「chain」の検索結果

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for Active Directory, Kerberos, LDAP, OAuth, enterprise messaging, Windows host forensics, credential material, and lateral-movement challenges. Use when the user asks to trace tickets or tokens, inspect mailbox rules, analyze Windows host evidence, understand an AD trust path, or explain a lateral-movement chain across sandbox-linked nodes. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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。

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for Linux credential artifacts, service tokens, SSH material, cloud and container secrets, socket-level trust, and host-to-host pivot chains. Use when the user asks to trace Linux auth artifacts, accepted token or key replay, socket or service-account trust edges, sudo or capability abuse, or explain lateral movement across Linux challenge nodes. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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。

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for reverse engineering, malware, DFIR, firmware, pwnable, and native exploit challenges. Use when the user asks to reverse a binary, unpack a sample, inspect a memory dump or PCAP, recover malware behavior, debug a crash, or build or verify an exploit chain under sandbox assumptions. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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。

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for queues, async workers, cron jobs, delayed tasks, retry behavior, worker-only config drift, and payload-to-side-effect chains. Use when the user asks to trace a queue payload, inspect async job execution, explain worker-only behavior, follow retries or dead-letter handling, or connect an enqueued item to a later file, cache, email, or privilege-bearing side effect. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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。

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for CI/CD, registry, dependency drift, artifact provenance, image build, release pipeline, and runtime consumer challenges. Use when the user asks to trace dependency drift, registry pulls, malicious packages, build or release tampering, CI execution, artifact signing, or which shipped artifact the runtime actually consumes. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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。

从逆向走到可用利用 (Working Exploit) 的全链路工程化方法。 适用场景：拿到了二进制 + 漏洞点 + 目标环境，需要写出一个能稳定打通的 exploit（不是只能本地复现一下、远程一打就崩的脚本）。 覆盖三大方向：栈溢出 / 堆利用 / 内核 pwn。强调"CTF 本地通 → 真实远程稳定打通"的工程差距：libc 版本错配、堆喷射时序、SMEP/SMAP/KASLR、栈对齐、远程缓冲。 核心工具链：pwntools + GEF/pwndbg + ROPgadget/Ropper + one_gadget + libc-database + qemu-system 内核调试。 触发关键词：pwn、栈溢出、堆溢出、ROP、ret2libc、ret2csu、one_gadget、libc-database、堆利用、tcache、fastbin、unsorted bin、kernel pwn、kROP、SMEP、SMAP、KASLR、modprobe_path、pwntools、GEF、pwndbg。

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 multi-stage attack-path planning and orchestration when a task spans reconnaissance, initial access, privilege escalation, lateral movement, or impact assessment. Route single-stage tasks directly to their specialist skill.

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。

This skill should be used when the user asks to "optimize a prompt", "improve prompt performance", "design a prompt template", "write better prompts", "debug prompt issues", "use chain-of-thought", "structured prompting", "few-shot prompting", or wants to apply advanced prompt engineering patterns for production LLM applications.

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。

Test smart contracts comprehensively using Hardhat and Foundry with unit tests, integration tests, and mainnet forking. Use when testing Solidity contracts, setting up blockchain test suites, or validating DeFi protocols.

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。

Master smart contract security best practices to prevent common vulnerabilities and implement secure Solidity patterns. Use when writing smart contracts, auditing existing contracts, or implementing security measures for blockchain applications.

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。

Find and fix flake-prone anti-patterns in an existing Cypress suite, and APIs removed in Cypress 12 to 16. Bundles a zero-dependency scanner for fixed cy.wait() delays, assigned command return values, async/await, force: true, chaining after actions, one-shot expect() inside .then(), conditional testing, and removed APIs such as Cypress.env(), cy.exec(), cy.server()/cy.route(). Use when asked to fix flaky Cypress tests, remove cy.wait, stabilize or harden a Cypress suite, audit Cypress specs, debug "detached from the DOM" errors, or prepare an upgrade to Cypress 15 or 16.

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。

Comprehensive guide for configuring and managing GitHub Dependabot. Use this skill when users ask about creating or optimizing dependabot.yml files, managing Dependabot pull requests, configuring dependency update strategies, setting up grouped updates, monorepo patterns, multi-ecosystem groups, security update configuration, auto-triage rules, or any GitHub Advanced Security (GHAS) supply chain security topic related to Dependabot. For pre-commit dependency vulnerability scanning in AI coding agents via the GitHub MCP Server, this skill references the Advanced Security plugin (`advanced-security@copilot-plugins`). Use this skill when an agent needs to scan dependencies for known vulnerabilities before committing.

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。

Authors a Dynamics 365 Finance and Supply Chain Management Solution Blueprint from scratch through a structured, section-by-section architect interview, establishing scope, target operating model, application and data architecture, integration landscape, migration strategy, security model, ALM, testing, deployment, and support approach, with a decision log capturing rationale and rejected alternatives. Use when the user wants to create D365 implementation architecture documentation, start a D365 implementation, design the architecture, prepare a Solution Blueprint, or identify the architectural decisions the programme must make. Do not use for critique of an existing design; that is a review task rather than blueprint authoring.

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。

Help the user pick, write, or apply an AgentRC policy. Policies customise readiness scoring by disabling irrelevant checks, overriding impact/level, setting pass-rate thresholds, or chaining org baselines with team overrides. Use when the user asks about strict mode, AI-only scoring, custom weights, CI gating, or wants org-wide standardisation.

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。

Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)

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。

Mootdx A-share market data via TCP-direct 通达信 servers. RETIRED 2026-10 — TDX servers stopped answering the client protocol, so this source is out of the automatic fallback chain and kept only for explicit `source="mootdx"` use.

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。

Stablecoin supply and flow analysis — USDT/USDC mint-burn signals, exchange stablecoin reserves, on-chain stablecoin velocity, and capital rotation indicators for crypto market timing.

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。

Flag misspelled, brandjacked, and typosquatted package names across npm, PyPI, and crates.io before installation, using edit-distance, keyboard-proximity, and known-target corpus matching with typomania, Microsoft OSSGadget's oss-find-squats, and pypi-scan. Use before adding a new dependency, as a CI/CD gate on pull requests, when reviewing an AI-generated ("slopsquatting") dependency list, or when auditing a lockfile diff for a swapped package.

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 anomalies in Modbus/TCP and Modbus RTU industrial traffic via function code monitoring, register range validation, timing analysis, and deep packet inspection, using Zeek's Modbus analyzer, Suricata IDS with OT rules, and Python Markov chain models of normal transaction sequences. Use for deploying Modbus IDS in OT environments, baselining polling patterns, investigating suspicious Modbus traffic, or building function code allowlists.

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。

Deobfuscates malicious JavaScript found in phishing pages, web skimmers, and dropper scripts by reversing encoding layers, eval chains, string manipulation, and control-flow obfuscation to reveal the original malicious logic. Use when investigating a phishing page's obfuscated JavaScript, analyzing a Magecart-style web skimmer, or deobfuscating a JavaScript dropper that fetches second-stage malware.

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 collaborative forensic incident timelines using Timesketch to ingest, normalize, and analyze multi-source event data (including Plaso output) for attack chain reconstruction and investigation documentation. Use when reconstructing the sequence of events during an incident investigation or when multiple analysts need to jointly tag, annotate, and search a shared DFIR timeline.

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。

Conduct internal Active Directory reconnaissance using BloodHound Community Edition's graph database with the SharpHound (AD) and AzureHound (Entra ID) collectors, mapping ACLs, sessions, and group memberships into attack paths from a low-privileged foothold to Domain Admin. Use after an initial AD foothold to identify privilege escalation chains, or to validate that AD hardening closed known attack paths.

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。