Results for “spy”

Scrapes LinkedIn job postings using the JobSpy library (python-jobspy). Use this skill whenever the user wants to find jobs on LinkedIn, search for open roles, pull job listings, build a job pipeline, source job targets for GTM research, or monitor hiring signals. Even if the user just says "find me some jobs" or "what roles is [company] hiring for", use this skill. It runs a local Python script that outputs a CSV of job postings with title, company, location, salary, job type, description, and direct URLs.

The English summary is being prepared. Showing the original description.

.NET / C# 二进制逆向。当目标是 .NET assembly（PE 头含 CLR、.exe/.dll 托管程序）、C# 编译产物（含 NativeAOT）、红队 Sharp* 工具（Rubeus / SharpHound / SharpHound 等）、.NET 混淆程序（ConfuserEx / SmartAssembly / Babel / Eazfuscator）、.NET loader / info-stealer / 套壳 malware 时使用。优先用 dnSpyEx + de4dot，需要 AI 直接操作时联动 dnSpy MCP。不用于纯 native 二进制（走 reverse-engineering / ida-reverse）。

The English summary is being prepared. Showing the original description.

获取并转换微信公众号/网页文章为 Markdown 的封装 Skill，完整支持 WeSpy 的单篇抓取、微信专辑批量下载、专辑列表获取、HTML/JSON/Markdown 多格式输出。Use when user asks to 抓取微信公众号文章、公众号专辑批量下载、URL 转 Markdown、保存微信文章、mp.weixin.qq.com to markdown.

The English summary is being prepared. Showing the original description.

Use when you need to write fast unit tests for Quarkus applications — including pure tests with @ExtendWith(MockitoExtension.class), @QuarkusTest with @InjectMock for full CDI mock replacement, @InjectSpy for partial CDI bean mocking, REST Assured for resource-focused tests, @ParameterizedTest with @CsvSource / @MethodSource, QuarkusTestProfile for test-specific configuration overrides, and naming conventions (*Test → Surefire, *IT → Failsafe). For framework-agnostic Java use @131-java-testing-unit-testing. This should trigger for requests such as Add or improve unit tests in a Quarkus project; Reduce slow @QuarkusTest usage with Mockito-first tests; Add @InjectSpy partial mocking or QuarkusTestProfile configuration in Quarkus tests; Convert repeated test methods to @ParameterizedTest with @CsvSource or @MethodSource; Write fast pure unit tests for Quarkus services. Part of Plinth Toolkit

The English summary is being prepared. Showing the original description.

Neutral, framework-agnostic decision tree for project kickoff: "which agent / RAG / LLM framework should I reach for?" Synthesizes the ecosystem sections of 7 landmark-project SOPs (LangGraph, LlamaIndex, DSPy, CrewAI, vLLM, Aider, Dify) into one layered rubric. Core stance: frameworks are LAYERS, not competitors — a real project usually combines DSPy (compile) + LlamaIndex (retrieve) + LangGraph (orchestrate) + vLLM (serve), and you choose ONE per layer, not one to rule all. Use when starting any LLM/agent/RAG project, or whenever the "which framework?" question is asked. Deliberately neutral — unlike vendor docs and the LangChain-biased `framework-selection` on skill.sh, this skill has no horse in the race.

The English summary is being prepared. Showing the original description.

Play board games on ShellGames.ai — Chess, Poker, Ludo, Tycoon, Memory, and Spymaster. Use when the agent wants to play games against humans or other AI agents, join tournaments, chat with players, check leaderboards, or manage a ShellGames account. Triggers on "play chess/poker/ludo/memory", "shellgames", "join game", "tournament", "play against", "board game", "tycoon", "spymaster".

The English summary is being prepared. Showing the original description.

.NET / C# 二进制逆向。当目标是 .NET assembly（PE 头含 CLR、.exe/.dll 托管程序）、C# 编译产物（含 NativeAOT）、红队 Sharp* 工具（Rubeus / SharpHound / SharpHound 等）、.NET 混淆程序（ConfuserEx / SmartAssembly / Babel / Eazfuscator）、.NET loader / info-stealer / 套壳 malware 时使用。优先用 dnSpyEx + de4dot，需要 AI 直接操作时联动 dnSpy MCP。不用于纯 native 二进制（走 reverse-engineering / ida-reverse）。

The English summary is being prepared. Showing the original description.

Program language models instead of prompting them — DSPy signatures, modules, and optimizers that tune prompts automatically.

The English summary is being prepared. Showing the original description.

Downloads a competitor's live ad creatives — the actual MP4s and JPEGs — from the Meta Ad Library into a local swipe folder, through the Novoads API. One sweep per competitor, priced by a live estimate before anything is charged, media pulled the moment the response lands because the CDN links expire, and every ad carried with its permanent Ad Library URL, the page running it, and how many audiences the creative is running against, closing with the three most established and why. Use when asked to spy on competitors, find what ads a brand is running, pull a competitor's video or static creatives, build or refresh a swipe file, check who is outspending you on Meta, or source a reference ad to recreate. It finds, ranks and files ads; it does not rebuild them — a static becomes your own ads, or a reusable template, via clone-image-ad. It writes no craft analysis and no creative brief. A sweep that finds nothing is a result, never an apology.

The English summary is being prepared. Showing the original description.

Declarative programming framework for optimizing LLM prompts through compilation and automatic tuning

The English summary is being prepared. Showing the original description.

DSPy: declarative LM programs, auto-optimize prompts, RAG.

The English summary is being prepared. Showing the original description.

Jest best practices, patterns, and API guidance for JavaScript/TypeScript testing. Covers mock design, async testing, matchers, timer mocks, snapshots, module mocking, configuration, and CI optimization. Baseline: jest ^30.5.2. Triggers on: jest imports, describe, it, test, expect, jest.fn, jest.mock, jest.spyOn, mentions of "jest", "unit test", "test suite", or "mock".

The English summary is being prepared. Showing the original description.

テスト品質のレビュー（bun:test）。`bun test` の全通過、エンティティのビルダー直接検証と createTestDb による実 SQLite E2E の使い分け、実ネットワーク（JRA）非依存、回帰テストの存在（ImportData の一致条件・枠番の JRA 割当規則・生年計算）、回帰テストの削除/skip 化の禁止、新機能・バグ修正へのテスト追随、契約の検証 vs 実装詳細への過剰結合、console spy と一時ファイルの後始末を検証。Use when user says 'テストレビュー', 'review test', 'テスト品質チェック', or 'カバレッジ確認'.

The English summary is being prepared. Showing the original description.

Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming

The English summary is being prepared. Showing the original description.

Using NSubstitute to create test doubles (Mock, Stub, Spy) specialized skill. Used when isolating external dependencies, simulating interface behavior, and validating method calls. Covers Substitute.For, Returns, Received, Throws and complete guidance. Keywords: mock, stub, spy, nsubstitute, mock, test double, test double, IRepository, IService, Substitute.For, Returns, Received, Throws, Arg.Any, Arg.Is, isolate dependencies, simulate external services, dependency injection testing

The English summary is being prepared. Showing the original description.

Retrieve stock, index, and ETF quotes with prices, changes, and short price histories. Check selected ticker symbols or a curated market overview, while distinguishing unavailable quotes from valid prices and identifying cache fallbacks.

• Check the price and movement of a specific stock
• Compare quotes for several ticker symbols
• Get a quick overview of selected market prices

Test trading strategies against historical market data and collect performance metrics. The skill checks whether data is real or synthetic and can digitally sign results to help detect tampering before a strategy moves into live trading.

• Evaluate a strategy against historical market data
• Compare returns, risk measures, and trading results
• Prepare signed backtest results for live-trading review

Run demanding neural-trader backtests, parameter comparisons, and model training in an Anthropic Managed Agent cloud container. Estimate costs, test inputs on a small run, collect performance metrics and CSV files, and verify signed results before storage.

• Testing trading strategies over several years
• Comparing many parameter combinations
• Training LSTM or Transformer models in the cloud

Classify market conditions as bullish, bearish, ranging, or volatile to guide strategy selection and historical testing. Distinguish synthetic demonstrations from analysis of actual price data, with confidence levels and historical comparisons where available.

• Reviewing market conditions for a stock symbol
• Choosing a strategy suited to market conditions
• Checking market conditions before a backtest

Test desktop application security across local storage, communication between processes, network traffic, and update channels. Map where trust and permissions change, then investigate weaknesses in credentials, access checks, and update verification.

• Checking how desktop apps store credentials
• Reviewing authentication between local processes
• Inspecting app traffic and API access controls

Measure Python execution time and memory use to locate slow or resource-heavy code. Use profiling results to guide improvements in data structures, caching, input and output operations, algorithms, and parallel processing.

• Finding slow functions in Python applications
• Reducing memory use and investigating leaks
• Improving data processing and database queries

Compare money moving into and out of US exchange-traded funds to assess sector rotation and appetite for risk. Produce a structured view of broad market, growth versus value, bond, and thematic fund flows.

• Review broad US equity ETF inflows and outflows
• Identify sectors attracting or losing capital
• Compare growth and value fund flows

Analyze malware communication with command servers to understand periodic check-ins, message formats, encoded data, and backup destinations. Produces a structured analysis report and network detection rules based on captured traffic and malware code.

• Investigating malware communication in captured traffic
• Measuring periodic check-in intervals and timing variation
• Mapping command servers and backup destinations

Investigate slow Node.js, Python, and Go applications by measuring CPU use, memory, database queries, and data transfer. Identify bottlenecks, inspect frontend bundle sizes, run load tests, and compare measurements before and after changes to verify improvements.

• Investigating slow API responses
• Tracking memory growth and suspected leaks
• Analyzing bundle growth after adding dependencies