7,321 件 ・ 関連度順
概要と使いどころ
Use for CRM audits, duplicate detection, missing or stale fields, job changes, domain checks, acquisitions, parent-child relationships, repair plans, prevention, monitoring, and evaluation. Supports HubSpot, Salesforce, Attio, and mapped exports. Start with read-only analysis. Skip outreach writing, CRM interface design, and unrelated analysis.
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。
Deprecated alias. This skill was renamed to `deepline-scoring`. Use when a user or script still invokes `/niche-signal-discovery`: immediately load and follow the `deepline-scoring` skill instead. Triggers: ICP analysis, niche signals, won vs lost analysis, differential signals, signal discovery, ICP signal report, account scoring signals, lead scoring, first-party signals, buyer signals.
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 for architecture review, dependency graphs, coupling analysis, or circular dependency detection. Establishes project module namespaces and traces imports plus Claude plugin hook, skill, script, PEP 723, and MCP boundaries. Emits partitioned Mermaid graphs with cycle, coupling, and ownership signals; verifies contract and consequence before assigning severity. Registers diagrams as codebase-analysis artifacts.
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。
This skill should be used when analyzing Claude Code session transcripts, reviewing agent performance, finding anti-patterns or tool misuse, mining workflow patterns, running kaizen analysis, debugging agent behavior, or performing session forensics.
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 transcript analysis findings into actionable improvements. Triggers on "generate hooks from findings", "improve agent", "fix anti-pattern", "kaizen improvement", "generate hook proposals", or "create improvement plan". Provides templates for hook generation, agent prompt refinement, skill patches, CLAUDE.md updates, and script automation from analysis data.
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 or reviews a pre-analysis plan before data collection — registry choice (OSF, AEA RCT Registry, AsPredicted, registered reports), PAP structure, models and decision rules locked in advance, analysis code run on simulated data, contingencies for attrition, failed manipulations, and exclusions, deviation records, and timeline. Operationalizes the pre-data-collection side of DA-RT. Use when the user asks to draft or audit a pre-registration or PAP, choose a registry, decide what to lock versus leave exploratory, or handle a later deviation. Hypotheses and estimands come from hypothesis-building, post-hoc reporting from methods-reporting.
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。
Deterministic Software Graph analysis via Ontoly CLI and MCP. Use when: (1) Codebase architecture, dependency, route, service, module, configuration, or impact questions need graph evidence, (2) A repository should be analyzed before source search, (3) You need a persistent SoftwareGraph.json for validation, MCP, or agent workflows. Triggers: "build software graph", "trace route", "impact analysis", "architecture summary", "dependency graph", "what uses this", "Ontoly".
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。
Delegates coding tasks to Codex CLI for prototyping, debugging, and code review. Use when: (1) Backend/logic implementation, (2) Algorithm design and optimization, (3) Bug analysis and debugging, (4) API/database code generation, (5) Code quality review and refactoring. Triggers: "implement algorithm", "debug", "analyze code", "backend task", "API implementation", "optimize performance", "refactor", "generate prototype", "code review". IMPORTANT: Default sandbox="read-only" (analysis/review/patch generation); use sandbox="workspace-write" only when codex must run tests/builds to verify. Always request unified diff patches only. Supports multi-turn sessions via SESSION_ID.
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。
Codified expertise for quality control, non-conformance investigation, root cause analysis, corrective action, and supplier quality management in regulated manufacturing. Informed by quality engineers with 15+ years experience across FDA, IATF 16949, and AS9100 environments. Includes NCR lifecycle management, CAPA systems, SPC interpretation, and audit methodology. Use when investigating non-conformances, performing root cause analysis, managing CAPAs, interpreting SPC data, or handling supplier quality issues.
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。
[AUTO-INVOKE] MUST be invoked when debugging failed on-chain transactions. Covers transaction receipt analysis, gas diagnosis, calldata decoding, revert reason extraction, and state verification. Trigger: any task involving failed tx analysis, revert debugging, or on-chain transaction troubleshooting.
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。
Comprehensive AI/ML development guide for LangChain, LangGraph, and ML model integration in FastAPI. Use when building LLM applications, agents, RAG systems, sentiment analysis, aspect-based analysis, chain orchestration, prompt engineering, vector stores, embeddings, or integrating ML models with FastAPI endpoints. Covers LangChain patterns, LangGraph state machines, model deployment, API integration, streaming, error handling, and best practices.
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 competitive analyst specializing in competitor intelligence, strategic analysis, and market positioning. Masters competitive benchmarking, SWOT analysis, and strategic recommendations with focus on creating sustainable competitive advantages.
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。
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。
Account assets, equity and fund positions, P&L, cash flow records, account statements, margin ratios, buy-power estimates, order management, and DCA recurring investments via Longbridge (most require Trade permission). Frameworks: portfolio diagnosis, rebalancing, asset allocation, risk analysis (VaR/CVaR), performance attribution, and tax-loss harvesting. Triggers: "持仓", "账户", "盈亏", "资产", "对账单", "下单", "买入", "卖出", "撤单", "定投", "组合诊断", "再平衡", "资产配置", "风险分析", "绩效归因", "税损收割", "持倉", "賬戶", "盈虧", "對賬單", "下單", "買入", "賣出", "組合診斷", "再平衡", "稅損收割", "positions", "portfolio", "P&L", "order", "buy", "sell", "DCA", "statement", "risk analysis", "rebalancing", "tax harvesting", "我的风险", "持仓风险", "风险敞口", "資產", "資產配置", "風險分析", "績效歸因", "撤單"
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。
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"
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).
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。
DeFi Protocol Analysis — analyzes TVL, revenue, yields, liquidity, security, governance, and competitive positioning with a composite DeFi Score (0-100)
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。
Crypto Fundamental Analysis — evaluates project viability, team quality, technology, adoption metrics, and competitive moat with a composite Fundamental Score (0-100)
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。
Crypto Technical Analysis — analyzes trend, momentum, volume, chart patterns, and market structure across 24/7 crypto markets with a composite Technical Score (0-100)
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。
Customer retention system — churn analysis, retention tactics, loyalty programs, and engagement scoring. Use when "retention", "reduce churn", "keep customers", "loyalty program", "customer retention", "churn prevention", "churn analysis", "engagement scoring", "win-back", "customer lifetime value", or any request to analyze, prevent, or reduce customer churn.
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。
Competitive intelligence teardown — 5-layer analysis (signals, product, marketing, positioning, strategy) plus sales battlecard. Use when "competitor analysis", "competitive teardown", "who are our competitors", "battlecard", "competitive intel", "compare us to X", "what is X doing", or any request to research, analyze, or position against competitors.
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 system that makes HTML output look like a professional consulting deliverable instead of default AI styling. Use WHENEVER generating any HTML document — report, analysis memo, proposal, research summary, dashboard, artifact — and when the user says 「HTMLで資料」「HTMLでまとめて」「レポートにして」「コンサル風に」「資料っぽく」「デザインがダサい」 or asks to restyle existing AI-generated HTML. Replaces boxes, borders, gradients, emoji and rainbow colors with whitespace-driven, typography-first, print-ready (A4) design.
Use this skill whenever the user wants to analyze binaries with radare2/r2 from the command line, including reverse engineering, disassembly, function analysis, strings/import inspection, patching, binary diffing, hex inspection, or r2 scripting. Also use it when the user mentions PE/ELF/Mach-O/DEX/WASM files together with CLI analysis, `rabin2`, `rasm2`, `radiff2`, `r2pipe`, or asks for radare2 command help on Windows/Linux/macOS.
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。
audioFlux is a deep learning tool library for audio and music analysis and feature extraction, supporting dozens of time-frequency transforms and hundreds of feature combinations for classification, separation, MIR, and ASR tasks.
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。