2,205 件 ・ 関連度順
概要と使いどころ
Run a self-refinement loop when a Blender result is subpar, user expectations are not met, validation fails, or repeated issues reveal missing skill depth. The loop diagnoses the gap, decides whether to repair the artifact or acquire/refine a generic skill, sanitizes lessons for reusable publication, updates docs/versioning, validates, and prepares stage/commit/push when explicitly requested.
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 and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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。
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
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。
Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.
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。
Building engineer self-branding by turning technical contributions into a professional brand. Use for GitHub/LinkedIn/blog/conference positioning or 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。
Converting a supplied prompt into an executable specification: detects vague quality/quantity/explanation/style/design/technical/judgment wording, role and persona theater, and self-contradiction, then replaces each with a numeric bound, an observable behavior, or a scorable criterion — with a per-term ledger of what changed and what stayed open. Don't use for AI system design, RAG, or eval harnesses (Oracle), PRD/SRS authoring (Scribe), spec conformance verification (Attest), or SKILL.md normalization (Gauge).
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。
Orchestrating ecosystem self-evolution: lifecycle-phase detection, agent relevance, cross-agent knowledge synthesis, evolution proposals. Use when auditing skill-ecosystem health or fitness.
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。
Search TikTok e-commerce store leaderboards via Kalodata and query detailed information for specific stores. Supports viewing high-ranking, high-sales TikTok Shop stores by region, currency, language, and date range, and using shopId to retrieve revenue, sales volume, on-sale product count, self-operated/affiliate/shopping mall channel revenue, and creator collaboration count. Trigger when users mention TikTok store ranking, TikTok shop leaderboard, TikTok top shops, TikTok store ranking, TikTok Shop store ranking, kalodata store search, kalodata store ranking, TikTok store detail, TikTok shop profile, store revenue, store sales, TikTok shop ranking, TikTok shop leaderboard, TikTok top shops, TikTok store ranking, TikTok shop detail, TikTok store detail, kalodata shop search/detail. Even if the user does not explicitly mention "kalodata", trigger this skill whenever their need involves viewing TikTok store leaderboards or detailed data for a specific TikTok store.
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 this when reviewing statistical analysis, experiments, model evaluation, or ML work done by someone else (a notebook, PR, or report) — checking that the required context is stated and that every method applied comes with the figures and values required by the matching *-diagnostics skill. When running an analysis yourself, apply the matching *-diagnostics skill directly.
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。
Select evidence-based study strategies matched to material type, learning goal, and student habits. Use when advising students on revision techniques, homework, or independent study 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。
Build a library of metacognitive prompts targeting planning, monitoring, or evaluation for a specific task. Use when developing students' thinking-about-thinking during independent 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。
Design metacognitive checkpoints that prevent AI-assisted learning from bypassing genuine understanding. Use when students use AI tools and may overestimate their own comprehension.
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 classroom as a prepared environment optimised for independent learning, calm transitions, and material access. Use when classroom layout hinders independence or self-directed 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。
Create an interactive digital worked example sequence with fading for online or blended delivery. Use when building e-learning modules, LMS content, or app-based instruction.
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。
Require the learner to explain a concept in their own words before the AI evaluates or extends it. Ensures the AI works from the learner's understanding rather than providing an explanation from scratch.
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 deliberately flawed examples that develop error-detection skills and deepen understanding. Use when students make characteristic errors and need practice spotting mistakes.
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 if-then implementation intentions and WOOP plans for specific student behaviour change goals. Use when students know what to do but struggle to follow through on habits or routines.
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。
Routes between five assessment pathways — formative, rubric/criteria, authentic/performance, peer/self, and diagnostic — with validity and equity checks. Use when a teacher needs help choosing how to assess.
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 scaffolds that gradually increase student choice, voice, and ownership within a learning task. Use when students depend heavily on teacher direction and need to develop autonomy.
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。
Capture confidence ratings before and after a learning attempt to identify overconfidence and underconfidence patterns. Use when a student wants to understand how well they actually know something versus how well they think they know it.
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。
Conducts multi-axis code review. Use before merging any change. Use when reviewing code written by yourself, another agent, or a human. Use when you need to assess code quality across multiple dimensions before it enters the main branch.
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。
Extracts what the user actually wants instead of what they think they should want. Achieves this through one-question-at-a-time interview until ~95% confidence about the underlying intent. Use when an ask is underspecified ("build me X" without "for whom" or "why now"), when the user explicitly invokes ("interview me", "grill me", "are we sure?", "stress-test my thinking"), or when you catch yourself silently filling in ambiguous requirements before any plan, spec, or code exists.
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。
Lightweight, script-driven variant of data2motion for turning data into a smooth, on-brand animated chart (one self-contained HTML) — built for a TEXT-ONLY model that cannot see its own output. You never write HTML/CSS/SVG/animation; you extract the data, pick a chart, fill a small JSON spec, and run one build script that owns 100% of the look and the motion, so the result cannot drift from the house template style or lose its smoothness. Use to turn a number/stat/table/CSV/paragraph into a quick animated chart, data reveal, or KPI when you want guaranteed template fidelity with minimal reasoning and no image feedback.
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。
Writes an architecture decision record (ADR) for a decision the team has already reached: the context, the options weighed, the choice and its consequences. Use when the user says "record this decision", "write an ADR", "we decided X, capture it", or asks to document a technical choice already made. Not for making the decision itself or for tracking the work it leads to.
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。