「difficulty」の検索結果

Designs worksheets, workbooks and practice material that actually teach — sequencing difficulty, managing cognitive load, writing instructions a child can follow unaided, and building answer keys that hold up. Use this to design or fix a worksheet or workbook, decide how much practice a skill needs, structure a page so it can be completed without an adult reading it aloud, or diagnose material that looks right and does not 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。

Curated bank of interview problems organized by company, pattern, and difficulty. Provides problem recommendations, coverage tracking, weak area identification, and premium problem alternatives for FAANG interview preparation.

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。

Fetch and parse LeetCode problems with metadata, constraints, examples, hints, difficulty ratings, and related problems. Integrates with LeetCode API for comprehensive problem data retrieval.

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。

Design evaluation datasets and supporting documentation for AI systems and agents, covering the scoping interview, difficulty distribution, dataset contract, sample review, and metric and tooling selection. Use when building or reviewing an evaluation set for a conversational agent, assistant, or retrieval-grounded AI system.

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。

Live SEO data via DataForSEO MCP server. SERP analysis (Google, Bing, Yahoo, YouTube), keyword research (volume, difficulty, intent, trends), backlink profiles, on-page analysis (Lighthouse, content parsing), competitor analysis, content analysis, business listings, AI visibility (ChatGPT scraper, LLM mention tracking), and domain analytics. Requires DataForSEO extension installed. Use when user says "dataforseo", "live SERP", "keyword volume", "backlink data", "competitor data", "AI visibility check", "LLM mentions", or "real search data".

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。

Live SEO data via DataForSEO API credentials. SERP analysis (Google, Bing, Yahoo, YouTube, Google Images), keyword research (volume, difficulty, intent, trends), backlink profiles, on-page analysis (Lighthouse, content parsing), competitor analysis, content analysis, business listings, AI visibility (ChatGPT scraper, LLM mention tracking), and domain analytics. Uses DataForSEO credentials from the selected project `.env`; MCP tools are optional adapters when already available. Use when user says "dataforseo", "live SERP", "keyword volume", "backlink data", "competitor data", "AI visibility check", "LLM mentions", "image SERP", "google images", "image rankings", or "real search data".

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 to research keywords, find target keywords, or analyze search intent. Also use when the user mentions "keyword research," "keyword tool," "target keywords," "search volume," "search intent," "keyword difficulty," "topical map," "keyword clustering," "People Also Ask," "Google autocomplete," "autocomplete keywords," or "alphabet method." For clusters, use content-strategy.

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 keyword research, classify by search intent, cluster into topical groups, and prioritize for content production. Use this skill whenever the user asks to do keyword research, find target keywords, identify ranking opportunities, classify search intent, build a topical map, or plan a content strategy around what people search for. Triggers on keyword research, keyword strategy, search intent, keyword clustering, topic clusters, keyword difficulty, search volume, ranking opportunity, content gap, what should I write about, target keyword, primary keyword, secondary keyword, long-tail. Also triggers when planning a content calendar or new site without keywords yet defined.

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 needs to design evaluation datasets, create test cases, stratify samples, generate adversarial examples, extract eval dimensions from traces/specs, or build a labeled evaluation set. Also use when the user mentions test data design, eval coverage, difficulty stratification, synthetic data generation for eval, or "how to create good evaluation data." Outputs datasets in OpenJudge-compatible format.

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。

Stage exploration before instruction on complex problems. The learner produces two attempted approaches before consolidation — which builds on those attempts, not from scratch. Use for genuinely hard problems where struggle produces deeper learning.

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 retrieval practice questions at varied difficulty levels for a topic or concept. Use when creating quiz starters, revision activities, or low-stakes testing materials.

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。

Redesign a blocked topic sequence into an interleaved plan with mixed practice across related topics. Use when planning units, homework schedules, or revision programmes.

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。

Expert blueprint for platformer games including precision movement (coyote time, jump buffering, variable jump height), game feel polish (squash/stretch, particle trails, camera shake), level design principles (difficulty curves, checkpoint placement), collectible systems (progression rewards), and accessibility options (assist mode, remappable controls). Based on Celeste/Hollow Knight design research. Trigger keywords: platformer, coyote_time, jump_buffer, game_feel, level_design, precision_movement.

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 auditing or recalibrating game balance: build a source-driven Monte Carlo balance lab (Rust + rayon) that extracts live game data, simulates human playstyles (AFK→pro), emits win-rate/economy verdicts with confidence intervals, and bruteforce-tunes parameters. Trigger on unfair levels, unreachable shops, farm exploits, interest-curve cliffs, post-content recalibration, or CI balance JSON diffs. Keywords: balance lab, Monte Carlo, win rate, difficulty curve, economy career, playstyle simulation, Resource extraction, GDScript parser, bruteforce tuning.

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。

Expert patterns for managing combat waves, difficulty scaling, and automated enemy spawning in Godot 4. Use when building wave-based shooters, tower defense, or arena games.

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。

Expert blueprint for roguelikes including procedural generation (Walker method, BSP rooms), permadeath with meta-progression (unlock persistence), run state vs meta state separation, seeded RNG (shareable runs), loot/relic systems (hook-based modifiers), and difficulty scaling (floor-based progression). Use for dungeon crawlers, action roguelikes, or roguelites. Trigger keywords: roguelike, procedural_generation, permadeath, meta_progression, seeded_RNG, relic_system, run_state.

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。

Live SEO data via DataForSEO MCP server. SERP analysis (Google, Bing, Yahoo, YouTube, Google Images), keyword research (volume, difficulty, intent, trends), backlink profiles, on-page analysis (Lighthouse, content parsing), competitor analysis, content analysis, business listings, AI visibility (ChatGPT scraper, LLM mention tracking), and domain analytics. Requires DataForSEO extension installed. Use when user says "dataforseo", "live SERP", "keyword volume", "backlink data", "competitor data", "AI visibility check", "LLM mentions", "image SERP", "google images", "image rankings", or "real search data".

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。

Perform keyword research using the SemRush API. Use when the user says "find keywords", "keyword research", "what should I rank for", "keyword ideas", "search volume", "keyword difficulty", "topic clusters", "content gaps", or asks about SEO keywords for a topic or niche.

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。

Discover, analyze, and prioritize keywords for SEO and GEO content strategies. Identifies high-value opportunities based on search volume, competition, intent, and business relevance. Generates topic clusters and content calendars. Use when asked to "find keywords", "keyword research", "what should I write about", "keyword analysis", "find me topics to write", "search volume", "keyword difficulty", "content ideas", or any keyword discovery task.

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 sports trivia questions by sport/era/difficulty. Multiple formats with obscure stats, fun facts, pub quiz ready content.

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。

Live SEO data via DataForSEO MCP server. SERP analysis (Google, Bing, Yahoo, YouTube, Google Images), keyword research (volume, difficulty, intent, trends), backlink profiles, on-page analysis (Lighthouse, content parsing), competitor analysis, content analysis, business listings, AI visibility (ChatGPT scraper, LLM mention tracking), and domain analytics. Requires DataForSEO extension installed. Use when user says "dataforseo", "live SERP", "keyword volume", "backlink data", "competitor data", "AI visibility check", "LLM mentions", "image SERP", "google images", "image rankings", or "real search data".

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, expand, and cluster keywords for content strategy. Use when planning content topics, building keyword lists, expanding seed keywords, clustering by topic and intent, or mapping keywords to funnel stages. Triggers on "keyword research," "keyword expansion," "keyword clustering," "content topics," "what to write about," "topic ideation," "content planning," "6 circles method," "seed keywords," "keyword difficulty," "search volume," "intent mapping," "pillar content strategy," or "content calendar planning." For SEO optimization and auditing, see seo-and-aeo-strategy.

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 retrieval practice questions at varied difficulty levels for a topic or concept. Use when creating quiz starters, revision activities, or low-stakes testing materials.

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。

Stage exploration before instruction on complex problems. The learner produces two attempted approaches before consolidation — which builds on those attempts, not from scratch. Use for genuinely hard problems where struggle produces deeper learning.

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。