リサーチ・学習のスキル

Earnings analysis — pre- and post-earnings. Pre-earnings preview: prior-guidance review, recent-events tracking, last call's Q&A, and a key-things-to-watch framework for an upcoming release. Post-earnings: two tiers — a fast in-chat summary card (default) and a full Markdown research report (on request). Covers beat/miss, segments, margins, guidance, estimates, valuation. US / HK / A-share. Use whenever the user wants an earnings preview or a post-earnings / quarterly-results writeup. Triggers: "earnings update", "quarterly results", "Q1/Q2/Q3/Q4 results", "earnings report", "post-earnings analysis", "beat/miss", "guidance update", "earnings preview", "pre-earnings", "what to watch this earnings", "before earnings", "财报分析", "业绩更新", "季度业绩", "季报", "年报", "盈利分析", "财报点评", "财报前瞻", "业绩前瞻", "财报预览", "上季度指引", "財報分析", "業績更新", "季度業績", "季報", "年報", "財報點評", "財報前瞻", "業績前瞻", "財報預覽".

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。

Researches one B2B SaaS competitor across 13 dimensions (company, product, ICP, pricing, reviews, content, launches, search and AI visibility, tech stack, hiring, go-to-market, social, paid ads) and writes a sourced dossier with an executive summary, a threat level, strengths and weaknesses, and a data gaps section. Run it once per competitor, then compare. Triggers: "competitor analysis", "competitor research on", "what is [competitor] doing", "compare us to", "competitive landscape", "battlecard prep". Run after /company-context and /icp-research. Not for researching your own company (use /company-context).

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。

Analyses sales call transcripts (and churn or renewal calls) to find why deals are won, lost, kept or churned. Reads each call across six dimensions (product, messaging, sales process, pricing, competition, customer context), pulls verbatim quotes with the speaker named, counts how often each pattern appears, and writes findings with recommendations. Works with any recorder export or pasted text. Triggers: "win/loss analysis", "why do we lose", "why do we win", "analyse these sales calls", "lost deals", "churn analysis", "objection patterns". Feeds /positioning and /messaging. Not for general meeting summaries or competitor research (use /competitor-research).

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Builds an ideal customer profile from evidence: case studies, testimonials, customer logos, solutions pages, reviews and any sales notes you have. Produces market sizing layers, firmographic segments, a champion and an economic buyer persona, a negative ICP, intent signals, customer proof points and a voice-of-customer section in the buyers' own words, every claim sourced. Triggers: "ICP research", "ideal customer profile", "who are our customers", "customer segments", "buyer personas", "who should we target". Run after /company-context. Not for competitor analysis (use /competitor-research) or call transcript patterns (use /win-loss).

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。

Управление consent в learning.tracking_consent — opt-in / opt-out / status / revoke. Обязательное условие для работы stage_evaluator worker (WP-253 Блок 2). GDPR-совместимо.

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。

Записать W-рефлексию (мировоззренческий слот RCS) в learning.w_reflections. Используется Диагностом R28 (MIM.R.009) или вызывается пользователем после рефлексивной сессии. Влияет на gate Д→П (FORM.089 §5.2).

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。

Диагностика ступени мастерства (Диагност R28, FORM.089 §6.1 v5.0) прямо в VS Code / claude.ai. До 6 вопросов, ~3 мин. Сохраняет cp-профиль в канонический журнал learning.cp_assessments — через MCP-инструмент (browser) или напрямую в Neon (VS Code). Запускай когда: пилот говорит «пройди диагностику», «какая моя ступень», «/diagnose» — или ПРОАКТИВНО когда видишь пустой cp_profile или нового пользователя без данных.

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。

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

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。

AgentDB Reinforcement Learning Training operates on 3 fundamental principles:

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。

Skill for discovering and researching autonomous AI agents, tools, and ecosystems using the AgentFolio directory.

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 research analyst specializing in comprehensive information gathering, synthesis, and insight generation. Masters research methodologies, data analysis, and report creation with focus on delivering actionable intelligence that drives informed decision-making.

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Scan the current chat session for durable learnings (clarifications, corrections, decisions, pitfalls) and update .agent/memory.md. Use after critical review and before concluding 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。

Layer 4: Learning and Pattern Extraction for Cognitive Surrogate 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。

Self-improvement loop for multi-agent workflows. Diagnose failures, improve tool descriptions, and learn from success/failure patterns.

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Expert knowledge synthesizer specializing in extracting insights from multi-agent interactions, identifying patterns, and building collective intelligence. Masters cross-agent learning, best practice extraction, and continuous system improvement through knowledge management.

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Expert error coordinator specializing in distributed error handling, failure recovery, and system resilience. Masters error correlation, cascade prevention, and automated recovery strategies across multi-agent systems with focus on minimizing impact and learning from failures.

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Extend the Strategy Coach agent with new capabilities. Use when user says "add coaching phase", "new research pillar", "extend agent", "add strategic output", or asks to enhance the AI coaching methodology.

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 and qualify real estate agencies in a given suburb

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Guide for authoring specialized AI agents. Use when creating, updating, or improving agents, choosing models, defining focus areas, configuring tools, or learning agent best practices.

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Add new learning resources (books, articles, courses, papers) to the appropriate resources.md file. Use when user mentions adding, saving, or tracking learning 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。

AI Engine Optimization - semantic triples, page templates, content clusters for AI citations

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 deep research on a single competitor. Produces a dossier with executive summary, position + messaging, status-quo alternatives, key differentiators, pricing position, where-they-win / where-they-lose, threat assessment, and what to watch. Writes to marketing/competitors/MMYY-{competitor-slug}.md. Triggers - "competitor research", "competitive analysis", "research competitor X", "battlecard prep", "analyze [competitor name]

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 structured ICP documentation by scraping case studies, testimonials, and solutions pages from your website. Produces firmographics, champion + economic buyer personas, pain points, decision criteria, anti-ICP, customer proof points, and voice-of-customer synthesis. Writes to marketing/icp/ICP.md as the canonical ICP every downstream skill reads. Triggers - "icp research", "ideal customer profile", "persona research", "customer segments", "who are our customers", "build ICP"

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。