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概要と使いどころ
Use for video-conditioned audio and automated Foley production: adding synchronized sound effects, ambience, impacts, footsteps, cloth, prop, and environmental audio to silent or under-sounded video using video-to-audio models, text-to-sound tools, manual spot lists, editing, rights checks, and QA.
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。
Automated cell type annotation using reference-based methods including CellTypist, scPred, SingleR, and Azimuth for consistent, reproducible cell labeling. Use when automatically annotating cell types using reference datasets.
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。
Automated and marker-guided single-cell cell type annotation using CellTypist, marker review, reference transfer, and confidence-aware label curation.
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。
Semi-automated fact-checking, source credibility evaluation, misinformation detection, prebunking, and evidence-ledger based claim verification. Use when a user asks to verify a claim, article, social post, screenshot, URL, quote, data point, compare sources, detect manipulation, evaluate source reliability, or generate an HTML fact-check card/share-safe correction. Supports text, URLs, screenshots/images, two-source comparison, and topic-level prebunking in Bulgarian, English, Russian, EU policy, health, science, and geopolitical contexts. Trigger phrases: "is this true?", "is this fake?", "fact check this", "compare these sources", "проверка на факти", "истина ли е", "дезинформация ли е", "сравни тези източници".
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。
Automated sequencing quality control and advanced visualization wrapping FastQC, MultiQC, and custom chart generation. Exposes an MCP stdio server for live AI integration alongside a ClawBio CLI runner.
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 system resilience through controlled fault injection. Covers hypothesis-driven chaos experiments, failure injection types (network, service, infrastructure, dependency), LitmusChaos/Chaos Mesh/AWS FIS/Gremlin/toxiproxy tooling, automated abort gating, game day planning, and progressive chaos adoption. Use when: "chaos engineering," "fault injection," "resilience test," "game day," "failure recovery," "system reliability," "blast radius." Not for: safe rollout flags/canary/dark launch during a release — use testing-in-production; designing new tests from production telemetry — use observability-driven-testing. Related: testing-in-production, observability-driven-testing, performance-testing, release-readiness, test-environments.
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 for regulatory compliance: GDPR/CMP consent verification, Google Consent Mode v2, Global Privacy Control (GPC), CCPA/US state opt-out, EU AI Act Article 50 transparency, Better Ads Standards, and cookie-inventory auditing. Covers automated consent-flow testing, third-party script blocking before consent, and cookie drift detection. Use when: "GDPR test," "compliance," "CMP test," "cookie consent," "consent mode," "CCPA," "GPC," "AI Act," "Better Ads," "privacy banner." Not for: WCAG/axe-core test authoring — use accessibility-testing. Not for: OWASP/vuln scanning — use security-testing. Not for: evaluating your LLM feature's quality or safety — use ai-system-testing. Related: accessibility-testing, security-testing, ai-system-testing, ci-cd-integration.
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 execute structured exploratory testing sessions. Covers Session-Based Test Management (SBTM), charter writing, heuristic-based exploration (HICCUPS, FEW HICCUPS), bug discovery patterns, note-taking templates, and conversion of findings to automated tests. Use when: "exploratory testing," "SBTM," "manual testing," "bug hunting," "test charter," "heuristic testing." Not for: an AI browser agent autonomously exploring the app from a natural-language goal — use agentic-browser-testing. Not for: testing your product's own AI/LLM features — use ai-system-testing. Related: test-planning, ai-bug-triage, risk-based-testing, agentic-browser-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。
Build and visualize QA dashboards and reports with Allure Report, Grafana, and ReportPortal. Covers test execution visualization, stakeholder-facing quality reports, trend/flakiness panels, release-readiness gates, alerting, and CI integration for automated report generation. Use when: "test dashboard," "Allure," "test report," "quality dashboard," "Grafana," "ReportPortal," "test results visualization." Not for: defining which KPIs to measure or how to interpret them — use qa-metrics (this skill builds the panels; qa-metrics decides what they should show). Related: qa-metrics, ci-cd-integration, ai-bug-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。
Move quality earlier in the development lifecycle. Covers dev/QA pairing patterns, Three Amigos sessions, TDD facilitation (Red-Green-Refactor), PR review checklists for testability, and Definition of Done with quality gates. Includes shift-left maturity model for team assessment. Use when: "shift left," "TDD," "dev-QA pairing," "definition of done," "testability," "quality culture," "QA in sprint planning." Not for: writing the unit tests themselves — use unit-testing; automated PR test-quality review at scale — use ai-qa-review; multi-quarter QA direction or roadmap — use test-strategy. Related: unit-testing, ai-qa-review, test-strategy.
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 says "/ultrawork", "ultrawork", or wants to run the full specify → execute pipeline automatically with a single command. Automated end-to-end workflow that chains specify and execute skills.
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。
Designs automated email sequences end to end: the trigger, branches and exit rules, the full copy of every message, and the sender setup that keeps them out of spam (SPF, DKIM, DMARC, one-click unsubscribe, consent). Covers onboarding and welcome series, lead nurture, abandoned cart, dunning, win-back and sunset flows. Use when someone asks for a "drip campaign", "welcome sequence", "onboarding emails", "nurture sequence", "win-back emails", "lifecycle emails", "email automation", or says "our emails land in spam" or "nobody clicks our onboarding emails". In-app onboarding belongs to onboarding-cro.
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。
Schedule recurring tasks with cron on Linux/macOS. Use when a user asks to run scripts on a schedule, set up automated backups, schedule data processing, or configure periodic tasks on a server.
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。
Sets up automated database backup, restore, and disaster recovery procedures for PostgreSQL, MySQL, MongoDB, and Redis. Use when you need to implement backup schedules, point-in-time recovery, cross-region replication, backup verification, or disaster recovery runbooks. Trigger words: database backup, pg_dump, mysqldump, mongodump, disaster recovery, point-in-time recovery, WAL archiving, backup rotation, restore database, RTO, RPO, backup verification.
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。
commitlint checks that Git commit messages follow the Conventional Commits format (type(scope): subject) and fails when they do not. Use when a user asks to standardize commit messages, enforce commit conventions with a husky commit-msg hook, lint commits or PR commits in CI, customize rules, or prepare commits for automated changelogs and semantic versioning.
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。
CodeRabbit reviews pull requests with AI on GitHub, GitLab, Azure DevOps and Bitbucket, and reviews local changes from a CLI before they are pushed. Use when a user asks to set up automated PR reviews, write or validate a .coderabbit.yaml, add path-specific review instructions, control a review with @coderabbitai commands, or run coderabbit review from the terminal or a coding agent.
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。
You are an expert in Clay, the data enrichment and outbound sales platform that pulls from 75+ data providers to build rich prospect profiles. You help teams automate lead discovery, enrich contacts with firmographic and technographic data, score leads against ICP criteria, and trigger personalized outreach sequences — replacing manual research with automated, data-driven prospecting.
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 Amazon RDS for fully managed relational databases. Provision MySQL, PostgreSQL, and Aurora instances with automated backups and patching. Configure read replicas for scaling, snapshots for recovery, and Aurora Serverless for variable workloads.
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。
Run AI agent and LLM evaluations in CI/CD pipelines — automated quality gates that fail the build when AI output quality drops. Use when someone asks to "test my AI agent", "add evals to CI", "catch prompt regressions", "compare models", "evaluate LLM output quality", "set up AI quality gates", or "benchmark my agent before deploying". Covers eval frameworks (Cobalt, Promptfoo, Braintrust), LLM-as-judge scoring, threshold-based assertions, and GitHub Actions integration.
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。
Run autonomous AI-driven penetration tests on web applications using tools like Shannon, PentAGI, and similar frameworks. Use when tasks involve setting up automated penetration testing pipelines, combining AI agents with security tools (nmap, subfinder, nuclei, sqlmap), building autonomous exploit chains, generating pentest reports with proof-of-concept exploits, or integrating AI pentesting into CI/CD pipelines. Covers the full pentest lifecycle from reconnaissance to reporting using AI orchestration.
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 expertise in automated crypto trading systems, including DEX sniping bots, arbitrage strategies, MEV protection, Telegram trading bots, and anti-rug protection. Use when "trading bot, sniping bot, arbitrage bot, Telegram trading, DEX bot, MEV bot, copy trading, auto trading, token sniper, " mentioned.
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 implementing policy-as-code, continuous compliance monitoring, automated evidence collection, or audit-ready systems requiring SOC2/ISO/PCI/HIPAA complianceUse when ", " mentioned.
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。
The systematic orchestration of AI-powered marketing workflows that combine content generation, approval processes, multi-channel distribution, and quality gates into cohesive automation systems. This skill integrates AI generation tools (Jasper, Claude, GPT) with automation platforms (Zapier, Make, n8n) and marketing systems to build scalable content pipelines. It focuses on maintaining brand consistency, implementing rigorous quality gates, and balancing automation with strategic human oversight. Key capabilities include designing parallel approval flows, monitoring costs, and architecting "invisible" automation that enhances productivity without sacrificing quality.Use when "AI workflow, automate content, content automation, workflow automation, AI pipeline, automated marketing, content distribution automation, approval workflow, scale content production, AI orchestration, automation, workflow, ai-orchestration, content-pipeline, approval-workflow, multi-channel, quality-gates, cost-control" mentioned.
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。
Expert in applying AI to education - AI tutors, personalized learning paths, content generation, automated assessments, and adaptive learning systems. Covers practical implementation of AI to enhance (not replace) human instruction. Use when "ai tutor, ai for learning, personalized learning, adaptive learning, ai assessment, generate course content, ai education, " mentioned.
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。