obsidian-vault

Obsidian vault에서 이번 주(토~금) 작성/수정된 글들을 모아 뉴스레터 생성. 서브 에이전트 기반 병렬 처리로 메인 컨텍스트 절약. 기술적, 리더십적으로 외부에 공유할 만한 내용을 선별하여 정리. "뉴스레터 만들어줘", "이번 주 글 정리해줘", "weekly digest" 등의 요청 시 자동 적용.

Vault Intelligence System (vis) CLI를 활용한 Obsidian vault 시맨틱 검색, 자동 태깅, MOC 생성, 관련 문서 연결, 주제별 문서 연결, 주제 수집, 태그 통계, 지식 공백 분석, 중복 감지, 학습 리뷰 등 vault 지식 관리 전반을 지원하는 skill. vault 검색, 문서 정리, 태그, MOC, 관련 문서, 주제 수집, 중복 검사, 학습 리뷰, 지식 공백, 클러스터링, 인덱싱, 주제별 문서 연결, 태그 통계 관련 작업 시 자동 적용.

Build custom Python data sources for Apache Spark using the PySpark DataSource API — batch and streaming readers/writers for external systems. Use this skill whenever someone wants to connect Spark to an external system (database, API, message queue, custom protocol), build a Spark connector or plugin in Python, implement a DataSourceReader or DataSourceWriter, pull data from or push data to a system via Spark, or work with the PySpark DataSource API in any way. Even if they just say "read from X in Spark" or "write DataFrame to Y" and there's no native connector, this skill applies.

세션 종료 시 plan/INDEX/메모리/저널을 업데이트하고 다음 세션 재개 프롬프트 제공

Searches and retrieves MLflow documentation from the official docs site. Use when the user asks about MLflow features, APIs, integrations (LangGraph, LangChain, OpenAI, etc.), tracing, tracking, or requests to look up MLflow documentation. Triggers on "how do I use MLflow with X", "find MLflow docs for Y", "MLflow API for Z".

Retrieves MLflow traces using CLI or Python API. Use when the user asks to get a trace by ID, find traces, filter traces by status/tags/metadata/execution time, query traces, or debug failed traces. Triggers on "get trace", "search traces", "find failed traces", "filter traces by", "traces slower than", "query MLflow traces".

Load context from vault memory. Temporal queries (yesterday, last week, session history) use agf (history.jsonl) for fast session lookup. Topic queries use vis semantic search. "recall graph" generates interactive temporal graph of sessions and files. Every recall ends with "One Thing" - the single highest-leverage next action synthesized from results. Use when user says "recall", "what did we work on", "load context about", "remember when we", "prime context", "yesterday", "what was I doing", "last week", "session history", "recall graph", "session graph".

React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

Fetches aggregated trace metrics (token usage, latency, trace counts, quality evaluations) from MLflow tracking servers. Triggers on requests to show metrics, analyze token usage, view LLM costs, check usage trends, or query trace statistics.

프롬프트 작성, brainstorming, planning, design, 설계, 기능 개발 시 Prompt Contracts 프레임워크 적용. Goal/Constraints/Format/Failure Conditions 4요소로 명확한 명세 작성. "바이브 코딩" 방지. brainstorming, writing-plans, 설계, 기능 구현 관련 작업 시 자동 적용.

Use jira CLI for Jira operations including issue management, project queries, transitions, and JQL search

Instruments Python and TypeScript code with MLflow Tracing for observability. Must be loaded when setting up tracing as part of any workflow including agent evaluation. Triggers on adding tracing, instrumenting agents/LLM apps, getting started with MLflow tracing, tracing specific frameworks (LangGraph, LangChain, OpenAI, DSPy, CrewAI, AutoGen), or when another skill references tracing setup. Examples - "How do I add tracing?", "Instrument my agent", "Trace my LangChain app", "Set up tracing for evaluation"