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概要と使いどころ
Risk Assessment & Position Sizing — analyzes volatility, drawdown scenarios, correlation, liquidity, and provides position sizing calculators (Kelly Criterion, fixed percentage, volatility-adjusted) with a composite Risk Score (0-100) for any publicly traded stock.
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Sentiment & Momentum Analysis Agent — news sentiment, social media buzz, analyst ratings, institutional activity, insider trading, and short interest with Sentiment Score (0-100)
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Fundamental Analysis Agent — valuation, growth, profitability, balance sheet, competitive moat, and management quality analysis with Fundamental Score (0-100)
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Head-to-Head Stock Comparison — takes two tickers and compares them across valuation, growth, profitability, technical setup, sentiment, risk profile, and analyst consensus with a scored comparison table and overall recommendation.
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Technical Analysis Agent — price action, indicators, chart patterns, support/resistance, and momentum analysis with Technical Score (0-100)
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Builds a complete Google Ads keyword strategy with search intent mapping, ad group structure, match type recommendations, negative keyword lists, estimated CPC ranges, Quality Score optimization, and ready-to-implement campaign architecture
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Full Ad Strategy Orchestrator. Launches 5 parallel subagents to build a complete advertising strategy from a single URL — audience personas, creative concepts, funnel architecture, competitive intelligence, and budget allocation. Produces a composite Ad Readiness Score (0-100) with a unified, client-ready strategy report.
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Fix iOS AVPlayer "CoreMediaErrorDomain error -12939 - byte range length mismatch" or similar video streaming failures when a proxy/edge/CDN service sits between the client and object storage (GCS, S3, R2). Use when: (1) iOS video fails with -12939 "byte range length mismatch - should be length 2 is length N", (2) curl with Range: bytes=0-1 returns full file instead of 2 bytes, (3) one URL path works with Range but another path to the same storage doesn't, (4) proxy advertises Accept-Ranges: bytes but returns 200 with full file for range requests. The root cause is typically a proxy handler that constructs a new request to the backend without forwarding the client's Range header.
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Debug ClickHouse deserialization errors in Rust caused by column ORDER mismatch when using SELECT alias.* on a VIEW that adds computed columns. Use when: (1) Some query variants (e.g. sort=trending) return 500 but others (sort=recent) succeed, (2) Queries use SELECT view_alias.*, extra_cols FROM view JOIN ..., (3) The Rust Row struct has the right number and types of fields but deserialization fails anyway (wrong type error, not "not enough data"), (4) The VIEW was recently extended with a computed column (e.g. trending_score).
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AI creative director with recursive self-assessment. Generates concepts using world-class methodologies (SIT, TRIZ, Lateral Thinking, bisociation), scores against 6 weighted criteria with Cannes/D&AD/HumanKind calibration, and recursively refines until the 9+ threshold is reached. Accepts briefs in any format — text, voice transcript, PDF, or raw notes. Use when the user asks to generate creative concepts, brainstorm campaign ideas, develop a Big Idea or campaign platform, evaluate or critique existing creative work, find consumer insights, or shares a brief for ideation — including activations, PR-stunts, brand utility, experiential, and non-advertising ideas. Calibrates against a library of 569 legendary campaigns (P01-P18 pattern map) to detect saturation and ensure originality. Do not use for media planning, production budgeting, brand identity/logo design, copywriting final drafts, or market research data collection.
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Analyzes test suites and suggests optimal test execution order based on code change impact and project requirements. Use when optimizing CI/CD pipelines, deciding which tests to run before committing, or selecting critical tests under time constraints. Prioritizes tests by analyzing recently modified files, test dependencies, and coverage overlap. Outputs detailed rankings with priority scores and reasoning to help teams run the most impactful tests first.
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Generate concise, structured summaries of requirements for quick team understanding. Use when analyzing requirements from text documents (MD, TXT, DOCX) or technical specifications to create bullet-point summaries that highlight core functionality and dependencies/constraints. Ideal for sprint planning, stakeholder updates, team onboarding, or any situation requiring rapid comprehension of requirement documents.
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Extract and summarize core features, constraints, and priorities from requirement documents. Use when analyzing PRDs, RFCs, business requirements, user stories, epics, or any requirement documentation to identify must-have features, technical constraints, priority levels (P0/P1/P2/MoSCoW), and key decision points. Triggers when users ask to summarize, analyze, extract, or understand requirements from documents.
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。
Generate concise summaries of source code at multiple scales. Use when users ask to summarize, explain, or understand code - whether it's a single function, a class, a module, or an entire codebase. Handles function-level code by explaining intention and core logic, and large codebases by providing high-level overviews with drill-down capabilities for specific modules.
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Analyzes surviving mutants from mutation testing to identify why tests failed to detect them. Takes repository code, test suite, and mutation testing results as input. Identifies root causes including insufficient coverage, equivalent mutants, weak assertions, and missed edge cases. Automatically generates actionable test improvements and new test cases. Use when analyzing mutation testing results, improving test suite effectiveness, investigating low mutation scores, generating tests to kill surviving mutants, or enhancing test quality based on mutation analysis.
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Search code repositories for code related to a given code snippet, ranking results by call chain similarity, textual similarity, and functional similarity. Use when finding related code, locating similar implementations, discovering code dependencies, or identifying code that performs similar operations. Outputs ranked file lists with matching code snippets and relevance scores.
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 Quarterly Business Reviews with account health scores, usage analytics, ROI analysis, expansion opportunities, and risk mitigation
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Research customer questions by searching across documentation, knowledge bases, and connected sources, then synthesize a confidence-scored answer. Use when a customer asks a question you need to investigate, when building background on a customer situation, or when you need account context.
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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.
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ACTIVATE when building executive dashboards, tracking business metrics, creating executive scorecards, monitoring organizational KPIs, setting up health metrics, building dashboards for real-time monitoring, or establishing systems to track and measure business performance. Critical for any executive who needs to monitor company health, make data-driven decisions, or track progress toward strategic goals.
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。
Turn vague or partially formed AI, computer-science, data-driven, and engineering-method research directions into evidence-grounded, falsifiable, paper-ready Research Idea Briefs through heuristic questioning, staged confirmation, literature positioning, novelty attack, and method-figure planning. Use when the user needs to clarify a research idea, formalize inputs/outputs/objectives/constraints, frame background and motivation, select 3-5 anchor papers, identify a defensible gap, derive 2-3 contributions, stress-test novelty, or design an input-to-output method framework and core-paper figure. Also trigger on requests such as 明确科研 idea、启发式梳理 idea、互动追问科研问题、梳理论文创新点、定义研究任务、找对标论文、把研究方向变成论文方案、画方法框架图. Do not use as the primary workflow for running experiments, exhaustive systematic reviews, drafting full manuscript sections, or polishing finished prose.
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。
Add, inspect, repair, or validate CodeDrobe Core adapters for Chromium/Electron AI desktop applications. Use when supporting a new app, updating Codex or WorkBuddy compatibility after an app release, discovering installation paths or CDP targets, selecting stable DOM landmarks, adding renderer profiles or transactional host settings, recording lastVerified versions, or writing adapter and real-renderer tests.
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The HyperFrames composition contract — build one renderable project. Use for composition structure, the `data-*` timing attributes, `class="clip"`, tracks, sub-compositions, variables, framework-owned media playback, deterministic-render rules, and validation. Also covers Tailwind projects and the STORYBOARD.md / SCRIPT.md plan formats. Read before writing composition HTML.
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Use when designing, querying, or troubleshooting the Financial Services Cloud data model — including managed-package (FinServ__ namespace) and Core FSC (standard objects, no namespace) object structures, household relationship modeling, financial account ownership, and the FSC rollup framework. Trigger keywords: FSC data model, FinancialAccount, FinancialHolding, FinancialAccountRole, FinancialAccountParty, FinServ namespace, household rollup, AssetsAndLiabilities, FinancialGoal, LifeEvent, AccountContactRelation FSC. NOT for setting up financial accounts and roles in Setup — use admin/financial-account-setup. NOT for household membership and rollup scheduling — use admin/household-model-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。