1,095 件 ・ 関連度順
概要と使いどころ
Transform git commit history into polished user-facing changelogs and release notes. Use when the user wants a CHANGELOG.md from a version tag or date range, weekly product updates, app store release notes, or Keep a Changelog formatted output with internal noise filtered out.
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 a whole-corpus, read-only audit of tracked stablecoin static metadata and domain sidecars, especially after catalog changes or when factual or internal-consistency drift needs 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。
Use when designing or changing a component's public API, its composition, or its state model in libs/ui-react or libs/ui-rnative — layering (core vs internal vs primitives), BaseProps/Props splits, converting a component to compound / changing its composition with createSafeContext, controlled/uncontrolled state, prop-naming conventions, and cross-platform API parity. Load this before shaping a new component, changing its props, or refactoring its composition.
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 researching external libraries/frameworks (React, Radix, Tailwind, etc.) — reach for the Context7 MCP to get up-to-date docs. Not for internal codebase or design-system-token questions.
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。
Analyzes Rust-compiled malware by detecting the Rust toolchain signature, demangling Rust v0/legacy symbol names, and identifying crate dependencies from embedded paths. Activates for requests to analyze Rust malware, demangle Rust symbols, or identify a Rust binary build and its crates.
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Reverses .NET/managed malware: decompiling MSIL back to C#, defeating common .NET protectors and string encryptors, and tracing reflection-based loaders to recover the real payload. Activates for requests to analyze a .NET sample, decompile MSIL, or unpack a managed loader.
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。
Analyzes Go-compiled malware by recovering function names from the pclntab, detecting the Go build/version string, and listing embedded package paths to overcome stripped symbols. Activates for requests to analyze Go malware, recover Go function names, or identify a Golang binary build.
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。
Reviews AudioUnit v2/v3 plugin implementations for spec compliance, thread safety, and correctness. Use when the user asks to review an AudioUnit plugin, check an AUv3 app extension, audit property or parameter handling, or asks "is my AUComponent thread-safe?" or "why does my AU crash in Logic?". Trigger on phrases like "review my AudioUnit", "check my AUv3", "is my render block safe?", or when you see AURenderCallback, internalRenderBlock, kAudioUnitProperty_*, or AUAudioUnit in the code.
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N-round adversarial review pipeline for empirical research output — the chain from data to LaTeX tables to a manuscript that cites them. A Claude drafter proposes minimal diffs, a deterministic mechanical battery gates every diff from a clean state with a regression gate, a Codex reviewer files check-backed critiques, and a blind judge panel decides residual disputes. Manual-invoke ONLY: trigger when the user explicitly runs /adversarial-empirical-review or names 'adversarial-empirical-review' / 'adversarial empirical review'. Do NOT auto-trigger on generic 'review my results', 'check my tables', or manuscript-editing requests. For prose-style refinement use style-emulation instead; this skill AUDITS WHETHER THE TABLES ARE CORRECT — that each number in the tables is what the analysis code computes, reproduces from the data, and is internally consistent. It is an empirical + code review: the manuscript is read only to resolve table numbering, and prose is not examined.
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 player-facing patch notes from git history, sprint data, and internal changelogs. Translates developer language into clear, engaging player communication.
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。
Reviews a game design document for completeness, internal consistency, implementability, and adherence to project design standards. Run this before handing a design document to programmers.
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。
Auto-generates a changelog from git commits, sprint data, and design documents. Produces both internal and player-facing versions.
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 behavior-preserving frontend refactoring focuses on conditional complexity, repeated branches, guards, special cases, or internal invariants, and the selected technique needs family-specific mechanics, risks, and JavaScript/TypeScript/React/Vue adaptation.
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Use when choosing, implementing, reviewing, or refactoring frontend state ownership across React, Vue, Next.js, Nuxt, URL state, server state, form state, browser persistence, or global stores. Prefer narrower skills for TanStack Query cache details, browser storage persistence, or form validation internals; Chinese triggers include state management, state ownership, store selection.
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。
Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.
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。
Develop research proposals with solicitation alignment, reviewer-focused logic, evidence-bounded preliminary results, milestones, risks, and compelling figures. Use for federal, foundation, equipment, partnership, or internal research proposals.
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 task needs the judgment of a Credit Analyst — spreading financial statements and computing leverage/coverage ratios, writing a commercial credit memo, assigning or recommending an internal risk rating, structuring covenants on a term loan or revolver, or stress-testing a borrower's debt service under a downside case. Narrower than loan-officer (originates and manages the client relationship; the analyst underwrites the credit that officer brings in) and financial-risk-specialist (owns portfolio-level risk and concentration limits, not single-borrower underwriting).
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 task needs the judgment of an Administrative Services Manager — evaluating a vendor contract on total cost of ownership rather than price, right-sizing a facilities/admin budget against actual utilization, anticipating where an operational process breaks at the org's next size milestone, or diagnosing why internal operational support is failing a growing organization.
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 task needs the judgment of an accountant/financial controller — closing the books on a fixed calendar, resolving a bank or account reconciliation variance, evaluating a revenue-recognition or substance-over-form question, designing or testing an internal control, or triaging a discrepancy in financial records before it reaches the auditors.
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 SEO analysis for any website or business type. Orchestrates 52 sub-skills (52 active): full site audits, technical SEO (CWV/INP), schema, E-E-A-T content quality, images, sitemaps, GEO for AI Overviews, llms.txt generation, AI crawler audit, plus live GSC and Ahrefs MCP data. GSC: overview, drops, opportunities, brand-vs-nonbrand, cannibalization, compare, content-decay, index-issues, new-keywords. Ahrefs: overview, backlinks, keywords, competitors, content-gap, broken-links, new-links, anchor-analysis, dr-history, top-pages. Cross-MCP: serp, content-brief, brand-radar, site-audit-pro, report. Local: log-analysis, ai-content-check, internal-links, local, migration-check, obsidian-kb. AI Readability: llms-txt, robots-ai. Performance: lighthouse-audit, core-web-vitals, page-experience. Quality & AI Search: rich-results, ai-optimization, eeat-audit, helpful-content. Triggers on: "SEO", "audit", "schema", "Core Web Vitals", "sitemap", "E-E-A-T", "AI Overviews", "GEO", "technical SEO", "content quality", "structured data", "GSC", "Ahrefs", "backlinks", "keywords", "search console", "domain rating", "local SEO", "migration", "lighthouse", "pagespeed", "LCP", "INP", "CLS", "page experience", "interstitials", "mobile friendly", "rich results", "rich snippets", "schema eligibility", "AI optimization", "AI search readiness", "EEAT", "trust signals", "helpful content", "people-first", "content quality gate".
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。
Programmatic SEO planning and analysis for pages generated at scale from data sources. Covers template engines, URL patterns, internal linking automation, thin content safeguards, and index bloat prevention. Use when user says "programmatic SEO", "pages at scale", "dynamic pages", "template pages", "generated pages", "data-driven SEO", "pSEO", "template engine", "data-driven pages", "index bloat", or "thin content at scale".
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 Dockerfiles, configures CI/CD pipelines, writes Kubernetes manifests, and generates Terraform/Pulumi infrastructure templates. Handles deployment automation, GitOps configuration, incident response runbooks, and internal developer platform tooling. Use when setting up CI/CD pipelines, containerizing applications, managing infrastructure as code, deploying to Kubernetes clusters, configuring cloud platforms, automating releases, or responding to production incidents. Invoke for pipelines, Docker, Kubernetes, GitOps, Terraform, GitHub Actions, on-call, or platform engineering.
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 AI chatbots end to end — conversation design, persona, retrieval grounding, escalation, and deployment. Use when creating a chatbot for support, sales, or internal assistance.
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。
Configure the complete Google Play Store publishing pipeline — upload key generation, AAB bundle optimization, Fastlane supply lanes for internal/beta/production tracks, staged rollout strategy, store listing metadata, data safety form guidance, and content rating preparation
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。