Dspy
Skill Verifiziert AktivBuild 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
To empower developers to build complex AI systems more systematically and efficiently by leveraging DSPy's declarative programming paradigm and automatic optimization capabilities.
Funktionen
- Declarative LM programming
- Automatic prompt optimization
- Modular RAG systems and agents
- Data-driven prompt improvement
- Systematic LM pipeline development
Anwendungsfälle
- Building complex AI systems with multiple components
- Developing maintainable and portable AI pipelines
- Improving model outputs systematically with optimizers
- Creating reliable RAG systems, agents, or classifiers
Nicht-Ziele
- Manual prompt engineering without optimization
- Complex AI systems without modularity
- Simple, single-prompt LM calls requiring no systematic improvement
Execution
- info:Pinned dependenciesDependencies are listed in SKILL.md but not explicitly pinned with versions, and Python scripts lack shebangs and side-effect headers.
Installation
npx skills add davila7/claude-code-templatesFührt das Vercel skills CLI (skills.sh) via npx aus — benötigt Node.js lokal und mindestens einen installierten skills-kompatiblen Agent (Claude Code, Cursor, Codex, …). Setzt voraus, dass das Repo dem agentskills.io-Format folgt.
Qualitätspunktzahl
VerifiziertVertrauenssignale
Ähnliche Erweiterungen
DSPy
98Build 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
LangChain
99Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.
Langchain Framework
99LangChain LLM application framework with chains, agents, RAG, and memory for building AI-powered applications
Init
100Erstellt, aktualisiert oder optimiert eine AGENTS.md-Datei für ein Repository mit minimalen, hochgradig aussagekräftigen Anweisungen, die nicht entdeckbare Codierungs-Konventionen, Eigenheiten der Werkzeuge, Workflow-Präferenzen und projektspezifische Regeln abdecken, die Agenten nicht aus dem Code ableiten können. Verwenden Sie dies beim Einrichten von Agent-Anweisungen oder der Claude-Konfiguration für ein neues Repository, wenn eine vorhandene AGENTS.md zu lang, generisch oder veraltet ist, wenn Agenten wiederholt vermeidbare Fehler machen oder wenn sich die Repository-Workflows geändert haben und die Agent-Konfiguration bereinigt werden muss. Wendet einen Entdeckbarkeitsfilter an – der alles weglässt, was Claude aus README, Code, Konfiguration oder Verzeichnisstruktur lernen kann – und ein Qualitätstor, um zu überprüfen, ob jede Zeile korrekt und betrieblich relevant bleibt.
Moyu (摸鱼)
100자동으로 과잉 엔지니어링 패턴을 탐지합니다: (1) 사용자가 명시적으로 요청하지 않은 코드나 파일을 수정하는 경우 (2) 요청되지 않은 새로운 추상화 레이어(클래스, 인터페이스, 팩토리, 래퍼)를 생성하는 경우 (3) 요청되지 않은 주석, 문서, JSDoc, 타입 어노테이션을 추가하는 경우 (4) 요청되지 않은 새로운 종속성을 도입하는 경우 (5) 최소한의 편집 대신 파일 전체를 다시 작성하는 경우 (6) diff 범위가 사용자의 요청을 명백히 초과하는 경우 (7) 사용자가 "너무 많아", "거기는 건드리지 마", "X만 변경해", "간단하게", "그만"과 같은 신호를 보내는 경우 (8) 발생할 수 없는 시나리오에 대한 오류 처리, 유효성 검사, 방어적 코드 추가 (9) 요청되지 않은 테스트, 설정 스캐폴딩, 문서 생성
Create Command
100Interactive assistant for creating new Claude commands with proper structure, patterns, and MCP tool integration