \"predictions_by_outcome\": self._count_outcomes(relevant_entries)\n        }\n    \n    def implement_human_oversight(self, prediction: Any,\n                                 threshold: float = 0.8) -> Dict:\n        \"\"\"Determine if human review is needed\"\"\"\n        confidence = prediction.get(\"confidence\", 1.0)\n        \n        return {\n            \"requires_human_review\": confidence \u003C threshold,\n            \"confidence\": confidence,\n            \"threshold\": threshold,\n            \"review_reason\": \"Low confidence\" if confidence \u003C threshold else None\n        }\n```\n\n### Privacy Protection\n```python\nclass PrivacyProtection:\n    \"\"\"Privacy-preserving ML techniques\"\"\"\n    \n    def apply_differential_privacy(self, gradients: np.ndarray,\n                                   epsilon: float = 1.0) -> np.ndarray:\n        \"\"\"Add noise for differential privacy\"\"\"\n        sensitivity = np.max(np.linalg.norm(gradients, axis=1))\n        noise = np.random.laplace(0, sensitivity \u002F epsilon, gradients.shape)\n        return gradients + noise\n    \n    def anonymize_data(self, data: pd.DataFrame,\n                      quasi_identifiers: List[str],\n                      sensitive_attributes: List[str]) -> pd.DataFrame:\n        \"\"\"Apply k-anonymity\"\"\"\n        # Generalize quasi-identifiers\n        for col in quasi_identifiers:\n            data[col] = self._generalize(data[col], col)\n        \n        return data\n    \n    def _generalize(self, series, col_name):\n        \"\"\"Generalize values for k-anonymity\"\"\"\n        if col_name == \"age\":\n            return pd.cut(series, bins=[0, 18, 30, 45, 60, 100], \n                        labels=[\"0-17\", \"18-29\", \"30-44\", \"45-59\", \"60+\"])\n        return series\n```\n",{"name":5,"license":15,"metadata":29,"description":7,"compatibility":21},{"audience":30,"category":19},"developers","main","2ea764e153842704d24a2e9aa4170441a7f03081",".opencode\u002Fskills\u002Fai-ethics","https:\u002F\u002Fgithub.com\u002FNeuralBlitz\u002FMito","https:\u002F\u002Fgithub.com\u002FNeuralBlitz","nuralnexus.org","Theoretical and Futuristic AI Nerd 🤓 ","2026-04-21 14:37:05",{"source":8,"skillName":5,"commands":40,"prompt":50,"zipAvailable":51},[41,44,47],{"tool":42,"command":43},"npx","npx skills add NeuralBlitz\u002FMito --skill ai-ethics",{"tool":45,"command":46},"bunx","bunx skills add NeuralBlitz\u002FMito --skill ai-ethics",{"tool":48,"command":49},"pnpm","pnpm dlx skills add NeuralBlitz\u002FMito --skill ai-ethics","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FNeuralBlitz\u002FMito\u002Fblob\u002Fmain\u002F.opencode\u002Fskills\u002Fai-ethics\u002FSKILL.md",true,[],[54,70,82,95,103,128],{"skillNo":55,"name":56,"nameZh":6,"description":57,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":58,"categories":59,"keywords":60,"publishedAt":24},"01M6HAZSAQK1S27CEZYAYJTQDX","wireless-communications","Wireless systems, mobile networks, and 5G\u002F6G","https:\u002F\u002Fgithub.com\u002FNeuralBlitz\u002FMito\u002Fblob\u002Fmain\u002F.opencode\u002Fskills\u002Fwireless-communications\u002FSKILL.md",[],[61,62,63,64,65,66,67,68,21,69],"communications","wireless communications","wireless","systems","engineering","networks","5G","students","mobile",{"skillNo":71,"name":72,"nameZh":6,"description":73,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":74,"categories":75,"keywords":76,"publishedAt":24},"01M6HAZFX6E8NK3WZWDX5YCKYY","websockets","WebSocket implementation and best practices","https:\u002F\u002Fgithub.com\u002FNeuralBlitz\u002FMito\u002Fblob\u002Fmain\u002F.opencode\u002Fskills\u002Fwebassembly\u002Fwebsockets\u002FSKILL.md",[],[72,77,78,22,21,79,80,81],"websocket implementation","time","real-time","websocket","best practices",{"skillNo":83,"name":84,"nameZh":6,"description":85,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":86,"categories":87,"keywords":88,"publishedAt":24},"01M6HAZ6DAXBQ7QHXXM1EM7715","vulnerability-auditing","Vulnerability assessment and auditing","https:\u002F\u002Fgithub.com\u002FNeuralBlitz\u002FMito\u002Fblob\u002Fmain\u002F.opencode\u002Fskills\u002Fvulnerability-auditing\u002FSKILL.md",[],[89,90,91,92,21,93,94],"vulnerability","vulnerability auditing","security","vulnerability assessment","cybersecurity","assessment",{"skillNo":96,"name":97,"nameZh":6,"description":98,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":99,"categories":100,"keywords":101,"publishedAt":24},"01M6HAZ3FKF3CS9Z3J96RVE2ZE","vulnerability-assessment","Finding and assessing security vulnerabilities","https:\u002F\u002Fgithub.com\u002FNeuralBlitz\u002FMito\u002Fblob\u002Fmain\u002F.opencode\u002Fskills\u002Fvue\u002Fvulnerability-assessment\u002FSKILL.md",[],[92,89,94,91,102,21],"vulnerabilities",{"skillNo":104,"name":105,"nameZh":6,"description":106,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":107,"categories":108,"keywords":119,"publishedAt":24},"01M6HAYSHM6HKRKEHFP0SGZQX7","vlsi-design","Very-Large-Scale Integration circuit design and semiconductor engineering","https:\u002F\u002Fgithub.com\u002FNeuralBlitz\u002FMito\u002Fblob\u002Fmain\u002F.opencode\u002Fskills\u002Fvlsi-design\u002FSKILL.md",[109,114],{"categoryNo":110,"categoryName":111,"taxonomyType":112,"taxonomyTypeDesc":113},"01JBSEEDCAT000000000000015","视觉设计","SCENARIO","使用场景",{"categoryNo":115,"categoryName":116,"taxonomyType":117,"taxonomyTypeDesc":118},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[120,121,122,123,124,65,125,21,126,68,127],"vlsi design","VLSI","design","hardware","integration","computer","scale","circuit",{"skillNo":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":132,"categories":133,"keywords":134,"publishedAt":24},"01M6HAYPQ30FFE2JXD7WQJ8R6C","unsupervised","Unsupervised learning concepts","https:\u002F\u002Fgithub.com\u002FNeuralBlitz\u002FMito\u002Fblob\u002Fmain\u002F.opencode\u002Fskills\u002Fvisualization\u002Funsupervised\u002FSKILL.md",[],[130,135,21,136,137,138,139,140],"learning","intelligence","unsupervised learning","machine","concepts","machine-learning"]