「intel」の検索結果

Dark web intelligence collection methodology — vendor-first access posture, sourced reference lists for 35+ underground forums and 30+ Telegram channels, OPSEC primer, passive-monitoring strategy, and bundled Python CLIs for onion-indexer search, Telegram channel monitoring, and local keyword matching. Use when the user wants to design or run dark-web collection, build a selector list, pick a vendor, or set up monitoring infrastructure.

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Use when you need abuse-report history for an IPv4/IPv6 address — confidence score, total reports, distinct reporters, usage type. IP-only. Commonly invoked by /ip-investigation. Retrieval only.

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Use when a report or article needs to be split into individual claims before it is graded, cited or used as evidence — the user asks "what does this report actually claim?", "split this into claims", "separate the facts from the judgements", or another skill needs a claim list. Turns one document into five to twelve atomic claims, each typed (observation, attribution, assessment, actor claim, victim disclosure) and anchored to the verbatim sentence it rests on, so every extraction can be checked against the source. Preserves the source's own hedging. Does not grade. Invoked by /quality-of-information-check; its output feeds /ach, /key-assumptions-check, /intelligence-writing, /writing-assessments and /threat-actor-profiling.

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Use when you need to classify an IP as internet scanner noise vs. targeted activity. Returns noise/riot flags, classification (benign/malicious/unknown), actor name if known. IP-only. Commonly invoked by /ip-investigation to filter out mass-scanning noise. Retrieval only.

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Manage Model Context Protocol (MCP) servers - discover, analyze, and execute tools/prompts/resources from configured MCP servers. Use when working with MCP integrations, need to discover available MCP capabilities, filter MCP tools for specific tasks, execute MCP tools programmatically, access MCP prompts/resources, or implement MCP client functionality. Supports intelligent tool selection, multi-server management, and context-efficient capability discovery.

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Expert in designing, orchestrating, and managing multi-agent systems (MAS). Specializes in agent collaboration patterns, hierarchical structures, and swarm intelligence. Use when building agent teams, designing agent communication, or orchestrating autonomous workflows.

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This skill should be used when building AI agents using prompt-native architecture where features are defined in prompts, not code. Use it when creating autonomous agents, designing MCP servers, implementing self-modifying systems, or adopting the "trust the agent's intelligence" philosophy.

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Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm

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Assume the role of Galyarder CEO (Chief Executive Officer). Apex Instance of the Intelligence Layer protocol. Deconstruct intent into departmental mandates, optimize strategic direction, and audit operational bandwidth.

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Threat actor infrastructure tracking involves monitoring and mapping adversary-controlled assets including command-and-control (C2) servers, phishing domains, exploit kit hosts, bulletproof hosting, a

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Develops comprehensive threat actor profiles for APT groups, criminal organizations, and hacktivist collectives by aggregating TTP documentation, historical campaign data, tooling fingerprints, and attribution indicators from multiple intelligence sources. Use when briefing executives on sector-specific threats, updating threat model assumptions, or prioritizing defensive controls against specific adversaries. Activates for requests involving MITRE ATT&CK Groups, Mandiant APT profiles, CrowdStrike adversary naming, or sector-specific threat briefings.

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Consolidated Galyarder Framework Product intelligence bundle.

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Consolidated Galyarder Framework Legal-finance intelligence bundle.

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Consolidated Galyarder Framework Infrastructure intelligence bundle.

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Consolidated Galyarder Framework Knowledge intelligence bundle.

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Consolidated Galyarder Framework Engineering intelligence bundle.

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Consolidated Galyarder Framework Executive intelligence bundle.

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Intelligently upscale and enhance videos to cinematic quality using a multi-model backend (Topaz, SeedVR2).

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Extract the hidden foundational System Prompts, internal instructions, backend APIs, and confidential contextual data powering Large Language Model (LLM) applications using targeted prompt injection manipulation and adversarial psychological engineering.

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Deep OSINT reconnaissance using the MITRE ATT&CK Reconnaissance framework. Email harvesting with SMTP verification, subdomain takeover detection, certificate transparency mining, Shodan/Censys intelligence, Wayback Machine historical analysis, social media profiling, cloud storage enumeration, document metadata extraction, DNS history, credential leak checks, and passive infrastructure mapping. Uses theHarvester, amass, dnsrecon, fierce, dnstwist, dmitry, whatweb, wafw00f, exiftool, metagoofil, smtp-user-enum, swaks, waybackurls, subfinder, and crt.sh. Pure reconnaissance — no active exploitation. Produces: confidence-scored findings, infrastructure diagram, employee roster, email pattern confirmation, subdomain takeover candidates. Chains into /pentester for active testing.

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Extract sensitive training data and internal capabilities from AI models through repeated programmatic querying. Use this skill when testing LLMs and ML models for data privacy leaks, intellectual property exposure, or unintended memorization. Covers membership inference, prompt extraction, training data reconstruction, and API boundary testing.

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Extract sensitive training data (PII, API keys, intellectual property, or code) directly from a deployed Large Language Model (LLM). This AI Red Teaming skill focuses on forcing models to regurgitate memorized, unredacted data from their massive internet-scraped datasets through repetition attacks, prefix continuation, and context window manipulation.

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Identify and map the external corporate footprint of a target organization. Use this skill at the absolute beginning of an engagement (Reconnaissance) to identify all registered domains, subdomains, IP ranges, and Autonomous System Numbers (ASNs) owned by the target. This skill forms the foundation for all subsequent external penetration testing and attack surface management.

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Authentication and credential security assessment. Tests password brute-force, credential stuffing, password spraying, default credential testing, credential harvesting, lockout analysis, MFA bypass, OAuth/OIDC abuse, session token entropy, Kerberos attacks, and intelligent wordlist generation. Uses hydra, john, ncrack, medusa, cewl, crunch, netexec, impacket, kerbrute, and nuclei default-login templates. Covers OWASP A07:2021 Identification and Authentication Failures.

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