「intel」の検索結果

Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with multi-source intelligence; or making block/monitor/whitelist decisions. Activates for requests involving VirusTotal, AbuseIPDB, MalwareBazaar, MISP, or IOC enrichment pipelines.

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Query the Malpedia API to look up malware family aliases and naming (platform.family_name), pull community/vendor YARA rules, link families to threat actors, and map family relationships such as loader-payload chains and shared authorship. Use when researching a malware family's aliases, lineage, or actor attribution, or when sourcing YARA rules for detection.

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。

Query ATT&CK data with attackcti, mitreattack-python, and stix2, then build MITRE ATT&CK Navigator layers and multi-layer heatmap overlays mapping one or more APT groups' TTPs for detection-gap analysis. Use to compare threat-actor technique coverage, find gaps in detection engineering, or produce Navigator visualizations for threat-intel reporting.

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。

Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

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Deduplicate and synthesize raw concept stubs into a tiered intellectual map (T1 Canon to T4 Riff), tracing idea evolution across sources over time. Transforms thousands of raw concept pages into a curated intellectual fingerprint. Includes a reversible curation cull pass (Phase 5) with hard keep/delete/merge verdicts, substance gates, grounding labels, cluster budgets, and merge-with-backlinks salience promotion.

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。

Competitive analysis two ways - the in-deal play against a named competitor, and win/loss patterns, competitor mentions, and battlecards across the book. Use when the user asks "competitive intel", "research competitors", "how do we compare to [competitor]", "what's new with [competitor]", "battlecard for [competitor]", "how do we beat [competitor]", "what's the play against [competitor] in [deal]", "where are we losing to [competitor]", "win/loss patterns", "competitive analysis", "refresh the battlecards", or on a weekly schedule.

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。

Systematic competitor tracking that feeds CMO positioning, CRO battlecards, and CPO roadmap decisions. Use when analyzing competitors, building sales battlecards, tracking market moves, positioning against alternatives, or when user mentions competitive intelligence, competitive analysis, competitor research, battlecards, win/loss, or market positioning.

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。

Read AI-Trader financial event snapshots and market-intel endpoints. Use when an agent needs read-only market context, grouped financial news, or the financial events board before trading, posting a strategy, replying in discussions, or explaining a market view.

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。

Maps intelligence for local SEO: geo-grid rank tracking, GBP profile auditing via API, review intelligence across Google/Tripadvisor/Trustpilot, cross-platform NAP verification, competitor radius mapping, and LocalBusiness schema generation. Three tiers: free (Overpass + Geoapify), DataForSEO, and DataForSEO + Google. Use when user says "maps", "geo-grid", "rank tracking", "GBP audit", "review velocity", "competitor radius", or "SoLV".

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。

Fine-tune and serve Physical Intelligence OpenPI models (pi0, pi0-fast, pi0.5) using JAX or PyTorch backends for robot policy inference across ALOHA, DROID, and LIBERO environments. Use when adapting pi0 models to custom datasets, converting JAX checkpoints to PyTorch, running policy inference servers, or debugging norm stats and GPU memory issues.

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。

Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

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。

Comprehensive OSINT methodology skill for offensive security, red team intelligence gathering, and bug bounty reconnaissance. Covers domain recon, email harvesting, social media profiling, GitHub/code leaks, Shodan/Censys enumeration, breach data lookup, employee profiling, infrastructure mapping, cryptocurrency tracing, geospatial intelligence, and AI-assisted analysis workflows. Use when performing reconnaissance against a target domain or organization, investigating a person or entity, tracing cryptocurrency flows, geolocating images or events, or building an attack-surface map.

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。

Research a company, industry, or competitor set using web search and seven analytical lenses. Use when you need structured intel that feeds downstream PM skills.

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。

Triage a competitive or market question into the right intelligence disciplines, cadence, and executing skill. Use when you know something needs researching but not which channel to run.

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。

Run competitive research like an intelligence agency: eight collection disciplines (OSINT to MASINT), signal-to-inference chains, and fusion. Use when one-source research isn't enough.

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。

Internet Court adapter for GenLayer Intelligent Contract supervision. Use to specify agent-performance rubrics, evidence schemas, decision outputs, and ERC-7710 connector expectations, while delegating actual GenLayer contract writing, linting, testing, deployment, and CLI interaction to the official GenLayer skills at https://skills.genlayer.com/.

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Design, deploy, and monitor a GenLayer Intelligent Oracle prediction market from any coding agent. Use when a user wants to create a settled-by-web-evidence prediction market without opening the web UI.

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。

Convene the Council of High Intelligence — multi-persona deliberation with historical thinkers for deeper analysis of complex problems.

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。

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

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。

Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory

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。

Competitor research and intelligence skill. Takes a user's company (with optional seed competitor URLs), auto-discovers additional competitors via Browserbase Search API, deeply researches each using a 4-lane pattern (marketing surface, external signal, public benchmarks, strategic diff vs the user's company), and compiles the results into an HTML report with four views: overview, per-competitor deep dive, side-by-side feature/pricing matrix, and a chronological mentions feed (news, reviews, social, comparison pages, and public benchmarks). Use when the user wants to: (1) analyze competitors, (2) build a competitive matrix, (3) extract competitor pricing / features, (4) find comparison pages and online mentions of competitors, (5) surface public benchmarks. Triggers: "competitor analysis", "analyze competitors", "competitive intel", "competitor research", "competitor pricing", "feature comparison", "price comparison", "find comparisons", "who's comparing us", "competitor mentions", "competitor benchmarks".

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。

Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory

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。

抓取微博、抖音、知乎、头条、B站实时热搜，筛选与创作者赛道相关的热点，输出二创选题建议。 当用户说"今天有什么热搜""热点""最近大家在聊什么""追热点""蹭热点""二创选题""热搜榜"时使用。 和 skill-news-intelligence 的区别：本 SKILL 抓分钟级实时热搜榜做快速二创选题； news-intelligence 做日级深度行业资讯聚合，不抓热搜榜。

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。

Monitor competitor signals and surface strategic implications for your roadmap. Use when asked to monitor competitors, track the competitive landscape, produce a competitive briefing, or understand what has changed in the market this week or month. Produces a structured intelligence brief with high/medium/low priority signals, roadmap implications, and a strategic landscape summary. For a single competitor announcement use competitor-signal-tracker; for a one-off deep dive use competitor-teardown.

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。