1643 个 ・ 集成工具: Docker · 按人气排序
Set up MISP to collect, store, and correlate threat intelligence from multiple feeds. Use PyMISP to automate searches and imports of indicators of compromise, such as suspicious IP addresses and file hashes, and export data for security monitoring tools.
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Build a central vulnerability dashboard with DefectDojo to combine scanner findings, remove duplicates, and track remediation. Includes Docker deployment, automated scan imports from CI/CD pipelines, Jira ticket integration, and API queries for reporting metrics.
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Build a GitLab CI/CD pipeline with automated checks for vulnerable code, dependencies, container images, running applications, and exposed secrets. Covers scanner configuration, consolidated findings, and approval policies for serious vulnerabilities before production deployment.
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Set up MISP to collect threat feeds and centrally manage indicators such as suspicious IP addresses and domains. Configure automated synchronization, connect related findings, and export indicators to security monitoring platforms or blocklists.
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Analyze suspicious Linux ELF executables through file inspection, runtime tracing, and reverse engineering. Identify malware behavior, network destinations, and persistence mechanisms, then organize findings and indicators for investigations involving servers, containers, or cloud infrastructure.
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Investigate compromised Docker containers by preserving evidence and examining image layers, logs, file changes, and host settings. Summarize suspicious activity, security configuration issues, and vulnerability findings to support incident response.
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Integrate USDC payments into AI agents on Base L2. Guides setup for earning or paying, generates tailored code, and verifies configuration, covering escrow that holds funds until delivery and instant payments for HTTP requests.
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Set up a local web search server using Bright Data SERP API and unfancy-search. Retrieve results from multiple engines, optionally expand queries with AI, and receive combined rankings with duplicates removed and results grouped by domain.
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Build or rewrite resume sections for a target role and career stage. Get tailored summaries, organized skills, achievement-focused experience entries, and guidance on education, optional sections, and the order in which to present them.
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Improve resumes for software engineering, technical product management, data, and DevOps roles. Organize technical skills, connect achievements to business results, and highlight projects in a format suited to applicant tracking systems, software used to process job applications, and recruiters.
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Improve resume layouts with consistent fonts, spacing, section order, and clear headings. The skill recommends formatting that supports quick reading by recruiters and text parsing by applicant tracking systems, with guidance on file formats.
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Guide reinforcement learning, training models through rewards, with slime. Prepare data, configure Megatron-LM training and SGLang response generation, and set up asynchronous runs or custom multi-turn workflows for supported language models.
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Plan and configure large language model training with OpenRLHF to better match human preferences. Covers training method selection, distributed GPU setup, inference acceleration with vLLM, and troubleshooting memory or training stability issues.
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Guides large language model reinforcement learning with miles, covering environment setup, low-precision training, consistency between training and inference, and speculative decoding, where a smaller model proposes tokens for a larger model to verify.
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Build searches with Qdrant that find similar content and filter results by attributes. The skill covers document retrieval for AI answers, vector storage, memory optimization, and distributed deployment for larger collections.
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Use Phoenix to record execution traces, detailed records of an AI application's operations, and investigate problems. Evaluate answers on datasets, compare prompts and models, and set up monitoring through a self-hosted server.
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Run language models locally with llama.cpp on CPUs, Apple Silicon, or AMD and Intel GPUs. The skill guides model setup, memory-saving GGUF format choices, interactive chat, and serving models through an OpenAI-compatible API.
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Deploy language models with vLLM through an OpenAI-compatible API or offline batch processing. Tune response speed, throughput, and GPU memory use with quantization, which reduces numerical precision, and execution across multiple GPUs.
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Evaluate language models with over 100 benchmark tests covering knowledge, math, coding, safety, and image understanding. Configure runs on Docker, Slurm computing clusters, or cloud infrastructure, compare models, and export results.
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Build and operate AutoGPT agents that carry out multi-step workflows. Covers a visual editor for connecting processing blocks, scheduled and webhook triggers, deployment, execution monitoring, and benchmarks for evaluating agent performance.
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Build AI agents that inspect screenshots, choose actions, and operate a mouse and keyboard. Covers the observation and action loop, desktop control examples, and isolated environments that restrict access to files, credentials, and networks.
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Evaluate code generation models with HumanEval, MBPP, and other benchmarks. Test generated programs, compare models across programming languages, and record pass@k scores, which estimate the chance that at least one of k solutions passes the tests.
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Run cognee in Docker, starting with a prebuilt image for local document ingestion and questions. It also covers Docker Compose profiles for adding the UI, MCP server, Postgres, and Neo4j, plus persistent data storage.
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Run the cognee API server and optional interface on your own machine, check server health, connect SDK or HTTP clients, and configure authentication for personal development or use by multiple users.
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