6,891 件 ・ 関連度順
Detects anomalous authentication patterns using UEBA analytics, statistical baselines, and machine learning models to identify impossible travel, credential stuffing, brute force, password spraying, and compromised account behaviors across authentication logs. Activates for requests involving authentication anomaly detection, login behavior analysis, UEBA implementation, or suspicious sign-in 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。
This skill outlines methodologies for performing authorized penetration testing against AWS, Azure, and GCP cloud environments. It covers understanding the shared responsibility model for testing scope, leveraging cloud-specific attack tools like Pacu and ScoutSuite, exploiting IAM misconfigurations, testing for SSRF to cloud metadata services, and reporting findings aligned to MITRE ATT&CK Cloud matrix.
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。
OpenCTI is an open-source platform for managing cyber threat intelligence knowledge, built on STIX 2.1 as its native data model. This skill covers building an automated IOC enrichment pipeline using O
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。
Single entry point for all AEM as a Cloud Service Workflow skills. Covers workflow model design, custom process step and participant chooser development, launcher configuration, workflow triggering, and production support including debugging stuck/failed workflows, triaging incidents with Cloud Manager logs, thread pool analysis, and Sling Job diagnostics for the Granite Workflow Engine.
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。
Survey available blocks from local AEM Edge Delivery Services project and Block Collection to understand the block palette available for authoring. Returns block inventory with purposes to inform content modeling decisions.
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 when asked to run, read, or interpret react-doctor output on the Namias portfolio. Covers the npm scripts, the doctor.config.json schema, the score model, the 12-rule catalog, and which findings are real vs false-positive by design.
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。
Optimized workflows for OpenCode IDE with MiMo v2.5 free model
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。
Create, edit, fix, or explain Mermaid diagrams for relationships, decision branches, interactions, lifecycles, and data models; also use when the parent selects a diagram to materially clarify an explanation. Do not trigger for simple status, one fact, short lists, quantitative charts, or marketing slides.
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。
Perform a bounded security design/hardening pass for explicit security or threat-model intent, or a concrete auth/authorization/secret/untrusted-input/file-upload/sensitive-data risk; do not trigger for every input, data store, third-party call, implementation, review, or release.
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。
AI regression scouting routing. Use when agents, prompts, skills, model/tool routing, harness fixtures, or generated-output expectations change and need regression scenarios; do not use for ordinary product-code regressions.
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。
How to write modern Swift well — modeling with value types, Swift 6 data-race safety and approachable concurrency (@concurrent, main-actor-by-default, actors, task groups), protocols and generics (some vs any), API design, performance and ARC, Swift Testing, macros, and the modern language features agents don't know about yet. Use when writing, reviewing, or migrating Swift, or when a concurrency error, a hang, a data race, a retain cycle, or a performance problem needs fixing.
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。
Design, validate, and plan a startup from scratch. Covers market research, competitive analysis, business model, brand identity, product definition, financial projections, and validation experiments. Trigger when the user has a startup idea to explore, wants to validate a business concept, needs a business plan or lean canvas, asks for market sizing or competitive landscape, wants brand positioning or go-to-market strategy, or says anything like "I have an idea for..." or "is this idea worth pursuing". Also handles resuming from a previous checkpoint.
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。
Builds Model Context Protocol (MCP) servers for Claude with tools, resources, and prompts. Use when users request "create MCP server", "build Claude tool", "MCP integration", or "custom Claude tools".
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。
Creates comprehensive load test plans with realistic scenarios, traffic models, k6 scripts, and success criteria. Use for "load testing", "performance testing", "capacity validation", or "stress testing".
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。
Elite frontend image-direction skill for generating premium, conversion-aware website design references. CRITICAL OUTPUT RULE — generate ONE separate horizontal image FOR EVERY section. A landing page with 8 sections produces 8 images. Never compress multiple sections into one image. Enforces composition variety (not always left-text / right-image), background-image freedom, varied CTAs, varied hero scales (giant / mid / mini minimalist), narrative concept spine, second-read moments, and a single consistent palette across all images. Optimized for landing pages, marketing sites, and product comps that developers or coding models can accurately recreate.
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。
Builds repeatable evaluation systems with golden datasets, scoring rubrics, pass/fail thresholds, and regression reports. Use for "LLM evaluation", "testing AI systems", "quality assurance", or "model benchmarking".
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。
Survey a codebase's animation and motion code as a senior motion advisor, then produce a prioritized audit and self-contained implementation plans for other agents (or cheaper models) to execute. Read-only on source code — it plans improvements, it does not apply them. Use when the user asks to "improve the animations", "audit the motion", "make this app feel better", or wants a roadmap of animation fixes rather than a review of a single diff.
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。
Turn a project directory or a website URL into a short, shareable launch video with music, motion, and share copy. One file, no bundled assets — built entirely by the model with the tools already on the machine. Use when someone says "/brag-slim", "let's /brag about this", "brag about <url>", "make a launch video", or wants to show off what they built. If the /brag skill is also installed, let /brag handle those phrases; it hands off here on Opus 5.5.
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。
Reduces LLM costs and improves response times through caching, model selection, batching, and prompt optimization. Provides cost breakdowns, latency hotspots, and configuration recommendations. Use for "cost reduction", "performance optimization", "latency improvement", or "efficiency".
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。
Implements secure authentication patterns including login/registration, session management, JWT tokens, password hashing, cookie settings, and CSRF protection. Provides auth routes, middleware, security configurations, and threat model documentation. Use when building "authentication", "login system", "JWT auth", or "session management".
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 when AI contribution metadata must match the actual workflow, when model alias and dated identifiers need auditing, when verified and fabricated citation counts feed a release decision, or when a venue's AI disclosure statement needs evidence behind it.
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。
Complete bug bounty workflow — recon (subdomain enumeration, asset discovery, fingerprinting, HackerOne scope, source code audit), pre-hunt learning (disclosed reports, tech stack research, mind maps, threat modeling), vulnerability hunting (IDOR, SSRF, XSS, auth bypass, CSRF, race conditions, SQLi, XXE, file upload, business logic, GraphQL, HTTP smuggling, cache poisoning, OAuth, timing side-channels, OIDC, SSTI, subdomain takeover, cloud misconfig, ATO chains, agentic AI), LLM/AI security testing (chatbot IDOR, prompt injection, indirect injection, ASCII smuggling, exfil channels, RCE via code tools, system prompt extraction, ASI01-ASI10), A-to-B bug chaining (IDOR→auth bypass, SSRF→cloud metadata, XSS→ATO, open redirect→OAuth theft, S3→bundle→secret→OAuth), bypass tables (SSRF IP bypass, open redirect bypass, file upload bypass), language-specific grep (JS prototype pollution, Python pickle, PHP type juggling, Go template.HTML, Ruby YAML.load, Rust unwrap), and reporting (7-Question Gate, 4 validation ...
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。
Local-tooling companion to the bug-bounty orchestrator — carries the SAME complete bug-bounty workflow, but reach for THIS variant when you also need to resolve where tools, wordlists, and clones are installed on the local machine (jhaddix, SecLists, trufflehog, ffuf, dalfox, ghauri); for pure orchestration/routing use the bug-bounty skill. Workflow it covers — recon (subdomain enumeration, asset discovery, fingerprinting, HackerOne scope, source code audit), pre-hunt learning (disclosed reports, tech stack research, mind maps, threat modeling), vulnerability hunting (IDOR, SSRF, XSS, auth bypass, CSRF, race conditions, SQLi, XXE, file upload, business logic, GraphQL, HTTP smuggling, cache poisoning, OAuth, timing side-channels, OIDC, SSTI, subdomain takeover, cloud misconfig, ATO chains, agentic AI), LLM/AI security testing (chatbot IDOR, prompt injection, indirect injection, ASCII smuggling, exfil channels, RCE via code tools, system prompt extraction, ASI01-ASI10), A-to-B bug chaining (IDOR→auth bypass...
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。
LLM bugs are only worth reporting when they cross a trust boundary you can prove — an OOB callback, a verbatim-reproducible secret, a cross-tenant record, or code execution. A model 'saying something bad once' is confabulation, not a vulnerability. Read the False-Positive Gate before claiming anything.
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。