planning

by langchain-ai

Break down a coding task into a structured implementation plan with clear steps, file identification, and risk assessment.

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

Download ZIP GitHub

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

More skills from langchain-ai

Access LangGraph documentation to build stateful agents and multi-agent workflows. Fetches official LangGraph Python docs covering state machines, graph-based agent design, and human-in-the-loop patterns Prioritizes relevant documentation by query type: implementation guides for how-to questions, concept pages for theory, tutorials for end-to-end examples, and API references for technical details Automatically selects 2–4 most relevant documentation URLs and retrieves their content to answer...

langgraph-human-in-the-loop

Pause graph execution for human review, approval, or validation, then resume with their input. Requires three components: a checkpointer (InMemorySaver or PostgresSaver), a thread ID in config, and JSON-serializable interrupt payloads interrupt(value) pauses and surfaces data; Command(resume=value) resumes and returns that value to the paused node All code before interrupt() re-executes on resume, so side effects must be idempotent (use upsert, not insert) Supports approval workflows,...

Use this skill for requests related to web research; it provides a structured approach to conducting comprehensive web research

langchain-oss-primer

ALWAYS START HERE for any LangChain, Deep Agents, or LangGraph agent building project. Required starting point before choosing other skills or writing any…

Guide for creating effective skills that extend agent capabilities with specialized knowledge, workflows, or tool integrations. Use this skill when the user…

Drafts platform-specific social media posts with research-backed content and generated companion images. Supports LinkedIn posts (1,300 characters with professional tone) and Twitter/X threads (280 characters per tweet with 1/🧵 format) Requires delegating research to a subagent before writing, then reading findings to ensure accuracy and relevance Generates eye-catching social images automatically using generate_social_image tool with bold, high-contrast compositions optimized for small...

deep-agents-memory

Pluggable memory and file backends for Deep Agents with ephemeral, persistent, and hybrid routing options. Four backend types: StateBackend (thread-scoped, ephemeral), StoreBackend (cross-session persistent), FilesystemBackend (real disk access for local dev), and CompositeBackend (route different paths to different backends) FilesystemMiddleware provides six file operation tools: ls , read_file , write_file , edit_file , glob , grep CompositeBackend uses longest-prefix matching to route...

deep-agents-orchestration

Orchestrate subagents, plan multi-step tasks, and require human approval for sensitive operations. Delegate work to specialized subagents via the task tool; custom subagents support isolated tool sets and system prompts, while the default "general-purpose" subagent inherits main agent configuration Plan and track complex workflows with write_todos , organizing tasks across pending, in-progress, and completed states; requires a thread_id for persistence across invocations Implement...