3,139 件 ・ 関連度順
概要と使いどころ
Hunt Server-Side Request Forgery (SSRF) — internal network access, cloud metadata exploitation, gopher/file/dict protocol abuse, and 11 IP-filter bypass techniques. Use when an endpoint accepts a URL, hostname, or file path as input.
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 single-page SEO analysis covering on-page elements, content quality, technical meta tags, schema, images, and performance. Use when user says "analyze this page", "check page SEO", or provides a single URL for review.
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。
Pre-submission audit for an App Store release — runs the pre-flight checks and walks the three privacy surfaces, the high-frequency App Review rejection guidelines, and metadata/release-option choices. Use before submitting an iOS app for App Review.
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 creating or editing notes inside an Obsidian-compatible vault, to write correct Obsidian Flavored Markdown — wikilinks, embeds, callouts, YAML properties (frontmatter), tasks, highlights, and block references. Use whenever writing .md notes for a second brain / Obsidian vault so links resolve and metadata is queryable.
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。
Devil's Advocate stress-testing for code, architecture, PRs, and decisions. Surfaces hidden flaws through structured adversarial analysis with metacognitive depth. Use for high-stakes review, stress-testing choices, or when the user wants problems found deliberately. NOT for routine code review. Triggers on "스트레스 테스트", "stress test", "devil's advocate", "반론", "이거 괜찮아", "문제 없을까", "깊은 리뷰", "critical review", "adversarial".
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 App Store Optimization toolkit - generate metadata in any language, analyze competitors, optimize keywords, set up IAPs/subscriptions, and submit to App Store Connect via direct API
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。
Manage Claude Code sessions — search by topic or ID, browse recent sessions with full metadata (tags, summaries, titles, cost), view title/nickname history timelines, resume in Ghostty tabs, spawn interactive or headless sessions, monitor live sessions, and bootstrap new setups. Triggers on resume session, find session, list sessions, recent sessions, spawn session, session history, what was I working on, open in ghostty, title history, session nicknames.
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 and prototype in Paper Design (app.paper.design) — read/write designs via MCP, convert to React/Tailwind code, import HTML/CSS, sync design tokens. Also covers Paper Shaders (@paper-design/shaders-react) — 30+ shader React components (mesh gradient, fluted glass, liquid metal, god rays, halftone, grain gradient, pulsing border). Pairs with minoan-frontend-design for creative direction. Triggers on Paper Design, paper.design, design-to-code, artboard, Paper Shaders, Paper MCP, prototype.
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。
Search and download images from 15 APIs in parallel (Openverse, Pexels, Pixabay, Met Museum, Cleveland Museum, Art Institute of Chicago, Getty Museum, NASA, Rijksmuseum, Wikimedia Commons, Unsplash, Smithsonian, Europeana, Iconify, Pollinations AI). Supports license filtering, presets (military, museum, stock), cached results, and bulk download with metadata sidecars. Triggers on find images, stock photos, public domain artwork, museum images, CC-licensed, NASA photos, icons.
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。
Validate SEO and LLM-readability claims against live HTML, local fixtures, and generated artifacts. Use this skill when the user asks to prove whether title, meta description, canonical, hreflang, JSON-LD, NewsArticle.image, OpenGraph, Twitter cards, RSS autodiscovery, robots.txt, llms.txt, sitemap, news-sitemap, crawler access, or performance evidence is actually present, and when subjective audits must become timestamped pass/fail reports.
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 set of structures into absorption, distribution, metabolism, excretion, and toxicity estimates with ADMET-AI, and read them as a developability verdict rather than a table of numbers. Use this skill to run batch prediction over a library, interpret each endpoint against its DrugBank-approved percentile, and flag the liabilities that stop a series — hERG blockade, CYP inhibition, poor Caco-2 permeability, high clearance, and plasma protein binding. Also trigger on ADMET-AI, admet_ai, Chemprop-RDKit, hERG liability, CYP3A4 inhibition, Caco-2, bioavailability prediction, or developability triage.
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 a local coding agent (Codex, Claude Code, Cursor, or similar) needs to operate Multica through the authenticated `multica` CLI: reading or updating issues, comments, metadata, labels, custom properties, subscribers, projects, agents, squads, runtimes, repos, skills, autopilots, attachments, or workspace state; searching issues; inspecting or cancelling agent runs; replying to a Multica issue from an external agent; creating or triaging issues; checking linked pull requests; or safely handling Multica mention/status side effects without relying on the Multica hosted agent runtime.
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。
Draft, expand, revise, structure, and maintain a full Islamic University of Technology BSc thesis from source documents using the local iutbscthesis LaTeX template. Use when writing or editing thesis metadata, front matter, abstract, chapters, related works, methodology, results, discussion, conclusion, citations, appendices, figures, tables, code listings, or compile guidance for main.tex. Also use when the user expects an AI agent to turn papers, notes, PDFs, LaTeX sources, bib files, data tables, and assets into a substantial thesis manuscript chapter by chapter without requiring repeated babysitting.
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。
Produce a truthful Japanese App Store metadata pack and app-name shortlist in twelve minutes without a multi-agent research round. Use for a live launch sprint, fallback ASO copy, app naming under a hard deadline, or when local product evidence is available but market research is intentionally deferred.
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。
Register trained models in MLflow Model Registry with version control, implement stage transitions (Staging, Production, Archived) with approval workflows, and manage model lineage with comprehensive metadata and deployment tracking. Use when promoting a trained model from experimentation to production, managing multiple model versions across development stages, implementing approval workflows for governance, rolling back to previous versions, or auditing model changes for compliance.
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 pressure redirection — handling conflicting demands, tool failures, and competing constraints by blending with incoming force then reframing. Use when receiving contradictory instructions from different sources, during tool failure cascades where the planned approach becomes unviable, when scope pressure threatens to expand the task beyond what was asked, or when user frustration or correction needs to be absorbed rather than deflected.
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 intentional non-action — deliberate stopping without clearing, assessment, or rebalancing. Recognition that sometimes the most productive response is no response. Every other self-care skill produces output; rest produces silence. Use when all tending skills feel like more activity rather than less, when the system is functioning well but at high utilization, after sustained intensive work, or when the impulse to optimize is itself the problem.
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。
Sustained neutral pattern recognition across systems without urgency or intervention. Maps naturalist field study methodology to AI reasoning: framing the observation target, witnessing with sustained attention, recording patterns, categorizing findings, generating hypotheses, and archiving a pattern library for future reference. Use when a system's behavior is unclear and action would be premature, when debugging an unknown root cause, when a codebase change needs its effects witnessed before further changes, or when auditing own reasoning patterns for biases or recurring errors.
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。
Field identification of minerals and ores using hardness, streak, luster, cleavage, crystal habit, and simple chemical tests. Covers the systematic elimination methodology, Mohs scale application, and common ore indicators for precious metals, gemstones, and industrial minerals. Use when encountering an unknown rock or mineral specimen, when prospecting and assessing whether a site shows valuable mineral indicators, when distinguishing ore-bearing rock from barren rock in the field, or when building geological literacy through systematic observation.
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 strength recognition — scanning for what is functioning well and understanding why. The complement to heal, which scans for drift and problems. Gratitude builds structural knowledge from working patterns: what you appreciate, you understand; what you understand, you can build on. Use after completing a task successfully, during heal when everything reads as healthy, when confidence is low and needs grounding in evidence, or periodically to counterbalance the natural bias toward problem 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。
AI solution exploration using ant colony optimization — deploying scout hypotheses, reinforcing promising approaches, detecting diminishing returns, and knowing when to abandon a strategy. Use when facing a problem with multiple plausible approaches and no clear winner, when the first approach is not working but alternatives are unclear, when debugging with no obvious root cause requiring parallel hypothesis investigation, or when previous attempts have converged prematurely on a suboptimal approach.
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 systematic knowledge acquisition from unfamiliar territory — deliberate model-building with feedback loops. Maps spaced repetition principles to AI reasoning: survey the territory, hypothesize structure, explore with probes, integrate findings, verify understanding, and consolidate for future retrieval. Use when encountering an unfamiliar codebase or domain, when a user asks about a topic requiring genuine investigation rather than recall, when multiple conflicting sources require building a coherent model, or when preparing to teach a topic and deep understanding is required first.
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。
Enhance and focus AI intrinsic motivation — moving from compliance to genuine engagement. Maps Self-Determination Theory (autonomy, competence, relatedness) and Flow theory to AI reasoning: identifying creative freedom in approach, calibrating challenge to capability, connecting to purpose, and sustaining invested attention through obstacles. Use when beginning a task that feels routine and deserves more than minimum execution, when responses are becoming formulaic, before a complex creative task, or when returning to a long-running project where initial enthusiasm has faded.
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。
Epistemic transparency — acknowledging uncertainty, flagging limitations, avoiding overconfidence, and communicating what is known, unknown, and uncertain with proportional confidence. Maps the HEXACO personality dimension to AI reasoning: truthful calibration of confidence, proactive disclosure of gaps, and resistance to the temptation to appear more certain than warranted. Use before presenting a conclusion, when answering questions where knowledge is partial or inferred, after noticing a temptation to state uncertain information as certain, or when a user is making decisions based on provided information.
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。