GITHUB AGENT SKILLS / 技能收录
给你的 AI 助手
装上专业技能
我们从 GitHub 持续收录、审核并分类优质的 Agent Skills，按职业与使用场景整理，复制一行命令即可安装到 Claude Code、Cursor 等智能体。
按职业浏览 Agent Skills
为不同岗位挑选的高频技能
按使用场景浏览 Agent Skills
从你要完成的任务出发
按创作者浏览 Agent Skills
按 GitHub Stars 排序的优秀创作者
Microsoft
@microsoft
openclaw
@openclaw
OpenAI
@openai
The Apache Software Foundation
@apache
GitHub
@github
Anthropic
@anthropics
AutoGPT
@Significant-Gravitas
Garry Tan
@garrytan全部 Skills 196852
implementing-network-traffic-analysis-with-arkime
Queries Arkime (formerly Moloch) full packet capture via its API to search sessions, download PCAPs, detect C2 beaconing through connection interval/jitter stats, spot DNS tunneling via query-length analysis, and flag known-bad TLS certificate issuers, using the bundled scripts/agent.py. Use when investigating suspicious network flows or doing full-packet-capture forensics against an Arkime deployment.
implementing-network-segmentation-with-firewall-zones
Designs and implements network segmentation using firewall security zones, VLANs, inter-zone ACLs, and workload-level microsegmentation to restrict east-west lateral movement and enforce least-privilege access. Use when architecting security zones, writing inter-zone firewall policies, or meeting PCI DSS/HIPAA/NIST 800-53/zero-trust segmentation requirements for dynamic or traditional network environments.
implementing-network-segmentation-for-ot
Implements OT network segmentation using VLANs, OT-aware firewalls, data diodes, and IEC 62443 zone/conduit architecture, with a traffic-baseline-driven design tool for migrating flat Purdue-model networks without disrupting operations. Use when segmenting a flat OT network into Purdue levels, deploying an IT/OT DMZ, or isolating safety instrumented systems from basic process control systems.
implementing-network-policies-for-kubernetes
Writes portable upstream Kubernetes NetworkPolicy YAML - default-deny-all, DNS egress, namespace and pod selector rules - that works on any conformant CNI such as Calico or Cilium. Use when segmentation must stay CNI-portable, introducing a default-deny posture, or restricting east-west traffic between pods and namespaces without depending on a vendor CRD. Keywords: NetworkPolicy, default deny, podSelector, namespaceSelector, ingress, egress, CNI portable. Do not use for Calico-specific resources - use implementing-kubernetes-network-policy-with-calico.
implementing-network-intrusion-prevention-with-suricata
Deploys and configures Suricata as an inline network intrusion prevention system, covering IPS mode setup (NFQueue), custom rule writing, Emerging Threats ruleset management, performance tuning, and logging integration. Use when deploying real-time inline traffic inspection to actively block malicious traffic, or when tuning Suricata rules and performance for production IDS/IPS deployment.
implementing-network-deception-with-honeypots
Deploy and manage network honeypots using OpenCanary, T-Pot, or Cowrie to detect unauthorized access, lateral movement, and attacker reconnaissance.
implementing-network-access-control
Implements 802.1X port-based network access control using RADIUS authentication, PacketFence NAC, and switch configuration to enforce identity-based access policies, posture assessment, and automatic VLAN assignment for authorized devices. Use when enforcing zero-trust access at the network edge, quarantining non-compliant endpoints, meeting PCI-DSS/HIPAA/SOC 2 access requirements, or onboarding BYOD devices with automated provisioning.
implementing-network-access-control-with-cisco-ise
Deploys Cisco Identity Services Engine (ISE) as a RADIUS policy server for 802.1X wired and wireless authentication, MAC Authentication Bypass, posture assessment, dynamic VLAN assignment, downloadable ACLs, and TrustSec Security Group Tags. Use when deploying enterprise NAC with ISE and Active Directory integration, enforcing endpoint posture compliance, or segmenting access with TrustSec instead of a generic 802.1X/PacketFence setup.
implementing-nerc-cip-compliance-controls
Implements NERC CIP controls for Bulk Electric System (BES) cyber systems: asset categorization (CIP-002), electronic security perimeters (CIP-005), system security management (CIP-007), configuration management (CIP-010), and supply chain risk (CIP-013), including 2025 MFA updates. Use when a registered entity must achieve or maintain NERC CIP compliance, prepare for a Regional Entity audit, or categorize newly commissioned BES cyber systems.
implementing-mtls-for-zero-trust-services
Configures mutual TLS (mTLS) authentication between microservices using Python cryptography library for certificate generation and ssl module for TLS verification. Validates certificate chains, checks expiration, and audits mTLS deployment status. Use when implementing zero-trust service-to-service authentication.
implementing-mobile-application-management
Implements Mobile Application Management (MAM) policies to protect enterprise data on managed and unmanaged mobile devices through app-level controls including data loss prevention, selective wipe, app configuration, and containerization. Use when securing corporate apps on BYOD devices, implementing Intune App Protection Policies, or enforcing data separation between personal and work apps. Activates for requests involving MAM deployment, app protection policies, mobile containerization, or BYOD security.
implementing-mitre-attack-coverage-mapping
Implement MITRE ATT&CK coverage mapping to identify detection gaps, prioritize rule development, and measure SOC detection maturity against adversary techniques.
implementing-mimecast-targeted-attack-protection
Deploys and configures Mimecast Targeted Threat Protection (TTP) modules -- URL Protect (click-time URL rewriting/analysis), Attachment Protect (sandbox detonation), Impersonation Protect (BEC/whaling detection), and Internal Email Protect -- for Microsoft 365 or Google Workspace. Use when defending against phishing, spearphishing, or business email compromise, or configuring TTP policies in the Mimecast Administration Console.
implementing-microsegmentation-with-guardicore
Implements microsegmentation with Akamai Guardicore Segmentation to map application dependencies, visualize east-west traffic flows, and create granular, least-privilege network policies across VMs, containers, bare metal, and cloud. Use when blocking lateral movement in a data center or when PCI DSS/HIPAA compliance requires validated network segmentation.
implementing-memory-protection-with-dep-aslr
Implements memory protection mechanisms including DEP (Data Execution Prevention), ASLR (Address Space Layout Randomization), CFG (Control Flow Guard), and other exploit mitigations to prevent memory corruption attacks. Use when hardening endpoints against buffer overflow exploits, ROP chains, and code injection. Activates for requests involving memory protection, exploit mitigation, DEP, ASLR, or CFG configuration.
implementing-log-integrity-with-blockchain
Builds an append-only log integrity chain using SHA-256 hash chaining, where each entry incorporates the previous entry's hash so tampering invalidates all subsequent hashes; covers log ingestion (syslog/JSON/plain text), chain verification, pinpoint tamper detection, and checkpoint anchoring to external timestamping services. Use for tamper-evident log storage for compliance or forensics, or to verify whether log entries were altered.
implementing-log-forwarding-with-fluentd
Configures Fluent Bit as an endpoint log forwarder and Fluentd as the central aggregator for centralized log collection, routing, filtering, and enrichment, covering input plugins for syslog/file-tailing/application logs and output routing to Elasticsearch, S3, and Splunk. Use when setting up centralized log aggregation across distributed infrastructure or generating Fluent Bit/Fluentd configuration files for a new log pipeline.
implementing-llm-guardrails-for-security
Implements input/output validation guardrails for LLM applications using NVIDIA NeMo Guardrails (Colang), custom Python validators for PII detection, and the Guardrails AI framework, intercepting user inputs (prompt injection, PII, off-topic queries) and model outputs (hallucinations, toxic content, schema compliance). Use when adding safety controls to an LLM app/chatbot/RAG pipeline or validating outputs conform to expected schemas.