'spy' 검색 결과

DSPy로 질문 응답과 문서 검색을 결합한 AI 처리를 만듭니다. 입출력을 정의한 구성 요소를 조합하고 학습 예시와 평가 지표로 프롬프트를 자동 조정합니다.

• 문서 검색을 결합한 질문 응답 만들기
• 학습 예시로 프롬프트 조정하기
• 텍스트에서 구조화된 데이터 추출하기

Build DSPy 3.2.x programs through spec, program, metric and baseline; extend to optimization and export when requested and justified by task budget. Orchestrates the other four DSPy skills (dspy-fundamentals, dspy-evaluation-harness, dspy-gepa-optimizer, dspy-rlm-module) in the correct order. Use for greenfield DSPy builds; prototypes may stop at a validated baseline.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build AI workflows with DSPy by defining inputs and outputs and combining reusable components. Create question answering systems that use retrieved documents, tool-using agents, and automatically optimized prompts, then compare results with evaluation data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build question answering systems that use retrieved documents
• Optimize prompts using examples and evaluation metrics
• Combine AI tasks into reusable processing stages

Build AI workflows with DSPy by defining inputs and outputs and combining reusable components. Create question answering systems that use retrieved documents, tool-using agents, and automatically optimized prompts, then compare results with evaluation data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build question answering systems that use retrieved documents
• Optimize prompts using examples and evaluation metrics
• Combine AI tasks into reusable processing stages

Decision rubric for promoting a prose prompt into a typed DSPy Signature. This is an ENHANCE overlay on top of the [[dspy]] library skill: it does NOT teach DSPy syntax — it answers the coder-agent decision "when do I stop hand-writing a prompt string and declare it as a `dspy.Signature`, and how do I name/describe its fields so the optimizer and the calling code both get a clean contract." Activate when: a prompt string grows past ~50 lines; the LM output is consumed by code (parsed, branched on, stored) rather than read by a human; the same prompt is reused across >1 call site; or a teammate asks "should this be a Signature?". Do NOT activate for one-shot throwaway prompts, or for HOW-TO questions about DSPy modules /optimizers/compile — defer those to the [[dspy]] skill and the [[agentsop-dspy]] workflow skill. Search keywords: typed prompt, structured prompt, DSPy Signature, prompt as a function, prompt contract, when to formalize a prompt.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyze compiled .NET and C# programs by identifying managed files, removing obfuscation, inspecting intermediate instructions, and debugging runtime behavior. Save extracted configuration and code changes as evidence for further review.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inspect compiled .NET program behavior
• Analyze obfuscated C# programs
• Extract malware configuration and communication destinations

Write idiomatic DSPy 3.2.x programs — typed Signatures, dspy.Module subclasses, Predict/ChainOfThought/ReAct/ProgramOfThought, and save/load. Use this when starting any new DSPy project or when fixing non-idiomatic DSPy code (hard-coded prompts, ad-hoc string templates, untyped outputs, non-serializable classes).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build DSPy evaluation harnesses with rich-feedback metrics that are essential for GEPA optimization. Use when writing a metric function, calling dspy.Evaluate, splitting dev/val sets, debugging "why is my optimizer not improving?", or designing CI-ready DSPy eval suites.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Optimize DSPy programs with dspy.GEPA — a reflective/evolutionary optimizer to consider against task-specific baselines within an authorized evaluation budget. Use when the user says optimize, compile, GEPA, reflective optimization, or "make this program better" and a DSPy program + metric + trainset exist.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ENHANCE overlay on [[dspy]] — the upfront rubric for choosing a reasoning SHAPE (Predict / ChainOfThought / ReAct / ProgramOfThought) BEFORE you write a prompt or pick an optimizer. The local `dspy` skill lists the modules but never surfaces the *selection criterion*: reasoning shape is chosen by task structure, not by reflexively defaulting to CoT. Activate every time a new LM-calling node/step is added to a pipeline. Do NOT activate for one-shot prompts, optimizer/teleprompter choice (that is the dspy SOP's job), or non-LM control flow. Search keywords: chain of thought vs ReAct, when to use CoT, reasoning type, ReAct vs CoT vs PoT, which dspy module, predict vs chain of thought.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Investigate SGLang runs that freeze across multiple GPUs. It uses stack traces, communication logs, GPU memory dumps, and comparisons between processes to locate state differences, trace their causes, and guide fixes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Locate where a multi-GPU inference run freezes
• Compare logs from distributed processes
• Trace the first difference in process state

Inspect game code, data files, live memory, and graphics captures to understand the internals needed for a mod. Choose tools for the game’s technology, document findings, and validate unknown file formats by decoding and re-encoding them.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding boss behavior or damage calculations
• Investigating an undocumented game file format
• Locating player coordinates in live memory

An overview of the core data API of ObsPy, a Python framework for processing seismological data. It is useful for parsing common seismological file formats, or manipulating custom data into standard objects for downstream use cases such as ObsPy's signal processing routines or SeisBench's modeling API.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Reverse-engineer successful affiliate strategies from competitors. Use this skill when the user asks about spying on competitors, researching what other affiliates promote, analyzing competitor affiliate sites, understanding how top affiliates in a niche make money, or says "what programs does X promote", "how does [site] make money", "what affiliate strategy does this site use", "spy on competitor affiliates", "reverse engineer affiliate site", "copy what works in my niche", "who are the top affiliates in X niche", "what content gets traffic in my niche", "competitor affiliate analysis".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Operating SOP for DSPy (Stanford NLP) — the declarative framework for "programming, not prompting" language models. Activate when the user says any of: "use DSPy", "compile a prompt", "optimize prompts/programs", "MIPRO/MIPROv2", "BootstrapFewShot", "GEPA", "Signatures + Modules", "teleprompter", "auto-tune prompts for a different LM", or whenever a brittle hand-crafted prompt pipeline needs to be turned into a *compiled*, measurable, swappable program. Do NOT activate for one-shot prompt tweaks, no-metric exploratory work, or pipelines where prompts must remain human-authored verbatim — use raw prompting or LangChain templates instead.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use ANTsPy/antspyx for medical image IO, ANTsImage metadata, image operations, registration, segmentation, visualization, and learning helper workflows.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use dspy.RLM (Recursive Language Model) for reasoning over contexts too large to fit in an LLM's working window — entire codebases, long logs, massive documents, or multi-step data exploration that needs a sandboxed Python REPL. Use when the input is >100k tokens, needs recursive chunking, or benefits from the LLM writing and running code to probe data.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

DSPy is a Python framework from Stanford NLP that replaces hand-written prompts with code: you declare LLM tasks as typed signatures, compose them into modules, and let optimizers (BootstrapFewShot, MIPROv2, GEPA) tune the prompts and few-shot examples against your metric. Use when the user asks about DSPy, signatures, ChainOfThought, ReAct, teleprompters or optimizers, or wants to stop hand-tuning prompts.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Construa sistemas de IA complexos com programação declarativa, otimize prompts automaticamente, crie sistemas RAG modulares e agentes com DSPy - framework de Programação Sistemática de Modelos de Linguagem da Stanford NLP

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build a Python system that follows DNS history, domain registration records, and certificate information from known suspicious domains or IP addresses. Map related attack infrastructure, identify shared connections, and monitor for newly discovered domains matching known patterns.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding related domains from a known suspicious IP
• Mapping attacker command-and-control infrastructure
• Monitoring domains that match known naming patterns

Convert long Markdown specifications, reports, and plans into a single HTML document with a sticky table of contents, search, section tracking, and code-copy buttons. Apply previously configured brand colors and layout styles.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turn long specifications into navigable HTML documents
• Add search and a table of contents to reports
• Apply consistent brand styling to technical documents

Investigate how a limited Linux account could gain root, the system administrator's privileges. The skill guides checks of permissions, scheduled tasks, services, and known vulnerabilities, while outlining evidence defenders can monitor during authorized security assessments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assessing Linux privilege escalation paths
• Practicing privilege escalation in CTF challenges
• Reviewing sudo and scheduled task configurations

ObsPy allows you to easily download earthquake data from various data centers/networks through its client API. This is an overview of available services and a demo of the client API for International Federation of Digital Seismograph Networks (FDSN). Use this if you need to download earthquake research data.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.