「network」の検索結果

Configures TLS 1.3 (RFC 8446) on servers, covering cipher suite and key-exchange group selection, and validates the resulting configuration with openssl s_client and testssl.sh. Use when deploying or hardening TLS 1.3 for secure communications, or when testing a server for common TLS misconfigurations and vulnerabilities.

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Systematically collects, categorizes, and distributes indicators of compromise (IOCs) during and after security incidents to enable detection, blocking, and threat intelligence sharing. Covers network, host, email, and behavioral indicators using STIX/TAXII formats and threat intelligence platforms. Activates for requests involving IOC collection, indicator extraction, threat indicator sharing, compromise indicators, STIX export, or IOC enrichment.

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Collect volatile forensic evidence from a compromised host by following the order of volatility, preserving memory, network connections, running processes, and system state with documented chain of custody before they are lost. Use before isolating, shutting down, or remediating a compromised host, especially when fileless or memory-resident malware is suspected, root cause analysis is needed, or the evidence must hold up in legal proceedings.

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Parse Windows LNK shortcut files to extract target paths, MAC timestamps, volume serial numbers, and machine identifiers for forensic timeline reconstruction. Use when investigating recently-accessed files, tracking removable media or network paths referenced by shortcuts, or building a DFIR timeline from LNK artifacts.

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Performs Linux memory acquisition using LiME (Linux Memory Extractor) kernel module and analysis with Volatility 3 framework. Extracts process lists, network connections, bash history, loaded kernel modules, and injected code from Linux memory images. Use when performing incident response on compromised Linux systems.

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Detonate malware samples in Cuckoo Sandbox to observe runtime behavior — process creation, file system and registry changes, network communications, and API calls — and generate behavioral reports for classification and IOC extraction. Use when a sample has passed static triage and needs dynamic/behavioral analysis, when mapping a full infection chain, or when building YARA/behavioral signatures from observed sandbox activity.

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Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials. Supports Windows, Linux, and macOS memory forensics. Activates for requests involving memory forensics, RAM analysis, volatile data examination, process injection detection, or memory-resident malware investigation.

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。

Analyzes DNS query logs to detect data exfiltration via DNS tunneling, DGA domain communication, and covert C2 channels using entropy analysis, query volume anomalies, and subdomain length detection in SIEM platforms. Use when SOC teams need to identify DNS-based threats that bypass traditional network security controls.

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URLScan.io is a free service for scanning and analyzing suspicious URLs. It captures screenshots, DOM content, HTTP transactions, JavaScript behavior, and network connections of web pages in an isolat

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Analyzes malware C2 communication over HTTP, HTTPS, DNS, and custom protocols to reverse-engineer beacon patterns, command structures, data encoding, and infrastructure (primary servers, fallback domains, dead drops). Use after reverse engineering reveals network traffic needing protocol analysis or when building detection signatures for a framework like Cobalt Strike, Metasploit, or Sliver.

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Creating interactive data visualisations using d3.js. This skill should be used when creating custom charts, graphs, network diagrams, geographic visualisations, or any complex SVG-based data visualisation that requires fine-grained control over visual elements, transitions, or interactions. Use this for bespoke visualisations beyond standard charting libraries, whether in React, Vue, Svelte, vanilla JavaScript, or any other environment.

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Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.

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Provides guidance for interpreting and manipulating neural network internals using nnsight with optional NDIF remote execution. Use when needing to run interpretability experiments on massive models (70B+) without local GPU resources, or when working with any PyTorch architecture.

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Use when the user needs something that ships outside cognee core — community database adapters (Qdrant, Milvus, Weaviate, Redis, Pinecone, FalkorDB, Memgraph, DuckDB, NetworkX, …), data-source connectors (Slack, Gmail, Notion, Confluence, Google Drive), custom tasks/pipelines/retrievers (Exa, ScrapeGraph, codify), Keywords AI observability — or wants to contribute a package to the cognee-community repo.

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Use when an agent needs to drive a real Chrome window via opencli — inspect a page, fill forms, click through logged-in flows, or extract data ad-hoc. Covers the selector-first target contract, compound form fields, stale-ref handling, network capture, and the agent-native envelopes the CLI returns. Not for writing adapters — see opencli-adapter-author for that.

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Work inside cua Spaces through the cua MCP server. A Space is a remote or local computer the user can watch; you can run commands in it, move files in and out, show its desktop or a single window on the user's screen, start coding agents inside it, teleport a signed-in app session into it, and share the host's network with it. Use when the user mentions Spaces, asks you to do work "in a Space", or wants a task isolated but visible.

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Reference skill for Zoom Probe SDK. Use after routing to a preflight workflow when testing browser compatibility, media permissions, audio or video diagnostics, and network readiness before users join.

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Reproduce and measure a Mastra Code runtime bug against a real model by driving the built TUI headlessly in tmux while every layer (network, provider stream, run engine, TUI) appends timestamped JSONL. Use when a bug depends on real provider timing, streaming order, or event flow that mocked tests and fixtures may not reproduce — e.g. wrong token rates, flicker, stuck status, ordering races, or "only happens sometimes with a real model".

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Security audit and vulnerability scanner for AI agent skills before installation. Use when: (1) evaluating a skill from an untrusted source, (2) auditing a skill directory or git repo URL for malicious code, (3) pre-install security gate for Claude Code plugins, OpenClaw skills, or Codex skills, (4) scanning Python scripts for dangerous patterns like os.system, eval, subprocess, network exfiltration, (5) detecting prompt injection in SKILL.md files, (6) checking dependency supply chain risks, (7) verifying file system access stays within skill boundaries. Triggers: "audit this skill", "is this skill safe", "scan skill for security", "check skill before install", "skill security check", "skill vulnerability scan".

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Phase 2 of building a Claude Managed Agent — turn a validated build sheet into exact API payloads and a resumable BYOK curl launch script, then launch (environment → agent → session → kickoff) using the founder's OWN Anthropic key. Use when the user says "launch it", "deploy the agent", "create the agent now", or when the orchestrator routes phase=stage-launch. payload_generator.py emits the four ordered payloads; launch_script_writer.py writes launch.sh that reads $ANTHROPIC_API_KEY at runtime and never embeds it; payload_validator.py runs a pre-launch check including an API-key-leak scan. No tool in this skill makes network calls — the user runs launch.sh themselves. Distinct from interview (planning) and grade-iterate (the outcome loop).

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Use when writing or reviewing asynchronous C++ networking code with Boost.Asio or standalone Asio — TCP/UDP servers and clients, SSL/TLS, timers, strands, io_context, co_spawn, awaitable, async_read/async_write, asio::spawn, yield_context, or pre-C++20 completion-handler callbacks.

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Persistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; Kiro skill instructions and steering state read selected project planning context. Recovery reads project planning files and their timestamps only, not agent transcript stores. This adapter registers no Stop hook, never requests continuation, and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.

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。

Persistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; agent instructions read selected project planning context when invoked. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. This adapter registers no lifecycle or Stop hook, never requests continuation, and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.

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。

Persistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. Optional gated mode can request continuation only when the host supports it and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.

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。