remember

द्वारा langchain-ai

वर्तमान वार्तालाप की समीक्षा करें और मूल्यवान ज्ञान — सर्वोत्तम प्रथाएँ, कोडिंग परंपराएँ, आर्किटेक्चर निर्णय, कार्यप्रवाह और उपयोगकर्ता प्रतिक्रिया — एकत्र करें,…

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-ai की और Skills

deepagents-thread-inspector
langchain-ai
स्थानीय Deep Agents Code SQLite सत्र भंडार में वार्तालापों का निरीक्षण और व्याख्या करें। LangSmith ट्रेस टूलिंग अनुपलब्ध होने पर फ़ॉलबैक के रूप में उपयोग करें, इसके लिए…
deepagents-python-quickstart
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आधिकारिक क्विकस्टार्ट का पालन करके पायथन में एक न्यूनतम स्थानीय डीप एजेंट तैयार करें, टैविली के बजाय प्रदाता-मूल वेब खोज का उपयोग करें। उपयोग करें जब उपयोगकर्ता चाहता है…
deepagents-typescript-quickstart
langchain-ai
आधिकारिक क्विकस्टार्ट का पालन करके TypeScript में एक न्यूनतम स्थानीय Deep Agent तैयार करें, Tavily के बजाय प्रोवाइडर-नेटिव वेब खोज का उपयोग करें। उपयोग करें जब उपयोगकर्ता…
eval-engineering
langchain-ai
किसी एजेंट रिपॉजिटरी और वैकल्पिक उपयोगकर्ता-प्रदत्त ट्रेस का पुनरावृत्त रूप से निरीक्षण करें, उपयोगकर्ता का साक्षात्कार लें, और एक-एक करके Harbor मूल्यांकन बनाएं, चलाएं, और ऑडिट करें। इसके लिए उपयोग करें…
LangChain RAG Pipeline
langchain-ai
इस कौशल का उपयोग किसी भी पुनर्प्राप्ति-संवर्धित पीढ़ी (RAG) प्रणाली के निर्माण में करें। इसमें दस्तावेज़ लोडर, RecursiveCharacterTextSplitter, एम्बेडिंग (OpenAI),… शामिल हैं।
LangChain Structured Output & HITL
langchain-ai
langchain-structured-output-&-hitl — AI एजेंटों के लिए एक इंस्टॉल करने योग्य कौशल, जो langchain-ai/langchain-skills द्वारा प्रकाशित है।
LangSmith Datasets
langchain-ai
इस कौशल का उपयोग करें जब ट्रेस से मूल्यांकन डेटासेट बनाना हो या LangSmith पर डेटासेट अपलोड करना हो या डेटासेट क्वेरी करना हो। इसमें डेटासेट प्रकार शामिल हैं (final_response,…
langsmith-evaluator
langchain-ai
लैंगस्मिथ के लिए मूल्यांकन पाइपलाइन बनाते समय इस कौशल का आह्वान करें। इसमें तीन मुख्य घटक शामिल हैं: (1) मूल्यांकनकर्ता बनाना - LLM-एक-न्यायाधीश, कस्टम कोड; (2)…