「cro」の検索結果

Security best practices and guidelines for the Jarvy CLI codebase - a cross-platform development environment provisioning tool that executes system commands with elevated privileges

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 or update root and nested AGENTS.md files that document scoped conventions, monorepo module maps, cross-domain workflows, and (optionally) per-module feature maps (feature -> paths, entrypoints, tests, docs). Use when the user asks for AGENTS.md, nested agent instructions, or a module/feature map.

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Creates new agent skills following modern best practices with proper structure and documentation. Use when asked to build a new skill, organize skill resources, design skill descriptions, or validate skill structure for portability across Copilot platforms.

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。

Delegate tasks across multi-agent architectures with proper context preservation.

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。

Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems. Orchestrates context across multi-agent workflows, enterprise AI systems, and long-running projects with 2024/2025 best practices. Use PROACTIVELY for complex AI orchestration. Use when: the task directly matches context manager responsibilities within plugin agent-orchestration. Do not use when: a more specific framework or task-focused skill is clearly a better match.

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Track and optimize agent specialization during methodology development. Use when agent specialization emerges (generic agents show >5x performance gap), multi-experiment comparison needed, or methodology transferability analysis required. Captures agent set evolution (Aₙ tracking), meta-agent evolution (Mₙ tracking), specialization decisions (when/why to create specialized agents), and reusability assessment (universal vs domain-specific vs task-specific). Enables systematic cross-experiment learning and optimized M₀ evolution. 2-3 hours overhead per experiment.

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。

Guide for multi-platform skill compatibility across Claude Code, Codex, Gemini CLI, Cursor, GitHub Copilot, and other AI coding agents.

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 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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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.

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。

Check agent configuration coverage across hierarchy levels and phases. Use to ensure complete agent system coverage.

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Expert context manager specializing in information storage, retrieval, and synchronization across multi-agent systems. Masters state management, version control, and data lifecycle with focus on ensuring consistency, accessibility, and performance at scale.

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。

Retain and recall work context across sessions. Use when user asks to remember something, recall previous work, or reference past discussions. Triggered by phrases like 'remember this', 'save for later', 'recall', 'what did we discuss about'.

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。

Open protocol for AI agent interoperability enabling standardized communication between agents, applications, and humans across different frameworks

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Add a new property to the AI agents database. Use when the user wants to add, create, or introduce a new column, property, field, or feature to track across all agents in the comparison matrix. Handles all four required steps - database updates, groups.json, table display, and GitHub issue templates.

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 weekly YouTube upload plan: long-form vs YouTube Shorts mix, posting cadence, per-slot title and thumbnail pairing, hook angle, community-tab posts between uploads, and a goal balance across CTR, retention, and subscribes. Maps Shorts as top-of-funnel that points at long-form, and long-form as the loyalty and watch-time engine. Use to plan a week or a content calendar instead of shipping ad hoc. Not for writing a single title, description, or hook (use the dedicated yt skills).

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 the Crabbox wrapper for OpenClaw remote validation across Linux, macOS, Windows, and WSL2, including delegated Blacksmith Testbox proof. Report the actual provider and id.

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Market intelligence: strategy screener, popularity rankings, top movers with news correlation, quote anomalies, index/ETF constituent stocks, morning briefings, catalyst monitoring for watchlist, event-driven strategies, ETF fund flows, sector rotation, market microstructure, supply chain analysis, industry overviews, and ARK-style disruptive innovation analysis. Triggers: "筛选", "策略筛选", "排行", "热度", "异动", "成分股", "晨报", "早报", "催化剂", "事件驱动", "ETF资金流", "板块轮动", "产业链", "行业概览", "颠覆式创新", "ARK", "篩選", "排行", "異動", "成分股", "晨報", "ETF資金流", "板塊輪動", "產業鏈", "screener", "rank", "anomaly", "constituent", "morning brief", "catalyst", "event strategy", "ETF flow", "ETF资金流", "ETF申赎", "ETF資金流", "etf flow", "资金申赎", "etf 资金", "sector rotation", "supply chain", "ARK", "disruptive innovation", "板块筛选", "行业筛选", "板塊篩選", "強勢板塊", "弱勢板塊", "top sectors", "催化劑", "事件驅動", "行業概覽", "顛覆式創新", "策略篩選", "熱度"

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Value investing analysis using Graham (NCAV/net-net/defensive-investor) and Buffett (economic moat/ROE/FCF) methodologies. Covers single-stock diagnostics and batch screening for both Graham cigar-butt and Buffett quality-compounder criteria. Runs cross-statement reconciliation before scoring. Data from Longbridge CLI first, MCP fallback, WebSearch only for genuine gaps. Triggers: "格雷厄姆", "巴菲特", "捡烟蒂", "烟蒂股", "NCAV", "净流动资产", "护城河", "价值投资", "安全边际", "深度价值", "撿煙蒂", "煙蒂股", "淨流動資產", "護城河", "安全邊際", "Graham", "Buffett", "cigar butt", "net-net", "NCAV screen", "moat", "value investing", "margin of safety", "deep value", "quality compounder", "價值投資", "深度價值", "防御型投资者", "防禦型投資者"

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Financial statements, business segments, dividends, valuation multiples (PE/PB/PS), industry comparison, operating data, corporate actions, company and executive profiles, cross-stock comparison, and valuation ranking via Longbridge. Also: DCF models, value investing screens (low PE/PB, margin of safety), and behavioral finance analysis frameworks. Triggers: "财报", "三表", "利润表", "资产负债", "现金流", "估值", "PE", "PB", "分红", "公司信息", "高管", "行业估值", "并购", "DCF", "内在价值", "低估值", "安全边际", "行为金融", "小盘成长", "专精特新", "主营业务", "业务构成", "收入结构", "业务分析", "是做什么的", "是干嘛的", "公司画像", "行业排名", "行业龙头", "市场份额", "收入占比", "业务结构", "財報", "估值", "分紅", "內在價值", "安全邊際", "主營業務", "業務構成", "收入結構", "行業排名", "行業龍頭", "financial report", "income statement", "balance sheet", "valuation", "dividend", "company info", "industry valuation", "DCF", "value screen", "behavioral finance", "main business", "business composition", "revenue structure", "what does XX do", "industry ranking", "market share", "利潤表", "資產負債", "現金流", "行業估值", "併購", "行為金融", "小盤成長"

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Start, continue, keep, pause, copy, watch and stop authorised Anabasis v2 campaigns through `bun run runs`: launch a campaign under a name, go on with one from its log on another login or commit, keep one going across logins until it ends on its own terms, pause one at a clean point, copy a stopped one to another worktree, read each run's status and each login's room, reach the other hosts, and stop one and release what it held. Use when the operator asks to start, launch, resume, continue, move, keep, pause, copy or stop a campaign or a run, which logins have room, how the runs stand on any host, whether a campaign is ready for its next move, or why a move was refused. Owns the run's life on the host; run-improvement-campaign reads the table, and whole-run-investigation reads what a run did.

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).

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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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Test an uncertain Anabasis v2 path at the smallest real layer that can decide it, before a paid round is asked to: one fact from a log or a dry run, a walk of one value across the layers a change touches, a replay of every recorded log through this tree's readers, a whole round rehearsed on a seeded copy of a campaign with a script in each model seat (`bun run runs seed`, `bun run runs rehearse`), or one live seat from a seed. Use when the operator asks whether a change will work in the real system, to simulate or rehearse a round, to stand again in a position a past run stood in, what a campaign of a given size can read, or what a kill or a stop leaves behind. Not for reading a finished run (whole-run-investigation) or starting a paid one (run-management).

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Measure whether a change made the tree simpler, on numbers rather than impressions — line counts, branching, coupling, interface width, rule count, standing exemptions and model-visible prose — across one diff or a whole stack of pull requests. Use before claiming a pass reduced complexity, when deciding which pull request in the stack added the machinery, and to read a function's branch count, which no v2 gate reports.

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。