「comparison」の検索結果

Analyze PMCC (Poor Man's Covered Call / diagonal spread) positions from IB portfolio. For each diagonal spread, reports short leg risk (delta, IV, assignment probability), daily P&L projections, top-3 roll candidates, and a side-by-side comparison table. Requires TWS or IB Gateway running locally.

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。

When the user wants an agent to run SEO for a product end to end on a daily schedule, with state kept in a seo/ folder in the repo. Covers site health checks, fact checks against commits, page 2 to page 1 pushes, AI answer tracking, competitor sitemap diffs, a monthly keyword map, articles, comparison pages, free tools and link drafts. Also use when the user mentions "seo autopilot" or asks for a daily SEO run.

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 swift-snapshot-testing to visually verify SwiftUI views during implementation. Renders views to PNG for comparison against design references. Fast feedback loop (~25s/cycle).

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。

When a founder needs demo scripts, discovery call frameworks, objection handling, RFP/RFI responses, competitive feature matrices, POC planning, or closing playbooks. Activate for sales calls, demo prep, talk tracks, bid responses, competitor comparisons, or pre-sales 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。

Process drone survey data for construction sites. Generate orthomosaics, DEMs, point clouds, calculate volumes, track progress, and integrate with BIM models for comparison.

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。

Calculate comprehensive ROI for AI implementation projects. Takes current costs, manual process time, team size, and hourly rates. Generates detailed roi-analysis.md with executive summary, cost-benefit tables, sensitivity analysis, break-even timeline, and comparison scenarios. Use when evaluating AI investments, building business cases, or justifying automation spend.

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。

Optimize content for search engines with keyword analysis, readability scoring, meta descriptions, and competitor comparison. Use this when users want to improve SEO, optimize blog posts, or analyze content for search performance.

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 pitch decks and investor presentations. Reads slide content, evaluates narrative flow, problem/solution clarity, market sizing, competitive positioning, financial projections, team credibility, and ask clarity. Generates a scored pitch-review.md with slide-by-slide feedback, overall score, top improvements, investor objection predictions, and comparisons to successful decks. Use when reviewing fundraising materials, investor decks, or pitch presentations.

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。

Construct statistical arguments for MVP/awards. Narrative framing, comparison to past winners, advanced metrics, counter-arguments.

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。

Uses Cowork-style multi-step analysis to process deal room documents -- contracts, financials, org charts. Outputs risk assessment, term comparison, key findings, and negotiation recommendations.

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。

Searches the open web for docs, comparisons, and news via You.com MCP or WebSearch. Use when a topic needs knowledge beyond GitHub, forums, or academia.

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 comparing an exposure-outcome association across countries with parallel national surveys (KNHANES, NHANES, CHNS). Harmonizes variables, runs parallel weighted analyses and builds comparison tables for 2-country (KR+US) or 3-country (KR+US+CN) designs.

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。

Guides fg-data-profiling data profiling, report generation, configuration, CLI, comparison, privacy, and optional Spark/notebook integration workflows.

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 Hugging Face Evaluate to load metrics, comparisons, and measurements; compute and combine results; run evaluator pipelines; create custom modules; troubleshoot optional dependencies, cache, Hub, and CLI workflows.

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 and advise on the best way to approach a software project, including architecture, tech stack, implementation strategy, pricing/operating-cost tradeoffs, benchmark research, and comparisons with similar real-world projects. Use before building, mid-build, or after completion when the user asks for project strategy, optimal approach, research comparables, similar projects, stack selection, vendor/service choice, repo analysis, architecture critique, implementation feedback, or a prioritized improvement plan. Avoid for narrow single-bug debugging or isolated file edits unless the user asks for broader project direction.

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 PET selection guide covering differential privacy, homomorphic encryption, secure multi-party computation, federated learning, zero-knowledge proofs, and trusted execution environments. Includes use-case matching matrix, performance comparison, and GDPR alignment assessment for each technology.

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 "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

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 professional, consultant-grade PowerPoint presentations from scratch using MckEngine (python-pptx wrapper) with McKinsey-style design. Use when user asks to create slides, pitch decks, business presentations, strategy decks, quarterly reviews, board meeting slides, or any professional PPTX. AI calls eng.cover(), eng.donut(), eng.timeline() etc — 67 high-level methods across 12 categories (structure, data, framework, comparison, narrative, timeline, team, charts, images, advanced viz, dashboards, visual storytelling), consistent typography, zero file-corruption issues, BLOCK_ARC native shapes for circular charts (donut, pie, gauge), production-hardened guard rails for spacing, overflow, legend consistency, title style uniformity, dynamic sizing for variable-count layouts, horizontal item overflow protection, chart rendering, and AI-generated cover images via Tencent Hunyuan 2.0 with professional cutout, cool grey-blue tint, and McKinsey-style Bézier ribbon decoration.

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。

Look development loop for Blender assets combining materials, lighting, camera, and screenshot comparison until shading reads correctly via MCP.

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。

Evidence-driven architecture research for understanding real systems and making technical decisions. Use when doing architecture landscape studies, source-backed system archaeology, build-vs-buy or adopt/adapt/build decisions, open-source and commercial comparisons, revisiting an earlier architecture choice, or handling requests such as 架构调研, 架构选型, 竞品架构, 技术尽调, 同类方案, 开源替代, how is X built, and what should we learn from X. Do not use for small mechanical changes, market-only discovery, or detailed design after the technology direction is already fixed.

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 comparing two videos to analyze compression results or quality differences. Generates interactive HTML reports with quality metrics (PSNR, SSIM) and frame-by-frame visual comparisons. Triggers when users mention "compare videos", "video quality", "compression analysis", "before/after compression", or request quality assessment of compressed videos.

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。

Optimize content for search engines with keyword analysis, readability scoring, meta descriptions, and competitor comparison. Use this when users want to improve SEO, optimize blog posts, or analyze content for search performance.

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。

Fix LRU-bounded Flutter/bloc state classes that silently lose insertion-order updates. Use when: (1) a state class uses `Equatable` with a `LinkedHashMap<K, V>` field to preserve insertion order (LRU, MRU, recent-items caches), (2) `Cubit.emit` appears to drop emissions when an existing key is re-inserted or moved to most-recent, (3) LRU eviction evicts the "wrong" entry after a refresh, (4) a test that reports the same key twice and then overflows the cap sees the refreshed entry evicted instead of the oldest. Root cause: Equatable's default map comparison is structural (unordered), so reordering without changing keys/values produces an "equal" state that `Cubit.emit` suppresses.

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。