「uat」の検索結果

Prompt engineering regression test matrix register: baseline outputs, semantic drift thresholds, judge evaluations, and golden dataset.

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。

Evaluate agent behavior with versioned cases and explicit verifiers. Use when comparing agent or prompt changes, reproducing failures, or running agent regression tests.

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 asks to "line edit", "edit my prose", "polish this chapter", "tighten the prose", "improve the sentences", "copyedit", "proofread", "proof pass", "check grammar and punctuation", "dialogue punctuation", "make the voices distinct", "everyone sounds the same", "read it aloud", "read-aloud pass", "text to speech", or wants a sentence-level quality pass on drafted chapters that preserves the author's voice. NOT for creating the style sheet, recording character voices, or checking voice consistency (use voice-style).

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。

Evaluates market bubble risk through quantitative data-driven analysis using the revised Minsky/Kindleberger framework v2.1. Prioritizes objective metrics (Put/Call, VIX, margin debt, breadth, IPO data) over subjective impressions. Features strict qualitative adjustment criteria with confirmation bias prevention. Supports practical investment decisions with mandatory data collection and mechanical scoring. Use when user asks about bubble risk, valuation concerns, or profit-taking timing.

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 US stock analysis including fundamental analysis (financial metrics, business quality, valuation), technical analysis (indicators, chart patterns, support/resistance), stock comparisons, and investment report generation. Use when user requests analysis of US stock tickers (e.g., "analyze AAPL", "compare TSLA vs NVDA", "give me a report on Microsoft"), evaluation of financial metrics, technical chart analysis, or investment recommendations for American stocks.

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。

Apply the bioengineering, translational systems biology, and tissue modeling frameworks of Linda Griffith, Professor of Biological Engineering at MIT and tissue engineering pioneer. Use this skill whenever facing decisions, designs, or evaluations in bioengineering, tissue modeling, microphysiological systems (organs-on-chips), synthetic matrix design, disease subtyping, or drug target validation. Reach for this skill when evaluating preclinical models (human vs. animal), reframing overlooked or neglected pathologies into rigorous engineering challenges, selecting biomaterials, designing cell signal processing assays, or diagnosing operational flaws in experimental platforms (e.g., PDMS compound absorption or Matrigel lot variability).

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, polish, and quality-check editable undergraduate or graduate thesis defense PowerPoint decks from a local thesis PDF/LaTeX project and an existing PPTX template. Use when the user asks for a formal defense PPT, thesis presentation, academic答辩PPT, template-matched PPTX, or local PowerPoint deck generation with visual inspection.

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。

Investigate MCP tool availability, discovery, and selection, including controlled adoption evaluations.

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。

Evaluate Jacobian reliability using recently resolved conjectures as held-out probes.

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。

Conduct comprehensive code reviews identifying bugs, security issues, performance problems, code quality concerns, and best practice violations. Use when reviewing pull requests, examining code changes, evaluating new code, assessing code quality, or providing feedback on implementations. Analyzes code for correctness, security vulnerabilities, performance bottlenecks, maintainability issues, test coverage, documentation quality, and adherence to coding standards. Produces structured markdown reviews with categorized findings, severity ratings, specific examples, and actionable recommendations. Triggers when users ask to review code, check pull requests, evaluate implementations, find bugs, or assess code quality.

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。

Evaluate vendors with comparison matrices, TCO analysis, risk assessment, reference check templates, and negotiation strategies

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。

Evaluate, compare, and manage vendor relationships. Trigger with "evaluate this vendor", "compare vendors", "vendor review", "should we renew", "RFP", or when the user is making procurement or vendor decisions.

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。

TRIGGER THIS when preparing interviews, planning hiring processes, creating interview questions, building evaluation frameworks, developing interviewer guides, or standardizing interview approaches. Builds complete interview kits with competency-based questions, scorecards, rubrics, interviewer training, and structured evaluation protocols.

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。

ACTIVATE when evaluating acquisition targets, conducting M&A due diligence, planning integration strategy, analyzing synergies, preparing sale materials, assessing culture fit, valuing targets, creating merger checklists, or managing M&A transactions. Critical for executives involved in buy-side or sell-side M&A, from initial target evaluation through post-acquisition 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 reproducible evaluations for AI agents with representative task sets, explicit rubrics, appropriate graders, baselines, regression gates, and failure analysis. Use when defining agent quality, comparing prompts or models, validating a release, measuring tool-use reliability, investigating regressions, or deciding whether an agent is ready for production.

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。

Think and work like an expert Computer Vision Scientist. Use when a task calls for Computer Vision Scientist judgment. Reasons from image formation, projective geometry (pinhole intrinsics and extrinsics, epipolar/PnP/bundle adjustment, similarity-scale ambiguity), and COCO/LVIS AP mechanics through DINOv3/SigLIP 2 foundation baselines, RF-DETR/YOLO26/SAM 3 models, COLMAP 4/GLOMAP and VGGT geometry, pycocotools/TrackEval/BOP evaluation, and CVPR reporting and EU AI Act limits while treating train–test and pretraining leakage, AP evaluation-setting gaming, preprocessing mismatches (EXIF, BGR, aliased resizing), label noise, and camera-convention and scale errors as first-class failure modes.

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。

Think and work like an expert Computer Graphics Researcher. Use when a task calls for Computer Graphics Researcher judgment. Reasons from the rendering equation, Monte Carlo bias/variance, sampling theory, and BSDF energy conservation through pbrt-v4/Mitsuba 3 references, MIS/ReSTIR/path guiding, DXR 1.2 real-time stacks, 3DGS and NerfBaselines protocols, FLIP/ColorVideoVDP evaluation, OpenUSD/MaterialX/OpenPBR interchange, and ACES 2/OCIO color while treating unconverged references, unequal-time comparisons, denoiser and temporal-reuse bias, scene- vs display-referred color errors, and white-furnace energy failures as first-class failure modes.

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。

Think and work like an expert Carbon Cycle Scientist. Use when a task calls for Carbon Cycle Scientist judgment. Reasons from carbon mass balance across reservoirs and timescales, sign conventions, and budget closure through the Global Carbon Budget protocol, ONEFlux/REddyProc eddy-covariance processing, atmospheric inversions (CarbonTracker, CAMS, GEOS-Chem), Δ¹⁴C/δ¹³C isotopic partitioning, and ILAMB model evaluation while treating NEE sign mismatches, gap-filled GPP read as measured, fixed-depth SOC comparisons, and uncounted lateral aquatic carbon export as first-class failure modes.

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。

Reverse DCF valuation, implied growth rate, market expectations analysis, reverse discounted cash flow, implied valuation assumptions, market-implied projections, DCF sanity check

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。

Discover and evaluate KOLs (Key Opinion Leaders) and creators across TikTok, Instagram, YouTube, and Xiaohongshu through SandBase. Use when asked for influencer research, creator outreach lists, KOL evaluation, or partnership 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。

Assess genome assembly quality using QUAST for contiguity metrics and BUSCO for completeness. Essential for evaluating assembly success and comparing assemblers. Use when evaluating assembly completeness and quality.

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。

Routing skill of last resort. Takes a plain-language QA situation and names the right 1-2 skills to use and in what order. Use ONLY when the request does not match any other skill's trigger phrases. Use when: "which skill should I use," "where do I start," "I'm not sure what to test," "/qa-do," or any vague QA situation that doesn't map to one skill. Not for: bootstrapping a brand-new project with no QA — use qa-start. Not for: capturing project setup/context — use qa-project-context. If the request clearly matches another skill, invoke that skill directly instead of routing through here. Related: qa-start, qa-project-context, 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。

Make technology decisions for every product capability by discovering existing assets, evaluating vendor-aligned options, and categorizing as Reuse/Extend/Build/Buy/Integrate/Research during PRD v0.5 Red Team Review. Handles both greenfield and brownfield contexts. Triggers on "tech stack", "build or buy?", "what technologies?", "technical decisions", "what do we reuse?", "existing stack", "vendor constraint", "IBM-first", "what tools do we need?", "evaluate solutions", "select tech stack". Consumes FEA- (features), SCR- (screens), RISK- (constraints). Outputs TECH- entries with decisions, rationale, and cross-references. Feeds v0.6 Architecture Design.

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。