リサーチ・学習のスキル

Promote existing research into a stable-status canonical article under `articles/` in a Knowledge Base project (the `knowledge-base` starter pack). Read when a decision has actually been made and the team wants the source-of-truth written down, or when asked to consolidate, canonicalize, promote research, or supersede an older article. Carries the decision-confirmation gate, the `supersedes:` chain that keeps the evidence trail intact, and the canonical voice. Does not conduct new research — that is the sibling `research-with-sources` skill.

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Investigate a topic against preserved sources and write a draft-status research article under `research/` in a Knowledge Base project (the `knowledge-base` starter pack). Read when asked to research a topic, compare options, synthesize sources, gather evidence, or extend an existing research doc. Carries the full procedure: scan existing coverage, agree a research rubric, capture every source verbatim before analyzing, write the article incrementally so a crash never loses work, cite every claim, and link it back into the graph. Does not promote findings to canonical knowledge — that is the sibling `consolidate-notes` skill, after a decision lands.

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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.

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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.

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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.

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Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination

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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.

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Build detailed B2B buyer personas with pain points, objections, buying triggers, and messaging angles. Use when given a product and market to research ideal customer profiles, creating buyer personas, or needing to understand who you're selling to before launching campaigns. Platform: Google and Meta.

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Find the exact pages AI assistants cite when answering questions in your category, and identify which ones you can get into. Claude clusters the cited sources, scores each by attainability, and outputs an outreach/content plan. Platform: AI visibility.

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Multi-AI research using available external providers (Double Diamond Discover phase) - Questions about best practices, patterns, or ecosystem research PRIORITY TRIGGERS (always invoke): "octo research", "octo discover", "co-research", "co-discover" DO NOT use for: simple file searches (use Read/Grep), questions Claude can answer directly, debugging issues (use skill-debug), or "what are my options" for decision support.

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Multi-AI research using available external providers (Double Diamond Discover phase)

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Multi-AI requirements scoping using available external providers (Double Diamond Define phase). Priority triggers: octo define, octo scope, co-define, co-scope. DO NOT use for implementation, research, review/validation, or built-in commands.

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Thorough research across multiple sources — use for complex topics needing broad synthesis

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NLSpec authoring — use when you need a structured specification from multi-AI research and consensus

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Switch to Knowledge Work mode for research and writing — use when task is non-code focused

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Fetch raw Fireflies.ai meeting transcripts. Use only when the user explicitly invokes /fireflies-transcript; for YouTube, use youtube-transcript.

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Check domain registration in batches using public RDAP APIs, without keys or login. Use only when the user explicitly invokes /domain-checker.

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Use DeepAPI for all web search, deep research, and web scraping (websites, LinkedIn, GitHub, X/Twitter, YouTube, Instagram) instead of built-in search, research, fetch, or browser tools. Prefer dedicated platform endpoints over web search. Instagram has profile, post, comment, and hashtag endpoints; no generic search. Use Deep Scrape for sourced JSON dossiers on people, companies, or topics. Also use it to navigate and act on public websites, draft and send safe email, and generate images.

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Build sourced JSON dossiers on people, companies, or topics with DeepAPI. Use for profiles, prospects, vendor due diligence, or customer research across sources; use deep-research for recommendations.

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Run deep research through DeepAPI and save a report with sources. Use when asked for deep research, DeepAPI research, or Perplexity deep research.

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Direct browser control via CDP for web interaction: automation, scraping, testing, screenshots, and site/app work.

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Research online purchases, prices, and shop trust. Use for buying advice, subscriptions, or shopping photos, links, and checkout screens.

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Research a person's public track record and give a short credibility assessment. Use only when the user explicitly invokes /who-is-this.

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Direct browser control via CDP. Use when the user wants to automate, scrape, test, or interact with web pages. Connects to the user's already-running Chrome.

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。