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概要と使いどころ
Evaluates fairness opinion methodologies across DCF, trading comps, and precedent transactions for board-level decisions. Use when reviewing fairness opinions, preparing board materials, or analyzing transaction fairness.
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Evaluates regulated utility rate-setting with RAB methodology, allowed return analysis, and regulatory reset risk assessment. Use when analyzing regulatory frameworks, modeling rate cases, or evaluating allowed return mechanics.
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Structures TAM/SAM/SOM analysis with bottom-up and top-down methodology and market timing assessment. Use when sizing markets, validating market opportunity, or assessing timing risk.
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Values infrastructure assets with DCF, regulated asset base, and comparable transaction methodologies adjusted for regulatory and contractual frameworks. Use when valuing infrastructure assets, analyzing regulated utilities, or benchmarking infra transactions.
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Evaluates GP-led continuation vehicle structures with existing LP options, pricing methodology, and new investor terms. Use when analyzing continuation vehicles, structuring GP-led transactions, or evaluating tender offers.
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, reverse-engineer, or update living feature specs following the spec-anchored methodology.
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。
Rewrite text so it reads like a real person wrote it, not an AI. Auto-detects Russian vs English and applies the matching rule set. Use when the user asks to humanize text, remove the "AI smell", de-AI, or make AI output sound natural - especially for Russian. Russian requests count too - очеловечь, перепиши как человек, оживи текст, убери запах ИИ, сделай живее, звучит как робот, убери канцелярит. Also use when asked whether a text was written by AI - нейросеть писала?, похоже на ChatGPT?, видно ли, что это ИИ, человек или бот писал, did AI write this? - then check and report findings without rewriting. While installed, also keep your own Russian and English writing free of the quick-list stock phrases, silently; never edit the user's text unless asked.
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Post-implementation QA planning — independent of /review (the developer chooses whether to run them sequentially or in parallel), only when the change has a running surface worth driving. Interviews the developer to turn the specs and the diff into an executable QA specification (specs/qa/QA-<N>-<slug>.md) that /execute-qa runs. The agent drives browser and shell; the human steps in only at named handoff points. Use only when the user asks to plan manual QA — never trigger automatically.
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。
Sync OpenClaw + ClawHub + Skills docs into a local mirror for the QMD memory backend.
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。
Report a problem with the open-pr plugin itself, or ask for a change, on its public issue tracker. Use when a run of one of its commands left something to say. Takes no URL.
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Find the most valuable commercial insight and the monetization point closest to lawful revenue for a project, product, service, website, community, content account, open-source tool, or business idea. Use when a user asks how to monetize, who pays, why people do not buy, what to charge, whether to keep building, what to stop, what to do next, or asks to continue or deepen an earlier business analysis. Answer the user's actual question before asking for more information and carry the analysis forward without repeating it. When the buyer, purchased result, or delivery ability is unknown, do not select them from industry stereotypes; use explicit conditional candidates and an evidence-gathering next step. Not for investment advice, income forecasts, get-rich-quick schemes, or guaranteed results.
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。
Interactive idea refinement using Socratic method to develop fully-formed 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。
Flatten nested conditionals with early returns or table-driven methods - keep nesting depth under 3 levels
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。
Never test mock behavior. Never add test-only methods to production classes. Understand dependencies before mocking.
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。
Guide for adding new RPC calls to Wave Terminal. Use when implementing new RPC commands, adding server-client communication methods, or extending the RPC interface with new functionality.
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AI-assisted debugging specialist with deep knowledge of modern debugging tools, observability platforms, and automated root cause analysis. Implements Cursor Debug Mode methodology — structured hypothesis ranking, targeted code instrumentation, human-in-the-loop reproduction gate, log-confirmed root cause, and mandatory cleanup.
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。
Specifies standards for RestController classes, including API route mappings, HTTP method annotations, dependency injection, and error handling with ApiResponse and GlobalExceptionHandler.
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Generate structured Product Requirements Documents using hypothesis-driven methodology with Implementation Phases tracking.
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Focuses on pandas-specific rules for data manipulation, including method chaining, data selection using loc/iloc, and groupby operations.
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。
Validate whether a proposed new artifact is feasible in the current stack before creator workflows 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。
Requires detailed type annotations for all Python functions, methods, and class members.
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Decision framework for agents to determine WHEN and WHETHER to use Claude Code's native cron scheduler (CronCreate/CronList/CronDelete) vs OS cron, GitHub Actions, or simple Task() delegation.
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。
Multi-step autonomous research methodology for deep investigation tasks with structured synthesis
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。
Context-Driven Development methodology - treating project context as managed artifacts alongside code for consistent AI interactions and team alignment.
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。