「ai platform」の検索結果

Audits and designs iOS/macOS/watchOS/visionOS interfaces against the Apple Human Interface Guidelines, including the Liquid Glass design language (announced WWDC25, shipped with iOS 26/macOS Tahoe, Sept 2025). Use when reviewing an Apple-platform mockup or app for HIG compliance, checking contrast or tap-target sizes, or designing native-feeling Apple UI (e.g., 'audit my iOS app against the HIG', 'is this text readable on Liquid Glass?').

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Use when female Chinese web novel writing or 女频小说写作 needs Fanqie female-rank market radar, 言情, 古言, 现言, 快穿, 团宠, 权谋, 情绪流, 女性向人设, 人物关系, 评论区, 短视频文案, or platform voice references for AI-assisted fiction writing.

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Scalingo is a European (French) Platform-as-a-Service for deploying and operating web applications, background workers, and managed databases. Use this skill whenever the user mentions Scalingo, wants to deploy an app to Scalingo, work with the scalingo CLI, provision addons (PostgreSQL, MySQL, MongoDB, Redis, OpenSearch, InfluxDB), configure review apps, manage the scalingo.json manifest or a Procfile, scale containers, set up log drains, migrate from Heroku, use the Scalingo Terraform provider, or target sovereign regions (osc-fr1, osc-secnum-fr1 on SecNumCloud infrastructure). Also trigger for "deploy a French PaaS", "HDS-compliant hosting", "SecNumCloud deployment", or questions about Scalingo's Heroku-compatible buildpack model.

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[Documentation] Use when scanning the codebase to (re)generate ONE project-reference doc. Parameterized by `--target=<key>`: project-structure | backend-patterns | frontend-patterns | scss-styling | design-system | code-review-rules | domain-entities | feature-spec | docs-index | e2e-tests | integration-tests | seed-test-data | ui-system. Shared 4-phase scan engine; per-target detail lives in references/targets.md. `ui-system` is an orchestrator meta-target (`kind: orchestrator`) that runs the 3 UI child scans in parallel instead of the 4-phase engine.

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[Dev Data] Use when you need to implement or enhance test data seeders that simulate QC happy-path scenarios via application-layer commands. Flag: --mode=review reviews a target seeder (or the current changes / current work-context result) against every universal seed-data rule AND the project-specific seeder conventions — read-only, evidence-backed PASS/FAIL.

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[Workflow] Use when activating the E2E testing workflow to generate, update, or maintain Playwright/E2E tests. Pick mode with --source={changes|recording|update-ui}.

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[Planning] Use when you need to research, analyze, and plan technical solutions that are scalable, secure, and maintainable.

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[Workflow] Use when activating code-to-spec development — author/maintain the single canonical Feature Spec FROM existing code, keeping spec, implementation, and tests synchronized. For idea→spec (no code yet) use workflow-idea-to-spec.

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[Code Quality] Use when you need to perform a security review or audit on any scope — application code (OWASP Top 10 2025), secrets exposure, dependency/supply-chain malware, third-party repository vetting before install, infrastructure/config, CI/CD pipeline, AI-agent risks, and host/VPS compromise detection.

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[Documentation] Use when you need to generate a detailed, AI-analyzed release document from git history over a time range or custom prompt.

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[Documentation] Use to author, audit, amend, or test-spec a business Feature Spec. The single spec skill — modes draft|init|update|audit|amend create/maintain the tech-free 8-section Feature Spec; draft authors a provisional spec from an idea/requirement (no code yet, Evidence: TBD); tests generates Section 8 TC-{FEATURE}-{NNN} test specifications; sync reconciles §8 TCs ↔ executing test code. Per-mode procedure lives in references/{author,tests,sync}.md.

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[Decision Support] Use when pressure-testing irreversible, high-stakes decisions with adversarial AI advisors.

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[Testing] Use when you need to drive an integration-test suite to fully green — each round runs /integration-test-verify (whole system by default, or the target named in the prompt), and on ANY failure combines /debug-investigate + /integration-test-review (report-only) to adjudicate the fault (test wrong · test not optimal · source wrong), then /fix to resolve it at the owning layer, then /changes-review on that round's fix diff, then re-runs a FRESH full verify — looping until the whole suite passes its 2-consecutive-green-runs gate with zero failures.

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[Content] Use when you need to brainstorm as a PO/BA — structured ideation for problem-solving, new product creation, or feature enhancement.

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[Code Quality] Use when you need to simplify and refine code for clarity, consistency, and maintainability while preserving all functionality.

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[Content] Use when you need to create high-converting copy for marketing materials, social media, landing pages, email campaigns, and product descriptions.

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[Testing] Use when generating, updating, or maintaining E2E tests from recordings, specs, or code changes.

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[DDD Quality] Use when you need to review domain entities and value objects for DDD design quality.

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Use when designing or implementing empty, first-run, no-result, offline, error, or waiting states in Apple-platform apps — every empty state explains what is missing, why it matters, and what to do next.

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Pre-share leak scanner for Claude Code skills (or any folder). Scans BEFORE you publish for secrets (API keys, tokens, private keys, .env values, high-entropy strings), PII (emails/phones), local machine paths (C:\Users\<name>, /home/<name>, /Users/<name>, UNC, internal hostnames), and client/proper-noun names from a LOCAL private dictionary; also flags service-specific code (jira/github/gitlab/slack/salesforce/notion/linear/ stripe/aws/google) that should be split into a dedicated service plugin. Use when the user says "sanitize this skill", "before sharing a skill", "check for leaks", "is this skill clean", "is this safe to commit/publish/ open-source", "scrub secrets", "redact before commit", or "pre-push leak check". Cross-platform, stdlib-only Python, fail-closed.

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AI-powered E2E web testing — eyes and hands for AI coding tools. Declarative YAML scenarios, Playwright execution, visual matching (OpenCV + OCR), platform auto-detection (Flutter/React/Vue), learning DB. Install: npx skills add ksgisang/awt-skill --skill awt -g

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AI-driven data extraction from 55+ Actors across all major platforms. This skill automatically selects the best Actor for your task.

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Build an automated pipeline to defang indicators of compromise (URLs, IPs, domains, emails) for safe sharing and distribute them in STIX format through TAXII feeds and threat intelligence platforms.

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Analyzes DNS query logs to detect data exfiltration via DNS tunneling, DGA domain communication, and covert C2 channels using entropy analysis, query volume anomalies, and subdomain length detection in SIEM platforms. Use when SOC teams need to identify DNS-based threats that bypass traditional network security controls.

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。