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概要と使いどころ
Conducts multi-turn iterative deep research on specific topics within a codebase with zero tolerance for shallow analysis. Use when the user wants an in-depth investigation, needs to understand how something works across multiple files, or asks for comprehensive analysis of a specific system or pattern.
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Answers questions about a code repository using source file analysis. Use when the user asks a question about how something works, wants to understand a component, or needs help navigating the codebase.
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 this skill when a user wants to create, run, or analyze evaluation suites for Microsoft 365 Copilot declarative agents with the public @microsoft/m365-copilot-eval CLI. Trigger on intents such as "evaluate my agent", "test my agent", "run my evals", "create eval prompts", "add multi-turn tests", "tune evaluator thresholds", "why is my agent failing", or "set up eval environment variables".
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Prepare azd-based Azure projects for deployment: generates azure.yaml, infrastructure (Bicep/Terraform), and Dockerfiles for the Azure Developer CLI (azd) workflow. USE ONLY when the user explicitly wants to use azd as the deployment tool, or the project already has an azure.yaml file. DO NOT USE FOR: non-azd deployments, Python App Service code-only deploys (use python-appservice-deploy), or cross-cloud migration (use azure-cloud-migrate). WHEN: prepare app for azd, create azure.yaml, set up azd infrastructure, modernize app for Azure with azd, deploy with azd, function app, timer trigger, service bus trigger, event-driven function, managed identity, generate Bicep, generate Terraform, create and deploy to Azure.
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。
Understand Microsoft technologies by querying official documentation. Use whenever the user asks how something works, wants tutorials, needs configuration options, limits, quotas, or best practices for any Microsoft technology (Azure, .NET, M365, Windows, Power Platform, etc.)—even if they don't mention "docs." If the question is about understanding a concept rather than writing code, this is the right skill.
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Convert raw notes, error logs, voice dictation, or screenshots into crisp GitHub-flavored markdown issue reports. Use when the user pastes bug info, error messages, or informal descriptions and wants a structured GitHub issue. Supports images/GIFs for visual evidence.
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 Ian Xiaohei-style HTML/SVG motion illustrations for Chinese articles, scripts, storyboard scenes, workflow explanations, concept metaphors, and short video visual aids. Use when the user asks for 小黑 SVG、漫画感 HTML、分层 SVG 动效、把小黑生图做成 HTML/SVG、可编辑矢量动效、口播流程动画、手绘漫画动效、正文配图动画, or wants a Xiaohei illustration style adapted into controllable HTML/SVG/GSAP output instead of raster image generation.
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。
SUPERSEDED by /claudex-loop (formerly /crucible; its docs-aware mode covers this variant) — prefer that skill unless you explicitly want this one. Two-act plan hardening with living documentation. ACT 1 (you ↔ Claude) — Claude interviews you relentlessly about a plan, one question at a time, challenging it against your project's existing domain model and glossary (CONTEXT.md), sharpening fuzzy terms, stress-testing with concrete scenarios, cross-referencing code, and updating CONTEXT.md + ADRs inline as decisions crystallise. ACT 2 (Claude ↔ Codex) — Claude writes the locked plan to PLAN.md and OpenAI Codex adversarially reviews it in a read-only sandbox (VERDICT:APPROVED/REVISE), Claude revises and re-submits to the SAME Codex session until APPROVED or a MAX_ROUNDS cap, then you sign off before any code. Use when the user says "/grill-with-docs-codex", "grill me against the docs then have codex review", "stress-test this against our domain model then get a second model on it", or is about to build something high-stakes in a project with established terminology/ADRs and wants alignment, documentation, AND a cross-model sanity check. Builds on Matt Pocock's grill-with-docs (MIT). NOT for reviewing already-written code (use /codex:review) and NOT for trivial changes.
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。
SUPERSEDED by /claudex-loop (formerly /crucible; adds a Phase 0 research/recon pass + rebuilt interview) — prefer that skill unless you explicitly want this one. Two-act plan hardening. ACT 1 (you ↔ Claude) — Claude interviews you relentlessly about a plan or design, one question at a time, recommending an answer for each and exploring the codebase when it can answer itself, until every branch of the decision tree is resolved. ACT 2 (Claude ↔ Codex) — Claude writes the locked plan to PLAN.md and OpenAI Codex adversarially reviews it in a read-only sandbox (VERDICT:APPROVED/REVISE), Claude revises and re-submits to the SAME Codex session until APPROVED or a MAX_ROUNDS cap, then you sign off before any code. Use when the user says "/grill-me-codex", "grill me then have codex review", "grill me and stress-test the plan", "interview me about this plan then get a second model on it", or is about to build something high-stakes (auth, schema, concurrency, migrations, payments) and wants both alignment AND a cross-model sanity check before implementation. Builds on Matt Pocock's grill-me (MIT). For the docs-aware variant use /grill-with-docs-codex; if you already have a plan and want only the Codex review use /codex-review. NOT for reviewing already-written code (use /codex:review) and NOT for trivial changes.
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 Stockbee-style setup model book from momentum-burst screener candidates, then update 3-day and 5-day forward outcomes with MFE/MAE, stop-hit status, outcome tags, and cohort statistics. Use when the user wants to study Stockbee Momentum Burst examples, track failed candidates, build setup fluency, review A/B setup quality, or convert screener outputs into a learning loop rather than immediate trade 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。
This skill should be used when analyzing market breadth charts, specifically the S&P 500 Breadth Index (200-Day MA based) and the US Stock Market Uptrend Stock Ratio charts. Use this skill when the user provides breadth chart images for analysis, requests market breadth assessment, positioning strategy recommendations, or wants to understand medium-term strategic and short-term tactical market outlook based on breadth indicators. All analysis and output are conducted in English.
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。
Detect and analyze trending market themes across sectors. Use when user asks about current market themes, trending sectors, sector rotation, thematic investing, what themes are hot or cold, or wants to identify bullish and bearish market narratives with lifecycle analysis.
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Calculate contract-based futures position sizes from a direction, entry, and stop-loss, using verified per-symbol contract specs (multiplier, tick size, tick value). Use when the user asks how many futures contracts to trade, wants to size a futures position (ES, NQ, ZB, GC, CL, 6E/E6, VX, BT, ...), or is handing off a contrarian-setup-gate READY_FOR_PLAN direction/invalidation_level for sizing. Pure, offline calculation -- no API keys, no network.
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Detect crowded speculative positioning in CFTC futures markets (COT report analysis) to find contrarian setups using Jason Shapiro's methodology. Screens large-speculator ("non-commercial") net positioning across 65 futures markets (indices, rates, FX, metals, energy, crypto) via the FMP Commitment of Traders API, computes a 3-year and 26-week COT Index per market, and classifies extremes as CROWDED_LONG / CROWDED_SHORT. Use when the user asks about COT report analysis, crowded positioning, "who is trapped", speculative positioning extremes, contrarian futures setups, or wants to run Jason Shapiro-style analysis. This skill automates crowding DETECTION only (step 1 of 5) — it does not generate trade signals by itself.
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 retrieves upcoming earnings announcements for US stocks using the Financial Modeling Prep (FMP) API. Use this when the user requests earnings calendar data, wants to know which companies are reporting earnings in the upcoming week, or needs a weekly earnings review. The skill focuses on mid-cap and above companies (over $2B market cap) that have significant market impact, organizing the data by date and timing in a clean markdown table format. Supports multiple environments (CLI, Desktop, Web) with flexible API key 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。
Runs a declarative strategy spec over OHLCV bars with the manifoldbt Rust engine, pairs the fill log into round trips, and emits the eight inputs the backtest-expert skill scores. Use when the user wants to execute a backtest, measure a rule they have described, obtain win rate / average win / average loss / max drawdown from real bars, or feed backtest-expert with measured numbers instead of estimates.
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。
Screen US stocks using William O'Neil's CANSLIM growth stock methodology. Use when user requests CANSLIM stock screening, growth stock analysis, momentum stock identification, or wants to find stocks with strong earnings and price momentum following O'Neil's investment system.
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Select the best MetaTrader 5 trading robots (Expert Advisors) that have not been backtested yet, by running the MT5 Strategy Tester from the command line through a 3-round pipeline. Use when the user wants to batch-test MT5 bots/EAs, screen robots across all symbols, optimize EA parameters, or move candidate bots to finalists based on profit, drawdown, positive months/years and equity-curve criteria. Runs terminal64.exe headless; Windows + MetaTrader 5 required at run time.
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Generate Minervini-style breakout trade plans from VCP screener output with worst-case risk calculation, portfolio heat management, and Alpaca-compatible order templates (stop-limit bracket for pre-placement, limit bracket for post-confirmation). Use when user has VCP screener results and wants actionable trade plans with entry/stop/target levels and position sizing.
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。
Judge whether a market FAILED to react to news favorable to a crowded speculative position — step 2 of Jason Shapiro's COT contrarian process. Consumes a cot-contrarian-detector report (or an explicit direction) plus a Claude-curated events JSON, fetches the underlying price series with a documented fallback chain, and produces a fail-closed CONFIRMED / NOT_CONFIRMED / INSUFFICIENT_EVIDENCE verdict using a statistically validated drift-significance test (not a naive failure-ratio, which false-confirms on pure noise). Generic beyond COT — reusable for PEAD and macro-crowding news-failure checks. Use when the user asks to check news-failure confirmation, whether a crowded market "shrugged off" good/bad news, or wants to run Shapiro step 2 on a CROWDED_LONG/CROWDED_SHORT market.
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 recent market-moving news events and their impact on equity markets and commodities. Use this skill when the user requests analysis of major financial news from the past 10 days, wants to understand market reactions to monetary policy decisions (FOMC, ECB, BOJ), needs assessment of geopolitical events' impact on commodities, or requires comprehensive review of earnings announcements from mega-cap stocks. The skill automatically collects news using WebSearch/WebFetch tools and produces impact-ranked analysis reports. All analysis thinking and output are conducted in English.
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 market breadth charts, specifically the S&P 500 Breadth Index (200-Day MA based) and the US Stock Market Uptrend Stock Ratio charts. Use this skill when the user provides breadth chart images for analysis, requests market breadth assessment, positioning strategy recommendations, or wants to understand medium-term strategic and short-term tactical market outlook based on breadth indicators. Also works WITHOUT chart images by fetching CSV data directly from public sources. All analysis and output are conducted in English.
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。
Converts agent definitions between frameworks — exports to Claude Code, OpenAI, CrewAI, Lyzr, and GitHub Models formats, and imports from Claude, Cursor, and CrewAI projects. Use when the user wants to convert an agent, migrate to another framework, export to LangChain/AutoGen/CrewAI, or import from existing automation tools.
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。
Guides installation of gitagent and creation of first agent with scaffolding, configuration, and validation. Use when the user is new to gitagent, asks how to get started, wants to install gitagent, set up their first agent, or says 'how do I start?'
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。