2,350 件 ・ 関連度順
概要と使いどころ
Design a multi-chain holdings workflow on Alchemy Portfolio APIs with explicit pagination, partial-failure, privacy, and reconciliation contracts. Use when building wallet portfolio views. Trigger with "Alchemy portfolio", "multi-chain token balances", or "wallet holdings dashboard".
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。
Organization-grade cloud and supply-chain attack-surface discovery: S3/GCS/Azure Blob bucket discovery via observed-name mining (CNAME/cert-SAN/Wayback) and bounded two-class permutation (6 prefixes x 15 suffixes on trusted tokens, bounded target-bound expansion on subdomain stems), existence (HEAD/GET) vs public-listing confirmation, object-key triage into 9 value tiers (database dumps, credentials, IaC state, kubeconfig, VCS dirs, config, archives, PII, logs), dangling-CNAME bucket-takeover detection, and the ownership-gated severity model that stops an unattributable public bucket from becoming a false CRITICAL; the fully offline AWS-account-ID recovery from a leaked AKIA/ASIA/AROA access key (base32 decode, runnable stdlib Python, canonical test vector, AWS-documentation-example-ID screening); dependency-confusion confirmation for npm/PyPI (internal-signal classifier -- private-registry binding vs org-namespace match -- paired with a read-only public-registry 404 check and the npm scope-claimability nuance the public search API misses); and passive cloud-native/container/Kubernetes/CI control-plane fingerprinting (Lambda URLs, API Gateway, Cloud Run, App Service, kubelet/etcd/K8s API/dashboard, Jenkins/GitLab/Argo CD) as an org-attribution and exposure surface. Passive/discovery only -- no exploitation, no credential submission, no active control-plane confirmation (a stage-6 validate_cloud active tier is described but out of scope). Use when enumerating a target's cloud storage footprint, recovering an AWS account ID from a leaked key, confirming a supply-chain dependency-confusion vector, or fingerprinting cloud-native/K8s/CI infrastructure for an authorized external recon engagement.
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。
Verify skillshare on a real Windows guest running in local UTM: build a pinned commit in the devcontainer, push it into the VM, and either run the Windows E2E runbook with full and basic-user tokens or hand the maintainer a one-line hands-on setup. Use this whenever a change touches Windows links, junctions, file symlinks, Developer Mode, copy fallback, Windows paths, or the dashboard on Windows, or when the user asks to test or accept something on Windows / UTM.
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。
Work on Grida's Gateway product consumer, scoped native AI tokens, installed gg provider and Desktop integration. Use for product execution adapters, not standalone Gateway service implementation or billing policy.
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。
Author SVG figures for Grida docs — diff-able, version-controlled vector diagrams embedded in doc pages instead of screenshots. Provides reusable primitives (selection chrome, size badges, anchor pins, resize cursors, click ripples), color/typography tokens, a starter template, and finished examples to crib from. Canvas user docs (docs/editor/) are the first consumer; the kit is meant to generalize to any product's docs. Use when drawing diagrams that explain UI behaviour — gestures, alignment, before/after states — that a screenshot alone can't capture. Trigger phrases: "svg diagram", "draw a figure", "visual for docs", "explain this gesture visually", "before/after diagram".
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 prompts for AI image tools to produce modern ML/RL paper-style figures matching the aesthetic of recent ICLR/NeurIPS/ICML 2024-2025 publications. Key traits — pure white canvas, white panels with soft drop shadow, rounded friendly font (Nunito/Poppins), densely packed but uncluttered floating elements, small pastel token squares, rich inline illustrations, colored keyword text, pill-shaped concept labels. Trigger phrases — "pastel风格论文配图", "现代ML论文配图", "modern ML figure prompt".
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 working with *.excalidraw or *.excalidraw.json files, user mentions diagrams/flowcharts, or requests architecture visualization - delegates all Excalidraw operations to subagents to prevent context exhaustion from verbose JSON (single files: 4k-22k tokens, can exceed read limits)
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。
Linux lateral movement playbook. Use after gaining initial access to pivot across Linux hosts via SSH hijacking, credential harvesting, internal pivoting, D-Bus exploitation, sudo token reuse, and shared filesystem abuse.
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。
PHP type juggling and weak comparison (`==`) bypass. Use when authentication, HMAC/signature checks, or token validation uses loose equality, numeric coercion, or hash comparisons without strict types — common in legacy PHP and CTF-style code 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。
Windows local privilege escalation playbook. Use when you have low-privilege shell access on Windows and need to escalate via token abuse, Potato exploits, service misconfigurations, DLL hijacking, UAC bypass, or registry autoruns.
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。
OAuth and OIDC misconfiguration testing playbook. Use when reviewing redirect URI handling, state and nonce validation, PKCE, token audience, callback binding, and identity-provider trust flaws.
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。
API authentication and JWT abuse playbook. Use when testing bearer tokens, API keys, claim trust, header spoofing, rate limits, and API auth boundary weaknesses.
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。
Dangling markup injection playbook. Use when HTML injection is possible but JavaScript execution is blocked (CSP, sanitizer strips event handlers, WAF blocks script tags) — exfiltrate CSRF tokens, session data, and page content by injecting unclosed HTML tags that capture subsequent page content.
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。
Entry P1 category router for API security. Use when choosing between API recon, authorization, token abuse, and hidden-parameter workflows before any deeper API topic 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。
Authentication bypass testing playbook. Use when assessing login flows, password reset logic, account recovery, MFA bypass, token predictability, brute-force resistance, and session boundary flaws.
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 attack pattern playbook. Use when analyzing flash loan attacks, price oracle manipulation, MEV sandwich attacks, governance exploits, bridge vulnerabilities, and token standard edge cases in decentralized finance 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。
HTTP Parameter Pollution (HPP): duplicate query/body keys parsed differently by servers, proxies, WAFs, and app frameworks. Use when filters and application layers disagree on which value wins, enabling bypass, SSRF second URL, logic abuse, or CSRF token confusion.
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 and fix visual hierarchy, spacing, color, and depth in web UIs. Use when the user mentions "my UI looks off" (or amateur/unprofessional), "fix the design", "Tailwind styling", "color palette", "visual hierarchy", "design system", "spacing scale", or "component styling". Also trigger when building consistent design tokens, creating dark mode themes, improving data-visualization clarity, or polishing UI details before launch. Covers grayscale-first workflow, constrained design scales, shadows, and component styling. For typeface selection, see web-typography. For usability audits, see ux-heuristics.
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。
Scaffold a new PortalJS data portal from a brief. Copies the canonical template from examples/portaljs-catalog and substitutes project tokens. Use when starting a brand-new data portal project from scratch.
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 building UIs leveraging the WordPress Design System (WPDS) and its components, tokens, patterns, etc.
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 context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.
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。
Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.
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。
Package entire code repositories into single AI-friendly files using Repomix. Capabilities include pack codebases with customizable include/exclude patterns, generate multiple output formats (XML, Markdown, plain text), preserve file structure and context, optimize for AI consumption with token counting, filter by file types and directories, add custom headers and summaries. Use when packaging codebases for AI analysis, creating repository snapshots for LLM context, analyzing third-party libraries, preparing for security audits, generating documentation context, or evaluating unfamiliar codebases.
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。
Add file-based observability, tracing, and run analysis to LLM/agent experiments — per-turn transcripts, tool calls and results, reasoning traces, token/cost accounting, per-episode artifacts, plus terminal viewers and a run-comparison report. Platform-agnostic: one small adapter covers OpenAI, Gemini/Vertex, Anthropic, LiteLLM, Tinker, vLLM, or any custom inference stack, and the same tooling then works unchanged. Use this skill whenever the user wants observability, tracing, logging, or run tracking for model experiments; asks why an agent failed a task or wants to replay/debug/diff an episode; wants to compare models, arms, or ablations; wants to instrument a new folder, repo, or platform ("add observability here", "do the same for Tinker/Gemini/OpenAI"); wants to import or tie in existing experiment results so old runs show up alongside new ones; or wants eval runner scripts where many models share one protocol. Reach for this even when they only say "log what the model is doing", "I can't tell what happened in this run", or "set up traces for this experiment".
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。