remember

작성자: langchain-ai

현재 대화를 검토하고 귀중한 지식(모범 사례, 코딩 규칙, 아키텍처 결정, 워크플로우, 사용자 피드백 등)을 캡처합니다.

npx skills add https://github.com/langchain-ai/deepagents --skill remember

Review our conversation and capture valuable knowledge. Focus especially on best practices we discussed or discovered—these are the most important things to preserve.

Step 1: Identify Best Practices and Key Learnings

Scan the conversation for:

Best Practices (highest priority)

  • Patterns that worked well - approaches, techniques, or solutions we found effective
  • Anti-patterns to avoid - mistakes, gotchas, or approaches that caused problems
  • Quality standards - criteria we established for good code, documentation, or processes
  • Decision rationale - why we chose one approach over another

Other Valuable Knowledge

  • Coding conventions and style preferences
  • Project architecture decisions
  • Workflows and processes we developed
  • Tools, libraries, or techniques worth remembering
  • Feedback I gave about your behavior or outputs

Step 2: Decide Where to Store Each Learning

For each best practice or learning, choose the right destination:

-> Memory (AGENTS.md) for preferences and guidelines

Use memory when the knowledge is:

  • A preference or guideline (not a multi-step process)
  • Something to always keep in mind
  • A simple rule or pattern

Global ($DEEPAGENTS_HOME/agent/AGENTS.md): Universal preferences across all projects Project (.deepagents/AGENTS.md): Project-specific conventions and decisions

-> Skill for reusable workflows and methodologies

Create a skill when we developed:

  • A multi-step process worth reusing
  • A methodology for a specific type of task
  • A workflow with best practices baked in
  • A procedure that should be followed consistently

Skills are more powerful than memory entries because they can encode how to do something well, not just what to remember.

Step 3: Create Skills for Significant Best Practices

If we established best practices around a workflow or process, capture them in a skill.

Example: If we discussed best practices for code review, create a code-review skill that encodes those practices into a reusable workflow.

Skill Location

$DEEPAGENTS_HOME/agent/skills/<skill-name>/SKILL.md

Skill Structure

skill-name/
├── SKILL.md          (required - main instructions with best practices)
├── scripts/          (optional - executable code)
├── references/       (optional - detailed documentation)
└── assets/           (optional - templates, examples)

SKILL.md Format

---
name: skill-name
description: "What this skill does AND when to use it. Include triggers like 'when the user asks to X' or 'when working with Y'. This description determines when the skill activates."
---

# Skill Name

## Overview
Brief explanation of what this skill accomplishes.

## Best Practices
Capture the key best practices upfront:
- Best practice 1: explanation
- Best practice 2: explanation

## Process
Step-by-step instructions (imperative form):
1. First, do X
2. Then, do Y
3. Finally, do Z

## Common Pitfalls
- Pitfall to avoid and why
- Another anti-pattern we discovered

Key Principles

  1. Encode best practices prominently - Put them near the top so they guide the entire workflow
  2. Concise is key - Only include non-obvious knowledge. Every paragraph should justify its token cost.
  3. Clear triggers - The description determines when the skill activates. Be specific.
  4. Imperative form - Write as commands: "Create a file" not "You should create a file"
  5. Include anti-patterns - What NOT to do is often as valuable as what to do

Step 4: Update Memory for Simpler Learnings

For preferences, guidelines, and simple rules that don't warrant a full skill:

## Best Practices
- When doing X, always Y because Z
- Avoid A because it leads to B

Use edit_file to update existing files or write_file to create new ones.

Step 5: Summarize Changes

List what you captured and where you stored it:

  • Skills created (with key best practices encoded)
  • Memory entries added (with location)

langchain-ai의 다른 스킬

deepagents-thread-inspector
langchain-ai
로컬 Deep Agents Code SQLite 세션 저장소의 대화를 검사하고 설명합니다. LangSmith 추적 도구를 사용할 수 없을 때 대체 수단으로 사용하며, …
deepagents-python-quickstart
langchain-ai
공식 퀵스타트를 따라 Python으로 최소한의 로컬 Deep Agent를 구축하고, Tavily 대신 공급자 기본 웹 검색을 사용합니다. 사용자가 다음을 원할 때 사용합니다…
deepagents-typescript-quickstart
langchain-ai
공식 퀵스타트를 따라 TypeScript로 최소한의 로컬 Deep Agent를 스캐폴드하고, Tavily 대신 제공자 네이티브 웹 검색을 사용합니다. 사용자가…
eval-engineering
langchain-ai
에이전트 저장소와 사용자가 제공한 선택적 트레이스를 반복적으로 검사하고, 사용자와 인터뷰하며, Harbor 평가를 한 번에 하나씩 생성, 실행, 감사합니다. 용도:…
LangChain RAG Pipeline
langchain-ai
이 스킬을 호출하여 검색 증강 생성(RAG) 시스템을 구축하세요. 문서 로더, RecursiveCharacterTextSplitter, 임베딩(OpenAI) 등을 다룹니다.
LangChain Structured Output & HITL
langchain-ai
langchain-structured-output-&-hitl — AI 에이전트를 위한 설치 가능한 스킬로, langchain-ai/langchain-skills에서 게시되었습니다.
LangSmith Datasets
langchain-ai
이 스킬은 평가 데이터셋을 트레이스에서 생성하거나 LangSmith에 데이터셋을 업로드하거나 데이터셋을 쿼리할 때 호출하세요. 데이터셋 유형(final_response, …)을 다룹니다.
langsmith-evaluator
langchain-ai
LangSmith 평가 파이프라인을 구축할 때 이 스킬을 호출하세요. 세 가지 핵심 구성 요소를 다룹니다: (1) 평가자 생성 - LLM-as-Judge, 사용자 정의 코드; (2)…