「recommend rule」の検索結果

Converges an underspecified request into a typed IntentSpec before planning or acting. Use when the user asks to implement, add, change, refactor, fix, migrate, configure, handle, triage, sort out, look into or decide something and the request leaves decisions open (which objects, which approach, what happens on failure, which trade-off), contradicts itself, admits two readings, or rests on an approach its sources may rule out — in a codebase, a ticket queue, a research brief or a runbook — or when the user says "clarify the intent", "what do you need from me", or invokes intent-router. Looks up what its sources hold (code, history, decision records, a ticket log, an order record, the policy in force) before asking; asks only preference or irreversible questions, one at a time, with a recommended default; halts instead of guessing; checks the delivered work against the spec. Silent on a fully stated task its sources do not contradict. Not for explaining existing state ("what does X do", "why is Y slow").

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。

Retirement projection with Monte Carlo simulation logic. Calculates required nest egg via 4% rule and 25x expenses, projected vs needed gap, contribution recommendations, asset allocation by age, Social Security claim optimization, healthcare cost projections, and withdrawal sequence planning. Use when the user says "/finance retirement", "am I on track for retirement", "how much do I need to retire", "Social Security timing", or any retirement question.

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。

REQUIRED before copying any third-party SKILL.md, MCP manifest, Cursor rule, or agent config into this machine or forwarding one to a user. Installing a skill grants Read/Bash/network under your identity — this is `curl | sh` in a different costume. Do not install, load, recommend, or forward any third-party agent asset until this skill has vetted it. Bypassing this turns the agent into a delivery vector.

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 A/B copy variants grounded in Eugene Schwartz's awareness and market-sophistication stages and John Caples' tested-headline discipline. Every variant declares its angle, the awareness stage it targets, and a falsifiable hypothesis, and the skill recommends which pair to test first and what delta counts as signal. Use when the user says "A/B test this", "write variants", "give me options", "test different angles", "copy variants", "ab copy", "test this headline", "multiple versions", "which version is better", or wants to test copy before publishing. For reviewing copy that already exists, see copy-review. For sample size and stopping rules, see growth-experiment. For diagnosing a page rather than its words, see page-cro. For full paid ad sets by platform, see ad-campaign-writer.

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。

Displays the full skills catalog with descriptions, autonomous build chains, recommended pipelines, and parallelization rules.

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 Claude Code context window consumption across agents, skills, MCP servers, and rules. Identifies bloat, redundant components, and produces prioritized token-savings 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。

AI compliance analysis for EU AI Act, ISO 42001, NIST AI RMF, GDPR, OECD, financial services regulations (SEC, FCA, FINRA, DORA, MiFID II), and other frameworks. Use when asked to generate a compliance checklist for an AI tool or use case, determine if a risk assessment is required, score an AI tool's risk level, identify where an AI tool or use case could run afoul of regulatory requirements, perform a gap analysis, recommend remediation steps, assess a vendor, draft an acceptable use policy, map training requirements, or review jurisdiction-specific AI rules. Triggers on phrases like "compliance checklist", "risk assessment", "risk score", "does this comply", "EU AI Act", "ISO 42001", "NIST AI RMF", "AI governance", "gap analysis", "vendor assessment", "acceptable use policy", "MNPI", "AI training requirements", or any request to evaluate an AI tool or use case for regulatory compliance or risk.

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 websites for SEO, performance, security, technical, content, and 15 other issue cateories with 230+ rules using the squirrelscan CLI. Returns LLM-optimized reports with health scores, broken links, meta tag analysis, and actionable recommendations. Use to discover and asses website o...

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 websites for SEO, performance, security, technical, content, and 15 other issue cateories with 230+ rules using the squirrelscan CLI. Returns LLM-optimized reports with health scores, broken links, meta tag analysis, and actionable recommendations. Use to discover and asses website or webapp issues and health.

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 the user asks to "personalize the email", "add merge tags / dynamic content", "set up conditional blocks per segment", or "make first-name and product-recommendation fields fall back safely"; produces a merge-tag map with per-tag fallbacks, conditional-block rules with per-segment variations, a fallback-safety audit, and a PII guard on what may render, informing the SEND E (Engagement/personalization) dimension. Not for building the segments — use list-segment-builder; not for writing the base copy — use email-creative-builder; not for scoring EQS or running vetoes — use email-quality-auditor. 邮件个性化/合并标签/条件内容块/兜底默认值

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。

Structured decision-making framework for technical proposals and trade-off analysis. When the user faces a choice between multiple approaches (architecture decisions, tool selection, refactoring strategies, migration paths), this skill produces a 1-3-1 format: one clear problem statement, three distinct options with pros/cons, and one concrete recommendation with definition of done and implementation plan. Use when the user asks for a "1-3-1", says "give me options", or needs help choosing between competing approaches.

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 websites for SEO, performance, security, technical, content, and 15 other issue cateories with 230+ rules using the squirrelscan CLI. Returns LLM-optimized reports with health scores, broken links, meta tag analysis, and actionable recommendations. Use to discover and asses website or webapp issues and health.

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。

Work out the order and the loose ends before turning down an offer or withdrawing from a selection process: whether the offer being kept is actually settled in writing, whether each decline is mid-selection, post-offer, or after acceptance, who besides the employer has to be told when the application came through an agent, a school recommendation, or a referral, which reply deadlines have passed, and what documents, expenses, or borrowed items are still outstanding. Use when a user holds more than one offer and needs to decline the rest, wants to withdraw from a process they are no longer pursuing, or has been asked to turn down other employers as a condition; do not use to decide whether to decline, to rule on what declining after acceptance means legally, or to send any message.

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。

Structured decision-making framework for technical proposals and trade-off analysis. When the user faces a choice between multiple approaches (architecture decisions, tool selection, refactoring strategies, migration paths), this skill produces a 1-3-1 format: one clear problem statement, three distinct options with pros/cons, and one concrete recommendation with definition of done and implementation plan. Use when the user asks for a "1-3-1", says "give me options", or needs help choosing between competing approaches.

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。