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概要と使いどころ
Reverse JavaScript-based custom DSL/VM interpreters, non-standard WASM-like runtimes, and risk-control engines. Use when analyzing IIFE or switch-based opcode dispatchers, extracting instruction tables, recovering bytecode semantics, capturing VM state at runtime, or reconstructing execution flow.
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。
Use for authorized security assessment of LLM applications and AI agents, including prompt injection, tool abuse, RAG exposure, memory poisoning, and model supply-chain risks.
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。
Build comprehensive attack trees to visualize threat paths. Use when mapping attack scenarios, identifying defense gaps, or communicating security risks to stakeholders.
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。
Plain-English translation layer for non-technical Copilot CLI users. Translates every approval prompt, error message, and technical output into clear, jargon-free English with color-coded risk indicators.
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。
Audit MCP (Model Context Protocol) server configurations for security issues. Use this skill when: - Reviewing .mcp.json files for security risks - Checking MCP server args for hardcoded secrets or shell injection patterns - Validating that MCP package-runner dependencies use exact reviewed versions, not bare names, @latest, or ranges - Detecting mutable npm/npx, bunx, pnpm dlx, yarn dlx, and npm exec references in MCP configurations - Auditing which MCP servers a project registers and whether they're on an approved list - Checking for environment variable usage vs. hardcoded credentials in MCP configs - Any request like "is my MCP config secure?", "audit my MCP servers", or "check .mcp.json" keywords: [mcp, security, audit, secrets, shell-injection, supply-chain, governance]
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。
Evaluate finished work for defects, risk, and simplification before it ships
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。
Apply the firm's KYC/AML rules grid to a parsed onboarding record — assign a risk rating, list every rule outcome with the rule cited, and flag what's missing or escalation-worthy. Use after kyc-doc-parse; this skill decides nothing, it scores and routes.
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。
Generate and publish a project status artifact — an opinionated, tabbed status page for a project too big for one update (overview & success criteria, the workstream sequence, next steps, plus background, plan, risks & open questions, and decisions/FAQ when they earn a tab) — published with the built-in Artifact tool to a default-private claude.ai page the user can share with teammates. Use when a piece of work spans several workstreams and you want a shareable overview kept current. Each artifact is backed by a small per-project config in the plugin data dir, so refreshing it re-gathers live state, redeploys the same URL, and reports only the delta. For software projects whose workstreams are PRs, also read swe.md (the X.Y PR-numbering convention; pulling PR state with gh/git; a per-PR detail block). Needs the built-in Artifact tool (claude.ai login). Not for single-PR changes or public docs.
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。
Deep research framework for pre-IPO / private companies (Ant Group, SpaceX, Stripe, ByteDance...). Six analyst lenses — business model, financial forensics, competitive landscape, risk & governance, tech & IP, alternative-data signals — run in parallel via run_swarm, then cross-validated for signal consistency before any verdict. Built around the core challenge of private-company work: information is scarce, so every data point carries a confidence label (high / medium / low), inference is shown separately from fact, and 'I don't know' is a valid output. Outputs a fair-value range, exit-path analysis, and an information-gap map. Use for any unlisted company where you need to judge what the business is actually worth.
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。
Professional financial research report generation — standard structure (summary / views / main body / risks / recommendation), Markdown formatting standards, rating system, and terminology guide.
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。
Asset allocation theory and optimizer usage — MPT / Black-Litterman / risk budgeting / all-weather strategy, including guides for 5 optimizers and rebalancing rules.
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。
Hedging strategy design (beta hedge / option protection / tail risk / cross-asset hedging), including hedge-ratio calculation and cost evaluation.
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。
Global macro analysis framework (central bank policy transmission / FX forecasting / geopolitical risk / capital flows), used to build macro factor signals that drive cross-asset allocation.
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。
DeFi yield analysis and optimization — lending rates, LP yields, staking returns, yield farming strategies, risk-adjusted yield comparison, and protocol-level sustainability assessment.
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。
The single ROUTER for every data need. Load this skill BEFORE any backtest, data-fetch, or research task to pick the best available source/tool, honour auth (env) requirements, and avoid ban-risk providers.
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。
SEC EDGAR filing analysis — 10-K, 10-Q, 8-K, proxy statements, insider Form 4. Extract key financials, risk factors, management discussion, and generate investment signals from US public company filings.
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。
ADR/H-share/A-share cross-listing premium analysis — track pricing gaps between US-listed ADRs, HK-listed H-shares, and A-shares for arbitrage signals, dual-listing valuation, and delisting risk assessment.
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。
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.
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。
Detect unauthorized SaaS and cloud service usage (shadow IT) by parsing proxy access logs, DNS query logs, and firewall/netflow data with Python pandas to aggregate traffic by domain, classify domains against known SaaS categories, and score risk by data volume and user count. Use when auditing an organization for unsanctioned cloud/SaaS usage or generating a shadow IT discovery report with remediation recommendations.
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。
Detect risky OAuth application consent grants in Azure AD / Microsoft Entra ID using Microsoft Graph API, audit logs, and permission analysis to identify illicit consent grant attacks.
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Audit Azure Blob and ADLS storage accounts for public access exposure, weak or long-lived SAS tokens, missing encryption at rest, disabled HTTPS-only traffic, and outdated TLS versions, using the azure-mgmt-storage Python SDK to generate a risk-scored report. Use when assessing an Azure subscription's storage accounts for misconfiguration, building cloud security posture checks, or investigating a suspected data exposure via public blob access.
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。
Plan and execute authorized vishing (voice phishing) pretext calls to assess employee susceptibility to social engineering and evaluate security awareness controls.
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。
Build a structured SOC escalation matrix defining severity tiers, response SLAs, tiered escalation paths, and notification procedures for security incidents, using context-driven criteria that combine business risk, asset criticality, and data sensitivity. Use when designing or revising how a SOC triages and escalates incidents across analyst tiers.
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 a vulnerability aging dashboard and SLA tracking system that measures time-to-remediation against severity-based deadlines (e.g. 14 days critical, 30 days high, 60 days medium, 90 days low), with automated escalations and compliance metrics reporting. Use when designing SLA policies, building aging/remediation dashboards, or proving compliance with remediation timelines.
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。