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概要と使いどころ
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 persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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。
Investigate a question against high-trust primary sources and capture the findings as a Markdown file in the repo. Use when the user wants a topic researched, docs or API facts gathered, or reading legwork delegated to a background agent.
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。
Fan out a one-shot or flat parallel batch of cc-fleet PROVIDER subagents (headless `cc-fleet subagent`) that return a result — DeepSeek / GLM / Kimi / Qwen / MiniMax, or a Codex/Claude subscription. Trigger ONLY to delegate a task to a cc-fleet provider worker (parallel research, bulk per-file work, a specialized model). Do NOT trigger for a multi-phase pipeline (that is /cc-fleet:workflow), for ordinary local shell/coding, or for editing or researching cc-fleet's own code.
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。
Transform resume bullets into detailed portfolio case studies
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 market rates, build negotiation strategy, and create counter-offer scripts
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 and maintain a research repository that makes findings findable, reusable, and cumulative across the organization.
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。
Email triage system that handles both one-time setup and recurring triage in a single skill. Auto-detects state: if no knowledge base exists at ${WORKSPACE}/Email/, runs an 8-section onboarding interview (grill-me discipline, one question at a time) to build a 7-file personalized KB, then offers to run triage immediately. If the KB already exists, skips setup and goes straight to triage — searches recent emails, classifies them via the user's taxonomy, researches new senders, generates TAKE IT/WORTH/PASS/FLAG recommendations, drafts replies (NEVER sends), delivers a report, and updates the KB. Use with 'update' argument to refresh preferences. Setup triggers: 'set up my inbox', 'configure inbox triage', 'set up my email system', 'configure email triage', 'build my email knowledge base', 'initialize email management'. Triage triggers: 'triage my inbox', 'inbox triage', 'check my email', 'run email triage', 'process my inbox', 'what's new in my email', 'handle my email', 'email triage'.
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 a diary study plan with prompts, duration, participant criteria, and analysis framework. Use when you need to understand user behavior over time in natural contexts.
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 a structured user interview script with warm-up, core exploration, and wrap-up sections. Use when preparing for user research interviews to ensure consistent, insightful conversations.
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 4-quadrant empathy map (Says, Thinks, Does, Feels) to synthesize user research into actionable insights. Use when you need to quickly capture and share user understanding across the team.
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 conduct, analyze, or synthesize customer research. Use when the user mentions "customer research," "ICP research," "talk to customers," "analyze transcripts," "customer interviews," "survey analysis," "support ticket analysis," "voice of customer," "VOC," "build personas," "customer personas," "jobs to be done," "JTBD," "what do customers say," "what are customers struggling with," "Reddit mining," "G2 reviews," "review mining," "digital watering holes," "community research," "forum research," "competitor reviews," "customer sentiment," or "find out why customers churn/convert/buy." Use for both analyzing existing research assets AND gathering new research from online sources. For writing copy informed by research, see copywriting. For acting on research to improve pages, see cro.
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。
Craft portfolio-ready case studies that tell the story of a design project.
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。
Format CVs for academic positions with publications, grants, and teaching
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。
Plan, smoke-test, execute, checkpoint, publish, audit, and reproduce full ShellBench native benchmark campaigns across OpenClaw, Hermes, Codex, and Claude Code, including model and reasoning identity, pinned harness versions, n=3 qualification through n=6 research runs, S3 trace retention, and task-turn-tool-token-cost exports.
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。
notes/ディレクトリへのメモ保存スキル。調査結果、企画、アイデア、会議メモなどを保存する時に使用。「メモして」「ノートにまとめて」「notesに保存」「notesにまとめて」「まとめておいて」で発動。「最近のメモ教えて」「メモの内容教えて」でメモ一覧・内容確認も可能。
xangi 上で起きた事象（再投稿・cron が回らない・データ不整合・想定外の動作・エラー等）の調査・根本原因分析・横展開・報告を行い、明示依頼時にだけ修正する汎用スキル。「調査して」「原因調べて」「再発防止」「横展開して」「xangi-kaizen」「事象を分析して」で発動。
Runs evidence-aware, multi-provider research with the Librarium v2 CLI. Use for deep research, competitive research, answer-engine visibility checks, or questions needing grounded multi-source coverage.
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。
銘柄・業界・マーケット・ビジネスモデルの深掘りリサーチ。Grok API (X/Web検索) と yfinance を統合して多角的な分析レポートを生成する。
知識グラフへの自然言語クエリ。過去のレポート・スクリーニング・取引・リサーチ・市況を検索。
Find open AI engineering jobs that fit a candidate, verify location and apply links, and assess each employer's product. Use for one-off shortlists or a job alert's research step. Don't use for résumé writing, job applications, or company news.
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。