GITHUB AGENT SKILLS / 技能收录
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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。
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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
detecting-model-extraction-attacks
Detect MITRE ATLAS AML.T0024 attacks (model stealing, inversion, membership inference) performed via inference-API abuse, by monitoring per-principal query volume/distribution, rate-limiting and perturbing outputs, and red-teaming your model's extractability. Use for a public or partner inference API needing cloning/inversion/membership-inference detection, or a pre-deployment red-team exercise to measure extraction risk.
detecting-modbus-protocol-anomalies
Detect anomalies in Modbus/TCP and Modbus RTU industrial traffic via function code monitoring, register range validation, timing analysis, and deep packet inspection, using Zeek's Modbus analyzer, Suricata IDS with OT rules, and Python Markov chain models of normal transaction sequences. Use for deploying Modbus IDS in OT environments, baselining polling patterns, investigating suspicious Modbus traffic, or building function code allowlists.
detecting-modbus-command-injection-attacks
Detect command injection against Modbus TCP/RTU in ICS/SCADA environments by monitoring unauthorized writes, anomalous function codes, malformed frames, and deviations from communication baselines using ICS-aware IDS and deep packet inspection. Use when deploying IDS for Modbus OT networks, investigating unauthorized PLC register/coil changes, or responding to FrostyGoop-style Modbus attacks.
detecting-mobile-malware-behavior
Detects and analyzes malicious behavior in mobile applications through behavioral analysis, permission abuse detection, network traffic monitoring, and dynamic instrumentation. Use when analyzing suspicious mobile applications for data exfiltration, command-and-control communication, credential stealing, SMS interception, or other malware indicators. Activates for requests involving mobile malware analysis, app behavior monitoring, trojan detection, or suspicious app investigation.
detecting-misconfigured-azure-storage
Audit Azure Storage accounts for public blob containers, missing encryption, overly permissive SAS tokens, disabled logging, and network access violations using Azure CLI, PowerShell, and Microsoft Defender for Storage. Use for storage security audits across subscriptions, responding to Defender for Storage anonymous-access alerts, verifying compliance controls, or setting security baselines when onboarding a subscription.
detecting-mimikatz-execution-patterns
Detect Mimikatz credential-dumping activity via command-line pattern matching, LSASS access signatures, binary/hash indicators, and in-memory detection of known Mimikatz modules. Use when threat hunting for T1003 credential access, triaging EDR/SIEM alerts on LSASS access, scoping compromise during incident response, or validating detection coverage in a purple team exercise.
detecting-malicious-scheduled-tasks-with-sysmon
Detect malicious scheduled task creation and modification using Sysmon Event IDs 1 (Process Create for schtasks.exe), 11 (File Create for task XML), and Windows Security Event 4698/4702. The analyst correlates task creation with suspicious parent processes, public directory paths, and encoded command arguments to identify persistence and lateral movement via scheduled tasks. Activates for requests involving scheduled task detection, Sysmon persistence hunting, or T1053.005 Scheduled Task/Job analysis.
detecting-malicious-npm-packages
Triage npm packages and lockfiles for install-script malware, credential exfiltration, and worming behavior using GuardDog, manual tarball inspection, and dynamic detonation with network/filesystem monitoring. Use when vetting a new dependency, reviewing a package.json/package-lock.json during code review, checking lockfiles against a supply-chain advisory's known-bad versions, or investigating a host suspected of installing a trojanized package.
detecting-living-off-the-land-with-lolbas
Detect Living Off the Land Binaries (LOLBins/LOLBAS) abuse including certutil, regsvr32, mshta, and rundll32 via process telemetry, Sigma rules, and parent-child process analysis with Sysmon endpoint data. Use when hunting for adversaries abusing built-in Windows binaries to download, execute, or proxy malicious code while evading traditional executable-based detection.
detecting-living-off-the-land-attacks
Detect abuse of legitimate Windows binaries (LOLBins) used for living off the land attacks. Monitors process creation, command-line arguments, and parent-child relationships to identify suspicious LOLBin execution patterns.
detecting-lateral-movement-with-zeek
Detect lateral movement in network traffic using Zeek (formerly Bro) log analysis. Parses conn.log, smb_mapping.log, smb_files.log, dce_rpc.log, kerberos.log, and ntlm.log to identify SMB file transfers, NTLM account spray activity, remote service execution, and anomalous internal connections.
detecting-lateral-movement-with-splunk
Detect adversary lateral movement across networks using Splunk SPL queries against Windows authentication logs, SMB traffic, and remote service (WMI/PsExec/RDP) abuse. Use when hunting for MITRE ATT&CK TA0008 lateral movement activity or investigating suspected pivoting between hosts during an incident, with Splunk as the SIEM.
detecting-lateral-movement-in-network
Identifies lateral movement techniques in enterprise networks by analyzing authentication logs, network flows, SMB traffic, and RDP sessions using Zeek, Velociraptor, and SIEM correlation rules to detect attackers moving between systems.
detecting-kerberoasting-attacks
Detect Kerberoasting attacks by monitoring for anomalous Kerberos TGS requests (Event ID 4769) targeting service accounts with SPNs, which attackers request offline to crack service account passwords. Use when hunting for MITRE T1558 credential access activity or investigating suspected service account password cracking attempts in Active Directory Kerberos logs.
detecting-insider-threat-with-ueba
Implement User and Entity Behavior Analytics (UEBA) using Elasticsearch/OpenSearch to build behavioral baselines, calculate anomaly scores, perform peer group analysis, and alert on insider threat indicators such as data exfiltration, privilege abuse, and unauthorized access. Use when building or tuning a UEBA pipeline rather than a one-off manual hunt.
detecting-insider-threat-behaviors
Detect insider threat behavioral indicators including unusual data access, off-hours activity, mass file downloads, privilege abuse, and resignation-correlated data theft. Use when proactively threat-hunting for malicious or negligent insider activity, or when investigating a departing or disgruntled employee for potential data theft.
detecting-insider-data-exfiltration-via-dlp
Detects insider data exfiltration by analyzing DLP policy violations, file access patterns, upload volume anomalies, and off-hours activity in endpoint and cloud logs. Uses pandas for behavioral analytics and statistical baselines. Use when investigating insider threats or building user behavior analytics for data loss prevention.
detecting-indirect-prompt-injection
Detect and defend against indirect prompt injection hidden in web pages, documents, and images consumed by an agent, via content extraction (HTML/PDF/OCR), normalization, and scanning with LLM Guard's PromptInjection scanner or Hugging Face Prompt Guard 2. Use when an agent ingests untrusted external content and you need to screen it for injected instructions before the LLM processes it.