「suggestions」の検索結果

Generate non-binding follow-up action suggestions from lead summaries or lead lists. Use when users ask for next best actions, call list for hot leads, or follow-up draft plan without automatic execution. Recommended chain: summary-generator then action-suggester then supervisor approval. Do not use for parsing, storage, or autonomous action execution.

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。

Scan HTML and JSX for accessibility issues and get fix suggestions. Use when you need to catch WCAG violations before they hit 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。

Evaluate Agent Skill design quality against official specifications and best practices. Use when reviewing, auditing, or improving SKILL.md files and skill packages. Provides multi-dimensional scoring and actionable improvement suggestions.

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。

Run all IP-level testcases from tc_list.md through the bench Makefile, capture results, and write per-failure issue reports with no fix suggestions

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 verified improvement suggestions from eval-core analysis to omcustom 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。

Opt-in post-PR analysis and improvement suggestions for code quality enhancement

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。

Read-only report of improvement suggestions from eval-core analysis engine

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。

App Store Optimization (ASO) toolkit for researching keywords, analyzing competitor rankings, generating metadata suggestions, and improving app visibility on Apple App Store and Google Play Store. Use when the user asks about ASO, app store rankings, app metadata, app titles and descriptions, app store listings, app visibility, or mobile app marketing on iOS or Android. Supports keyword research and scoring, competitor keyword analysis, metadata optimization, A/B test planning, launch checklists, and tracking ranking changes.

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。

Normalize and screen scattered foreign-trade leads into a conservative, structured lead pool that can flow into customer-intel research. Use when an operator has company names, websites, emails, or contact clues from search results and needs a standardized output with missing-field warnings, review flags, next-step suggestions, and customer-intel-ready JSON.

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。

Aesthetic assessment and design scoring across 6 dimensions. Use for UI critique, design review, visual quality assessment, remix suggestions. Activate on "design critique", "aesthetic review", "UI assessment", "visual quality", "design score", "remix this design". NOT for implementation (use frontend-developer), accessibility-only audits (use color-contrast-auditor), or brand identity creation.

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。

Synthesizes actionable feedback from validation results, confidence scores, and iteration triggers. Creates structured improvement guidance for re-execution. Activate on 'synthesize feedback', 'improvement suggestions', 'actionable feedback', 'iteration guidance', 'feedback generation'. NOT for iteration detection (use dag-iteration-detector) or convergence tracking (use dag-convergence-monitor).

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 receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation

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 receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation

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。

Code simplification suggestions — clarity, unnecessary complexity, redundant abstractions. Advisory only.

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。

Code simplification suggestions — clarity, unnecessary complexity, redundant abstractions. Advisory only.

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。

Before adding/installing any dependency the agent named — verify the package exists (slopsquatting: ~1 in 5 AI suggestions are hallucinated), isn't typo-adjacent, is pinned + locked, and CVE-scanned

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 receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation

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 citability scoring and optimization. Analyzes web page content to determine how likely AI systems (ChatGPT, Claude, Perplexity, Gemini) are to cite or quote passages from the page. Provides a citability score (0-100) with specific rewrite suggestions.

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。

Find niches, topics, audiences, products, channels, and content angles via deep web search. Use whenever Ahmad says "find me a niche", "hunt niches in X", "what's an underserved audience for Y", "find similar sites/videos to <example>", "research the X market", "what are people building in Y space", "/niche-hunter X", or any phrasing that asks for niche / market / topic / audience / product / channel discovery across the open web. Trusts domain + keyword search; chains web_search → web_fetch / web_extract / web_crawl + reddit_search + search_youtube + github_repo_intel. NEVER uses awesome-list shortcuts. Output shape is FLEXIBLE — list, table, report, suggestions, or normal prose — picked from how Ahmad phrased the ask.

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 after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

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。

Explicit retrospective of a coding session with evidence-backed suggestions for future work.

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。

Compress review pressure into the right next action instead of more code by default. Classify findings, reject non-liabilities, and choose proof-only vs minimal-fix vs refactor-kernel, preventing one-patch-per-comment churn. Use after a code review — PR comments, reviewer suggestions, or audit findings — and especially when you are about to reflexively patch every comment.

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。

Pull the open Bonemeal action queue (landing pages and blog posts the user has not built yet) via the bonemeal MCP server, let the user pick which ones to build, dispatch each to the spawn-pages or spawn-blogs skill with the action's brief, and mark each completed in Bonemeal once the file is written. Use this whenever the user says "grow", "/grow", "build my Bonemeal queue", "what's in my Bonemeal queue", "build the suggested pages", or wants to act on Bonemeal's content suggestions. Stop and ask the user before marking actions complete the first time, then remember their preference.

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 working with Coderabbit — codeRabbit AI code review management including automated review comments, auto-suggestions, review customization, and learning from feedback. Use when configuring CodeRabbit for AI-assisted code review, custom review rules, and automated suggestion 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。