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Planning

  • 195 installs
  • 27.3k repo stars
  • Updated August 5, 2026
  • langchain-ai/deepagents

Decompose goals into ordered agent tasks with dependencies, tool calls, and checkpoints so multi-step coding or research runs stay coherent.

About

DeepAgents planning skill defines how agents break objectives into staged tasks, select tools, track dependencies, and recover from failures—core orchestration for building dependable autonomous coding and research workflows.

  • Goal decomposition trees
  • Dependency-aware task ordering
  • Tool routing checkpoints
  • Retry and fallback planning
  • Multi-agent handoff maps

Planning by the numbers

  • 195 all-time installs (skills.sh)
  • +8 installs in the week ending Aug 2, 2026 (Skillselion tracking)
  • Ranked #2,920 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/langchain-ai/deepagents --skill planning

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Listed on Skillselion
Installs195
repo stars27.3k
Last updatedAugust 5, 2026
Repositorylangchain-ai/deepagents

What it does

Decompose goals into ordered agent tasks with dependencies, tool calls, and checkpoints so multi-step coding or research runs stay coherent.

Files

SKILL.mdMarkdownGitHub ↗

Planning Skill

Use this skill when starting a new coding task to create a thorough implementation plan.

Steps

1. Understand the Task

  • Read the issue/task description completely
  • Identify the expected outcome and acceptance criteria
  • Note any constraints or requirements mentioned

2. Explore the Codebase

  • Find the repository root and read the project structure
  • Identify the tech stack (language, framework, test runner)
  • Read README, CONTRIBUTING, or similar docs if they exist
  • Find existing tests to understand testing patterns

3. Identify Relevant Files

  • Use grep to find code related to the task
  • Read the most relevant files (entry points, related modules)
  • Identify which files need to be modified vs. created
  • Check for existing patterns you should follow

4. Write the Plan

Use write_todos to create a structured plan:

write_todos([
    "1. <specific change in specific file>",
    "2. <next specific change>",
    "3. Write tests for <feature>",
    "4. Run test suite and fix failures",
    "5. Review all changes"
])

5. Assess Risks

  • Are there breaking changes?
  • Are there edge cases to handle?
  • Does this affect other parts of the codebase?
  • Flag anything uncertain for review

Guidelines

  • Plans should have 3-10 concrete steps
  • Each step should be specific enough to execute without further planning
  • Include test writing and test running as explicit steps
  • End with a review/verification step

Related skills

AI & Agent Buildingagentsautomation

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