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Golang configuration library using spf13/viper — layered precedence (flag > env > file > KV > default), BindPFlag/BindPFlags, SetEnvPrefix + SetEnvKeyReplacer + AutomaticEnv, ReadInConfig + ConfigFileNotFoundError, Unmarshal + mapstructure struct tags, Sub for sub-trees, WatchConfig + OnConfigChange for hot reload, viper.New() for test isolation, and remote KV integration. Apply when using or adopting spf13/viper, or when the codebase imports `github.com/spf13/viper`. For CLI command structure alongside viper, see the `samber/cc-skills-golang@golang-spf13-cobra` skill. For general CLI architecture, see `samber/cc-skills-golang@golang-cli`.

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Defensive Golang coding against accidental bugs — nil panics, typed-nil interfaces, `append` backing-array aliasing, silent int64-to-int32 truncation, float `==` comparison, `defer` inside loops, defensive copies of slices and maps, and usable zero values. Use when a Go program panics on a nil map write or nil pointer dereference, when reviewing code for nil-safety, numeric conversion overflow, or resource lifecycle, or when designing a type whose zero value must be safe. Not for designing concurrent access with goroutines, channels, or sync primitives (→ See `samber/cc-skills-golang@golang-concurrency` skill), not for exploitable vulnerabilities such as injection, weak crypto, or leaked secrets (→ See `samber/cc-skills-golang@golang-security` skill), and not for debugging an already-failing program (→ See `samber/cc-skills-golang@golang-troubleshooting` skill).

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Comprehensive guide for Go database access — parameterized queries, struct scanning, NULLable columns, transactions, isolation levels, SELECT FOR UPDATE, connection pool, batch processing, context propagation, and migration tooling. Use when writing, reviewing, or debugging Golang code that interacts with PostgreSQL, MariaDB, MySQL, or SQLite; for database testing; or for questions about database/sql, sqlx, or pgx. Does NOT generate database schemas or migration SQL.

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Comprehensive guide for dependency injection (DI) in Golang. Covers why DI matters (testability, loose coupling, separation of concerns, lifecycle management), manual constructor injection, and DI library comparison (google/wire, uber-go/dig, uber-go/fx, samber/do). Use this skill when designing service architecture, setting up dependency injection, refactoring tightly coupled code, managing singletons or service factories, or when the user asks about inversion of control, service containers, or wiring dependencies in Go. For a specific DI library, → See `samber/cc-skills-golang@golang-google-wire`, `samber/cc-skills-golang@golang-uber-dig`, `samber/cc-skills-golang@golang-uber-fx`, or `samber/cc-skills-golang@golang-samber-do` skills.

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Provides AI and machine learning techniques for CTF challenges. Use when attacking ML models, crafting adversarial examples, performing model extraction, prompt injection, membership inference, training data poisoning, fine-tuning manipulation, neural network analysis, LoRA adapter exploitation, LLM jailbreaking, or solving AI-related puzzles.

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Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.

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Read Hyperliquid (app.hyperliquid.xyz) perp and spot market data through opencli and Hyperliquid's public info API: mark, oracle, and mid prices, 24h change, funding rates (hourly and annualized APR), open interest, volume, the L2 order book, OHLCV candles, funding history, and cross-venue funding comparisons (Hyperliquid vs Binance vs Bybit) for funding arbitrage. Use this skill whenever the user mentions Hyperliquid (HL, HL DEX, hyperliquid.xyz) or asks about its perps, spot pairs such as PURR, funding, open interest, order book, or candles. Read-only market data: no account, order, or transfer operations.

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Estimate a public company's intrinsic value with DCF, relative (peer multiple), and sum-of-the-parts (SOTP) methods, then blend them into an implied share price with upside/downside vs the market price, a WACC and terminal-growth sensitivity grid, and bull/base/bear scenarios. Use this skill whenever the user asks what a company or ticker is worth: fair value, intrinsic value, implied share price, a price target from fundamentals, whether it is overvalued or undervalued, building a DCF (WACC, terminal value, discounted cash flow), EV/EBITDA or P/E based targets, peer comparison valuation, or SOTP and conglomerate discounts. Run the model rather than answering valuation questions from memory.

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Analyze how liquid a stock is using Yahoo Finance data (yfinance): bid-ask spreads, volume and dollar volume (ADTV), top-of-book and options depth, square-root market impact and slippage estimates, turnover ratio, and Amihud illiquidity, rolled into a liquidity grade. Use this skill whenever the user asks about liquidity or trading costs — how easily a position can be entered or exited, what a large order would do to the price, spread or execution-cost estimates, order book depth, volume patterns, or liquidity comparisons — especially for small caps, penny stocks, and thinly traded names.

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Fetch financial and market data with the yfinance Python library (Yahoo Finance). Use this skill whenever the user wants stock data: current quotes and price history, financial statements (income statement, balance sheet, cash flow), options chains, dividends and splits, earnings and analyst estimates, price targets and ratings, institutional and insider holdings, news, multi-ticker comparisons, stock screens, or sector and industry data. Use it even when the user gives only a ticker symbol (AAPL, MSFT, TSLA) and the intent has to be inferred. For earnings previews or recaps, estimate revisions, valuation, correlation, liquidity, or ETF premium analysis, prefer the dedicated skill.

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Design system and templates for Claude's built-in generative UI — the show_widget tool that renders interactive HTML/SVG widgets inline in claude.ai conversations. Use this skill whenever the user wants visual or interactive output rather than plain text or markdown: charts and dashboards, diagrams, flowcharts and architecture drawings, mockups, comparison grids, illustrations, interactive explainers ("explain how X works" with a visual), or tools with sliders, toggles, and live-updating displays — including financial data shown visually and requests phrased as "show me", "visualize", "draw", or "chart this".

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[omh] Uncertain technical choice for a spike: bounded decision prototype workflow: resolve one uncertain interaction, API, performance, or integration choice with a disposable, isolated experiment whose observed result feeds planning. Use when the user says: decision-prototype, decision prototype, prototype this uncertain choice before planning, prototype before planning, prototype the uncertain choice, run a small spike, small spike, spike solution.

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Create text-driven visuals in Markdown: charts, diagrams, cards, architecture and page layouts. Charts and analytical views (bar, line, pie, heatmap, correlation, regression); reliability and operations (latency, incident, throughput, cycle time, OKR, standup, on-call); product and finance (funnel, retention, revenue, budget); process and workflow (approval, BPMN); software design and behaviour (class, state machine, sequence, C4); dependencies and impact (dependency graph, ER, causality); system architecture as HTML (layer stack, wings, zones, topology, service catalogue, request paths); infrastructure (cloud, Kubernetes, ETL, network, security, IAM, compliance); governance and people (ArchiMate, org chart, hiring); knowledge and planning (mind map, roadmap, Gantt, migration); documents (comparison, SWOT, memo, policy, catalogue, case study). Use when a document needs a diagram, chart or card. Figures render live. Not for slide decks or math notation. Not recommended: mermaid / canvas / drawio / dot.

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Crabbox/Testbox remote proof: portable provider routing, untrusted isolation, Linux/macOS/Windows/WSL2, live E2E, diagnostics, cleanup.

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Build responsive, accessible calculators, comparisons, charts, and simulations with the sandbox contract.

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AI becomes provocative and mocks the opponent during gameplay

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Retrieve and analyze AlphaFold predicted structures for a protein. Use when the user provides a specific UniProt Accession ID and wants structural confidence metrics (pLDDT), domain boundary analysis, or disorder assessment. Do not use if the user only has a protein name, gene name, or amino acid sequence — ask for a UniProt ID first.

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Authoring and running behavioral evals for a shopping or merchant agent, covering the case shape, authoring rules, code graders and judges, the run pattern, and poisoned fixtures. Load when writing eval cases or rubrics or deciding how a suite runs.

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Spatial depth design with isometric views, vanishing points, and layered elements that guide attention through 3D-like realism.

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Web scraping with anti-bot bypass, content extraction, undocumented APIs and poison pill detection. Use when extracting content from websites, handling paywalls, implementing scraping cascades or processing social media. Covers requests, trafilatura, Playwright with stealth mode, yt-dlp and instaloader patterns.

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Conduct enterprise-grade research with multi-source synthesis, citation tracking, and verification. Use when user needs comprehensive analysis requiring 10+ sources, verified claims, or comparison of approaches. Triggers include "deep research", "comprehensive analysis", "research report", "compare X vs Y", or "analyze trends". Do NOT use for simple lookups, debugging, or questions answerable with 1-2 searches.

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Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.

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Plan, deploy, operate, and troubleshoot multi-rack (rack scale) deployments of Azure Local — preintegrated racks scaling to hundreds of machines, built on Network Fabric Controller, Cluster Manager, SAN storage, and managed network fabric. Use for the Microsoft.NetworkCloud and Microsoft.ManagedNetworkFabric control plane. NOT for standard 1-16 node Azure Local, and NOT for rack-aware clusters (two racks as availability zones, up to 8 nodes, synchronous replication) — those are standard scale. WHEN: multi-rack, rack scale Azure Local, aggregation rack, compute rack, Network Fabric Controller, NFC, Cluster Manager, network fabric, isolation domain, az networkcloud, az networkfabric, multi-rack logical network, multi-rack Arc VM.

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Protein quantification from mass spectrometry data including label-free (LFQ, intensity-based), isobaric labeling (TMT, iTRAQ), and metabolic labeling (SILAC) approaches. Use when extracting protein abundances from MS data for differential 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。