Deep Agents is an extensible agent harness that provides an out-of-the-box agent for long, multi-step tasks, with features such as planning, sub-agents, filesystem access, context management, memory, and human approval of tool calls. It is used by developers building agents with different language models, and its catalogue entries extend the harness with reusable skills, MCP servers, and instructions.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/langchain-ai/deepagents/planningnpx skills add langchain-ai/deepagents --skill planninggit clone --depth 1 https://github.com/langchain-ai/deepagentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/langchain-ai/deepagents/planning)<a href="https://agentmods.dev/skills/langchain-ai/deepagents/planning"><img src="https://agentmods.dev/badge/skills/langchain-ai/deepagents/planning.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00023 | $0.00360 |
| Opus 5 | $0.00012 | $0.00180 |
| Sonnet 5 | $0.00005 | $0.00072 |
| Haiku 4.5 | $0.00002 | $0.00036 |
Grade A, and why
planning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
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
grepto 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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 54 lines · 23 tokens per session scan A 5733ac8765d8
planning is a skill published in the GitHub repository langchain-ai/deepagents (28,893 stars, last pushed today), licensed MIT. It adds 23 tokens to every session and 360 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
recover-from-failure
How to recover when a tool call fails — diagnose, not blindly retry.
workspace-conventions
Reminders about how Dawn's workspace tools behave and what the path-jail allows.
dawn
Build AI agents and workflows with the Dawn framework — the TypeScript meta-framework for LangGraph. Use when creating, editing, or debugging a Dawn app (routes, tools, state, agents, workflows, testing, deployment).
cite-sources
How to attribute every factual claim to a corpus document.
synthesize-findings
How to merge researcher sub-answers into one cited report.
code-reviewer
当用户要求进行代码审查、Code Review、查找 Bug、安全风险、性能问题或代码质量问题时使用。.