Deepagents Planning Todos

What it does

When your agent uses it

• Implement task planning with writetodos
• Break down complex tasks into subtasks
• Track agent progress through todos
• Debug why todos arent completing

Example prompts

• “/deepagents-planning-todos”

Requirements

• Python 3

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

1. 1Create todos with "status": "pending" for newly planned work.
2. 2Update to "in_progress" when starting work on a todo.
3. 3Mark "completed" when finished (don't delete - keeps context).
4. 4For interactive workflows, ask user approval ("Does this plan look good?") before starting execution.

What it can do on your machine

Read from SKILL.md and the folder at commit a2d4a10. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships 2 files in scripts/ (Python), which the agent can run.

  Shell commands in SKILL.md call:

  • uv

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • docs.langchain.com

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Deepagents Planning Todos loads about 2.3k tokens when it runs, and up to ~5.5k if it reads all its reference files. Until then it costs about 126 tokens; SKILL.md has 688 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~126

When it runs · the whole SKILL.md, loaded when a task matches

~2.3k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~5.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from soba-labs/langchain-agent-skills at commit a2d4a10, republished under its MIT licence (© soba-labs). 688 words, ~2,310 tokens.

name

deepagents-planning-todos

description

Use the write_todos tool effectively for task planning and decomposition in Deep Agents. Use when users want to (1) implement task planning with write_todos, (2) break down complex tasks into subtasks, (3) track agent progress through todos, (4) debug why todos aren't completing, (5) design todo structures for different task types (research, coding, analysis), (6) understand todo status lifecycle and best practices, or (7) visualize todo progression from LangSmith traces.

from deepagents import create_deep_agent

# TodoListMiddleware is included by default in create_deep_agent
agent = create_deep_agent(
    model="anthropic:claude-sonnet-4-5-20250929",
    tools=[search_tool, summarize_tool],
    system_prompt="You are a research assistant. Use write_todos for multi-step tasks."
)

# Agent workflow:
# 1. Call write_todos with initial plan
# 2. Ask user: "Does this plan look good?"
# 3. User approves → start executing
# 4. Update the todo list as work progresses
# 5. Keep todos aligned with the current plan and execution state

# Agent calls write_todos internally:
{
  "name": "write_todos",
  "arguments": {
    "todos": [
      {"content": "Search for papers on LLM agents", "status": "pending"},
      {"content": "Read and extract findings from top 5 papers", "status": "pending"},
      {"content": "Identify common themes", "status": "pending"},
      {"content": "Write summary report", "status": "pending"}
    ]
  }
}

{
  "content": "Task description (clear, actionable)",
  "status": "pending" | "in_progress" | "completed"
}

pending → in_progress → completed

# Step 1: Create initial plan (all pending)
write_todos([
  {"content": "Research topic", "status": "pending"},
  {"content": "Write summary", "status": "pending"}
])

# Step 2: Ask user approval
# User: "Yes, proceed"

# Step 3: Start first task
write_todos([
  {"content": "Research topic", "status": "in_progress"},
  {"content": "Write summary", "status": "pending"}
])

# Step 4: Complete first task, start second
write_todos([
  {"content": "Research topic", "status": "completed"},
  {"content": "Write summary", "status": "in_progress"}
])

# Step 5: Finish all tasks
write_todos([
  {"content": "Research topic", "status": "completed"},
  {"content": "Write summary", "status": "completed"}
])

# Export trace from LangSmith (download JSON)
# Then run:
uv run skills/deepagents-planning-todos/scripts/visualize_todos.py trace.json

# Show Mermaid diagram:
uv run skills/deepagents-planning-todos/scripts/visualize_todos.py trace.json --format mermaid

# Show full timeline:
uv run skills/deepagents-planning-todos/scripts/visualize_todos.py trace.json --show-timeline

Todo Timeline for trace abc123:

Initial Plan (Step 1):
  ⏳ [pending] Search for papers on LLM agents
  ⏳ [pending] Read and extract findings
  ⏳ [pending] Identify common themes
  ⏳ [pending] Write summary report

Final State:
  ✅ [completed] Search for papers on LLM agents
  ✅ [completed] Read and extract findings
  ✅ [completed] Identify common themes
  ✅ [completed] Write summary report

© soba-labs, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 9 other files (scripts, references, assets) in skills/deepagents-planning-todos of soba-labs/langchain-agent-skills.

• SKILL.md
• assets/examples/todo-driven-agent/.env.example
• assets/examples/todo-driven-agent/README.md
• assets/examples/todo-driven-agent/agent.py
• assets/examples/todo-driven-agent/pyproject.toml
• assets/todo-structures/coding-todos.json
• assets/todo-structures/research-todos.json
• references/todo-patterns.md
• scripts/.gitignore
• scripts/visualize_todos.py

Open the folder on GitHubat commit a2d4a10