「udi」の検索結果

Experiment executor and monitor for academic research. 2-agent system covering code experiments (ML training, statistical analysis, ETL, simulation) and human studies (surveys, field studies, interviews). 4 modes: run (execute + monitor code), manage (track human studies), validate (statistical interpretation + reproducibility verification), plan (Socratic experiment design). Use when a researcher wants to run or monitor a research experiment, manage or resume a human-subject study, check the statistics or reproducibility of research results, or design an experiment or study, in English or Chinese (e.g., 跑實驗、管理研究、驗證結果、規劃實驗).

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Produce text-to-speech with Alibaba/Qwen Qwen3-TTS through DashScope/Model Studio or open-weight Qwen3-TTS checkpoints. Use when an agent must choose Qwen3-TTS models, voices, realtime versus non-realtime synthesis, voice cloning, voice design, instruction/prosody controls, multilingual or dialect speech, audio artifact handling, regional routing, pricing/privacy/licensing constraints, or production QA for narration, characters, assistants, audiobooks, ads, localization, or voice-enabled media.

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。

Provider-independent production guidance for translating measured audio features into deterministic video timing and motion. Use for beat-, onset-, phrase-, energy-, silence-, or spectrum-reactive visualizers, edits, typography, and generated compositions; not for music-video concept direction, audio generation, or a runtime-specific recipe.

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Produce audio-first podcast episodes with generative tools — design the show and episode format (interview, narrative, news brief, two-host conversational), write scripts for the ear, decide between fully-synthetic and hybrid (recorded human + synthetic) production and meet the disclosure duty each triggers, cast and direct multi-voice TTS for consistency and chemistry, build episode structure (cold open, intro/outro, segments, ad slots), assemble and edit, clear music and SFX rights, hit podcast loudness and delivery standards, ship metadata/chapters/transcripts through RSS, and pass platform AI-content policy and QA before publishing. Use this skill whenever the deliverable is a podcast episode or a podcast show bible, or when an agent must make production, rights, disclosure, or delivery decisions for spoken-word audio. Not for music tracks, single-voice notification prompts, or video where picture leads (route audio-for-video to a video 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。

Produce full-length audiobooks and long-form narration with generative voice tools. Use when the task is to turn a manuscript or long text into hours of spoken audio: preparing the manuscript for narration (front/back matter, footnotes, tables, dialogue), casting a single narrator or full cast, controlling pronunciation and voice consistency across a whole book, running a proofing/QC listen, meeting a retailer's technical delivery specs (RMS, peak, noise floor, room tone, chapterized files, metadata), and choosing a distribution route under each platform's current AI-narration policy. Not for short TTS clips, single-line voiceover, podcast production, or captioning existing video.

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。

Research target audiences across Reddit, Twitter, Instagram, and Xiaohongshu to understand demographics, interests, pain points, language patterns, and content preferences. Produces audience personas backed by real community data.

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。

Create or review audit support deliverables with traceable assumptions, decision criteria, ownership, and risk controls. Use when the user asks for audit support planning or analysis.

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 a website's SEO with Firecrawl. Use when the user asks for an SEO audit, metadata and heading review, sitemap/site-structure analysis, keyword opportunities, competitor SERP comparison, or prioritized search optimization recommendations.

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Onboard a new QA engineer to an existing codebase, or audit an existing test architecture. Produces a 30-day ramp plan: codebase orientation, framework walkthrough, test architecture audit, mentorship pairing, and first-test guidance. Use when: "QA onboarding," "new tester," "ramp up," "test architecture audit," "first 30 days," "QA mentorship," "joining QA team." Not for: setting up QA on a brand-new project from scratch — use `qa-start`. Related: qa-start, qa-project-context, shift-left-testing, ai-qa-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。

Audits and troubleshoots DNS records for domains including A, AAAA, CNAME, MX, TXT, SPF, DKIM, DMARC, CAA, and NS records. Use when someone needs to verify DNS configuration, debug DNS propagation issues, check email authentication records, or audit domain security. Trigger words: DNS records, dig, nslookup, SPF, DKIM, DMARC, MX records, DNS propagation, nameservers, CAA, domain configuration.

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。

Explore and manage databases with Drizzle Studio. Use when a user asks to browse database contents visually, inspect tables and data, run ad-hoc queries, manage database records through a GUI, debug database issues, or use a lightweight alternative to pgAdmin or DBeaver. Covers setup with Drizzle ORM, standalone usage, data browsing, filtering, and inline editing.

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 images and videos with Cloudinary. Use when a user asks to optimize images, add image transformations, implement responsive images, upload media, or serve optimized assets from a CDN.

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。

Finds orphaned, idle, and underutilized cloud resources across AWS, GCP, or Azure accounts. Use when someone needs to audit cloud spending, find unused EBS volumes, stale snapshots, unattached IPs, idle load balancers, or oversized RDS instances. Trigger words: cloud waste, orphaned resources, unused volumes, cloud audit, infrastructure cleanup, cloud bill analysis.

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 web pages and components for WCAG 2.2 accessibility compliance. Use when a user asks to check accessibility, find a11y issues, audit for WCAG compliance, fix screen reader problems, check color contrast, ensure keyboard navigation works, or prepare for accessibility regulations like the European Accessibility Act or ADA.

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 code safely in isolated sandboxes with resource limits, audit trails, and kill switches. Use when someone asks to "sandbox my agent", "run agent code safely", "add guardrails to AI agent", "isolate agent execution", "audit agent actions", "prevent agent from deleting files", "restrict agent permissions", or "add safety controls to AI coding agent". Covers Docker isolation, filesystem restrictions, network policies, resource locking, and comprehensive audit logging.

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 technical SEO auditing covering crawlability, indexation, Core Web Vitals, site architecture, canonicalization, structured data validation, and performance optimization. Use when the user asks about technical SEO, site audits, crawl issues, indexation problems, Core Web Vitals, page speed, or site architecture optimization. For content-focused SEO, see the seo-content-writer 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。

Conversion Rate Optimization auditing for landing pages, signup flows, checkout funnels, forms, and CTAs. Identifies friction points, runs heuristic evaluations, produces prioritized recommendations using ICE/PIE frameworks. Use when the user asks about conversion optimization, funnel analysis, landing page audits, form optimization, CTA testing, or checkout improvement.

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。

LinkedIn Ads platform expertise. Audit campaigns, optimize targeting (job title, company, seniority, industry), creative strategy, Lead Gen Forms, conversion tracking, and ABM campaigns. Use when the user asks about LinkedIn advertising, B2B paid social, LinkedIn campaign optimization, Lead Gen Forms, Matched Audiences, or professional audience targeting.

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。

Microsoft Advertising (Bing Ads) platform expertise. Audit campaigns, keywords, audiences, and UET tracking. Use when the user asks about Microsoft Ads, Bing Ads, search advertising on Microsoft, LinkedIn audience targeting, or Performance Max campaigns on Microsoft.

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。

Patterns for building multimodal AI applications that combine text, images, audio, and video. Covers vision APIs, audio transcription, and unified pipelines. Use when "multimodal AI, vision API, image understanding, GPT-4V, Claude vision, audio transcription, Whisper, document extraction, image to text, " 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。

Full SEO audit powered by the DataForSEO API. Orchestrates 5 specialist subagents (keywords, technical, competitors, content, backlinks) hitting DataForSEO Keywords Data, SERP, On-Page, Backlinks, and Labs endpoints in parallel, then produces a composite SEO Score (0-100) with prioritized action plan and audit JSON ready for PDF export.

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。

Closes a large backlog of findings (a gap analysis, an audit, or 10+ bugs) in prioritized waves. Loops through triage, dependency analysis, parallel fix streams, a test gate, and re-audit until the target is met. Use after a gap analysis, audit, or review produces 10+ findings, or to burn down a large bug or tech-debt backlog.

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 whether a paper's EVALUATION DESIGN actually measures what it claims and whether its reporting is complete — the validity layer family D (experiment-forensics) cannot reach. Three patterns: train/test leakage means the reported score may not measure generalization (HP-EVAL-LEAKAGE — adopts the Kapoor & Narayanan 8-type / 3-category leakage taxonomy; the illegitimate-proxy / sampling-bias / pretraining-contamination subtypes hand off as needs_external_check, naming but NEVER running Oren-2023 exchangeability / Shi-2023 Min-K% / Golchin-2023 Time-Travel / BIG-bench canary); a load-bearing LLM judge is conflicted (same model/family as a compared system) or unvalidated (no human-agreement, no bias control) (HP-JUDGE-VALIDITY); a declared condition/metric is dropped or switched to favor the method, or 'best' is chosen with no held-out set (HP-SELECTIVE-REPORTING). Verdict-bearing at L0/L1 from the DESCRIBED protocol — NOT repo-gated like experiment-forensics; L2 only CONFIRMS against split/preprocessing/result files. A fresh cross-model reviewer (gpt-5.6-sol xhigh, read-only, fresh thread per pass) PROPOSES findings, each span-anchored to a ledger claim_id; tools/adjudicate_findings.py DECIDES the verdict. Leakage and under-reporting are usually HONEST methodological errors — every finding describes a discrepancy to CHECK, never an accusation. An LLM generating GROUND-TRUTH labels is HP-FAKE-GT (experiment-forensics) — routed there, not here. Emits eval-design-forensics.findings.json; computes NO verdict. Detect-only. Triggers: "eval design audit", "evaluation validity", "train/test leakage", "data leakage", "is the score measuring generalization", "LLM judge bias", "is the judge validated", "selective reporting", "cherry-picked results", "评估设计审计", "评测有效性", "数据泄漏", "训练测试集泄漏", "裁判模型有没有验证", "选择性报告".

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 experiment integrity against the evidence ledger. At L2 (repo + result files present) a fresh cross-model reviewer reads the eval code line-by-line for fake/derived ground truth, score self-normalization, phantom results (a paper number with no backing file/key), dead/uncalled metric code, verified-scope inflation, method-described ≠ method-evaluated drift, synthesized-looking results, placeholder/fake data still wired into a released result, code-output ≠ reported-number mismatch, and missing reproducibility artifacts (an empirical/agent/LLM paper shipping neither code nor the prompts/configs its results need) — every finding span-anchored to a ledger claim_id. At L0/L1 (PDF / source only) the same patterns are surfaced as info-level 'could-not-verify' signals where the ledger gives an anchor (observability_level_required:2) — NEVER a fraud verdict from a PDF. The reviewer PROPOSES findings; tools/adjudicate_findings.py computes the verdict. Detect-only. Triggers: "experiment forensics", "audit the results", "check the eval code", "实验诚实度".

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。