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tldraw offline Skill · nousresearch | DecimalAI

Design & Mediaby nousresearch

tldraw offline Skill

Drive and script tldraw offline canvases with an agent.

Maintained

SkillScore

SkillScore77/100

Test results 77

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

18%77%of test cases passed +59 pts

Without skill4 of 22

With skill17 of 22

14 fixed3 passed either way4 still failing1 broke

Measured on gemini-3.6-flash8/17/2026v218 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskEvery time my tldraw offline document script reloads, it adds duplicate copies of my UI box shape to the canvas. Which helper function should I use in main.js so shapes are only created if they do not already exist?

Without the skillMissed

With the skillPassed

Same model, same task. Answers are capped and redacted.

Use this skill

Your agent runs on

Adds every file the skill has, pinned so you can update it. No account needed.

pip install "decimalai>=0.15"
decimalai skills add nousresearch-tldraw-offline
claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

The same install from Python, for a notebook or a setup script.

Install

pip install "decimalai>=0.15"

Add the skill

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("nousresearch-tldraw-offline")
if result.exit_code:
    raise SystemExit(result.problems)

print("Added. Start Claude Code with the command below.")

Start Claude Code with it

claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

Type these in a Claude Code session. No account or API key. It writes the skill and any files it bundles, and asks first when its safety rating isn't Passed.

Add the DecimalAI plugin, once

/plugin marketplace add decimal-labs/decimalai-claude-plugins
/plugin install decimalai@decimalai

Install this skill

/decimalai:install nousresearch-tldraw-offline

The plugin needs python3 and curl on your machine.

No Python or account. Claude Code picks it up in its next session.

Save it into your project

curl -fsSL --create-dirs -o .claude/skills/nousresearch-tldraw-offline/SKILL.md https://app.decimal.ai/s/nousresearch-tldraw-offline/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/nousresearch-tldraw-offline/SKILL.md

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Claude Code docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add nousresearch-tldraw-offline

Add it to your agent

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

The same install from Python, for a notebook or a setup script. agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("nousresearch-tldraw-offline")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/nousresearch-tldraw-offline/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from claude_agent_sdk import ClaudeAgentOptions, query

skill = Path(".agents/skills/nousresearch-tldraw-offline/SKILL.md").read_text()
options = ClaudeAgentOptions(system_prompt=f"You are a helpful agent.\n\n{skill}", max_turns=6)

async def main():
    async for message in query(prompt="Hello!", options=options):
        print(message)

# In a notebook or async code: await main()
asyncio.run(main())

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Claude Agent SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai-agents]"
decimalai skills add nousresearch-tldraw-offline

Add it to your agent

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

Install

pip install "decimalai[openai-agents]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("nousresearch-tldraw-offline")
if result.exit_code:
    raise SystemExit(result.problems)

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/nousresearch-tldraw-offline/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/nousresearch-tldraw-offline/SKILL.md").read_text()
agent = Agent(name="support", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

OpenAI Agents docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai]"
decimalai skills add nousresearch-tldraw-offline

Add it to your agent

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

Install

pip install "decimalai[openai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("nousresearch-tldraw-offline")
if result.exit_code:
    raise SystemExit(result.problems)

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/nousresearch-tldraw-offline/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/nousresearch-tldraw-offline/SKILL.md").read_text()
reply = client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=[
        {"role": "system", "content": f"You are a helpful agent.\n\n{skill}"},
        {"role": "user", "content": "Hello!"},
    ],
).choices[0].message

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

OpenAI SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[anthropic]"
decimalai skills add nousresearch-tldraw-offline

Add it to your agent

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

Install

pip install "decimalai[anthropic]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("nousresearch-tldraw-offline")
if result.exit_code:
    raise SystemExit(result.problems)

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/nousresearch-tldraw-offline/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/nousresearch-tldraw-offline/SKILL.md").read_text()
reply = client.messages.create(
    model="claude-haiku-5-5",
    max_tokens=4096,
    system=f"You are a helpful agent.\n\n{skill}",
    messages=[{"role": "user", "content": "Hello!"}],
)

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Anthropic docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[google-genai]"
decimalai skills add nousresearch-tldraw-offline

Add it to your agent

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

Install

pip install "decimalai[google-genai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("nousresearch-tldraw-offline")
if result.exit_code:
    raise SystemExit(result.problems)

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/nousresearch-tldraw-offline/SKILL.md

Give it to your agent

from pathlib import Path
from google import genai
from google.genai import types

client = genai.Client()
skill = Path(".agents/skills/nousresearch-tldraw-offline/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Gemini API docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add nousresearch-tldraw-offline

Add it to your agent

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

Install

pip install "decimalai[langchain]" langchain-openai

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("nousresearch-tldraw-offline")
if result.exit_code:
    raise SystemExit(result.problems)

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/nousresearch-tldraw-offline/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/nousresearch-tldraw-offline/SKILL.md").read_text()
agent = create_agent("openai:gpt-5.4-mini", tools=[], system_prompt=f"You are a helpful agent.\n\n{skill}")
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

LangChain docs

CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add nousresearch-tldraw-offline

Add it to your agent

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

The same install from Python, for a notebook or a setup script. CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("nousresearch-tldraw-offline")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

No DecimalAI package. CrewAI's own loader reads the folder, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/nousresearch-tldraw-offline/SKILL.md

Give it to your agent

from pathlib import Path
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                         # your own CrewAI LLM
    skills=[Path(".agents/skills")],
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

CrewAI docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[adk]"
decimalai skills add nousresearch-tldraw-offline

Add it to your agent

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

Install

pip install "decimalai[adk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("nousresearch-tldraw-offline")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/nousresearch-tldraw-offline/SKILL.md

Give it to your agent

from pathlib import Path
from google.adk.agents import LlmAgent

skill = Path(".agents/skills/nousresearch-tldraw-offline/SKILL.md").read_text()
agent = LlmAgent(
    name="support",
    model="gemini-3.5-flash",
    instruction=lambda context: f"You are a helpful agent.\n\n{skill}",
)

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Google ADK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add nousresearch-tldraw-offline

Add it to your agent

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

Install

pip install "decimalai[pydantic-ai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("nousresearch-tldraw-offline")
if result.exit_code:
    raise SystemExit(result.problems)

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/nousresearch-tldraw-offline/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/nousresearch-tldraw-offline/SKILL.md").read_text()
agent = Agent("openai:gpt-4o-mini", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Pydantic AI docs

Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add nousresearch-tldraw-offline

Add it to your agent

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

The same install from Python, for a notebook or a setup script. Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("nousresearch-tldraw-offline")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/nousresearch-tldraw-offline/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/nousresearch-tldraw-offline/SKILL.md").read_text()
agent = create_deep_agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Deep Agents docs

Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add nousresearch-tldraw-offline

Add it to your agent

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

The same install from Python, for a notebook or a setup script. Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("nousresearch-tldraw-offline")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/nousresearch-tldraw-offline/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/nousresearch-tldraw-offline/SKILL.md").read_text()
agent = Agent(client=client, instructions=f"You are a helpful agent.\n\n{skill}")
asyncio.run(agent.run("Hello!"))

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Microsoft Agent Framework docs

Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add nousresearch-tldraw-offline

Add it to your agent

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

The same install from Python, for a notebook or a setup script. Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("nousresearch-tldraw-offline")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/nousresearch-tldraw-offline/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/nousresearch-tldraw-offline/SKILL.md").read_text()
agent = Agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent("Hello!")

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Strands docs

Adds every file the skill has, pinned so you can update it. Cursor, Copilot, Cline and Warp read this folder by themselves.

pip install "decimalai>=0.15"
decimalai skills add nousresearch-tldraw-offline

No Python or account. Cursor, Copilot, Cline and Warp read this folder by themselves.

Save it into your project

curl -fsSL --create-dirs -o .agents/skills/nousresearch-tldraw-offline/SKILL.md https://app.decimal.ai/s/nousresearch-tldraw-offline/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/nousresearch-tldraw-offline/SKILL.md

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Cursor and other editors docs

Fetch the file and give its text to your model as instructions. No account or package needed.

Get the file

curl -fsSL https://app.decimal.ai/s/nousresearch-tldraw-offline/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/nousresearch-tldraw-offline.json

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v2, tested 8/17/2026
Source: nousresearch/hermes-agent ↗
License: MIT

Manage it in DecimalAI

Saved prompts and skills, pinned versions, and traces of this skill in your runs. DecimalAI does not run your agent.

Claude Code reads files from your project, so DecimalAI config does not apply there.

On Claude Agent SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI Agents: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Anthropic: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Gemini API: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On LangChain: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On CrewAI: set DECIMAL_API_KEY and add decimalai.init(crewai=True) to record runs and skill use. The skill version stays in your repo.

On Google ADK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Pydantic AI: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Deep Agents: set DECIMAL_API_KEY and add decimalai.init(langchain=True) to record runs and skill use. The skill version stays in your repo.

On Microsoft Agent Framework: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

On Strands: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

Editors read files from your project, so DecimalAI config does not apply there.

Anywhere else: record a use with decimalai.log_skill_activation() inside a trace.

Use this skill

SKILL.md

tldraw offline Skill

Work with the tldraw offline desktop app (offline.tldraw.com): read the open canvas, make edits, and write document scripts — JavaScript embedded in a .tldraw file that runs on load and gives the file durable behavior. The app runs a local HTTP API (default localhost:7236) that a coding agent drives with plain curl from its terminal — this is exactly how the app's own homepage demo (Codex editing a canvas live) works. The agent does NOT use computer-use / GUI clicking, and does NOT hand-edit the .tldraw file directly. Keep tldraw offline open while you work.

When to Use

The user has tldraw offline open and asks you to build or modify a canvas (diagrams, wireframes, layouts).
You want to add durable behavior to a drawing (reactive shapes, interactive buttons, animation, connection logic) via an embedded document script.

Do NOT hand-place shapes to imitate a drawing — write the code that generates them. Agents are far better at scripting the canvas than at drawing on it.

Prerequisites

tldraw offline installed and running, with a document open. Releases: https://github.com/tldraw/tldraw-offline/releases/latest (macOS DMG, Windows x64/Arm64, Linux x86_64/arm64 AppImage or amd64/arm64 .deb).
Agent skills installed in the app: Develop → Install Agent Skills. The app writes its own tldraw skill into ~/.codex/skills/, ~/.claude/skills/, ~/.cursor/skills/, and ~/.gemini/skills/ — teaching that agent the curl recipes below. (This Hermes skill mirrors that guidance for Hermes.)
The local control API. On launch the app writes server.json to its config dir (Linux ~/.config/tldraw/, macOS ~/Library/Application Support/tldraw/, Windows %APPDATA%\tldraw\) with port (default 7236), a bearer token, pid, and startedAt. Every request except GET / needs Authorization: Bearer <token>. A clean quit removes server.json; if it's present but the port doesn't answer, the app quit uncleanly — treat as not running.
Re-read port + token on EVERY shell call. Each terminal call is a fresh shell, so an exported token does not persist — "export once and reuse" sends an empty token and 401s. Read both inline at the top of each call: PORT=$(jq -r .port <server.json>); TOKEN=$(jq -r .token <server.json>).
No account or network needed for local editing.

How to Run

Two distinct workflows. Pick by whether the change must survive a reload.

A. One-off canvas edits (/exec) — layout, generating shapes, cleanup. This is a live edit, not saved script:

BASE=http://localhost:7236
TOKEN=$(python -c "import json;print(json.load(open('$HOME/.config/tldraw/server.json'))['token'])")
# find the focused document id
DOC=$(curl -s "$BASE/api/search" -X POST -H 'content-type: application/json' \
  -H "Authorization: Bearer $TOKEN" \
  -d '{"code":"return (await api.getFocusedDoc()).id"}' | python -c "import sys,json;print(json.load(sys.stdin)['result'])")
# run code with the live `editor` + `helpers` in scope
curl -s "$BASE/api/doc/$DOC/exec" -X POST -H 'content-type: application/json' \
  -H "Authorization: Bearer $TOKEN" \
  -d '{"code":"const {createShapeId,toRichText}=await import(\"tldraw\"); editor.createShape({id:createShapeId(),type:\"geo\",x:0,y:0,props:{geo:\"rectangle\",w:200,h:100,color:\"blue\",fill:\"solid\",richText:toRichText(\"hello\")}}); return editor.getCurrentPageShapes().length"}'

B. Durable behavior (script/main.js) — reactive/interactive logic that must survive reload. Edit the file on disk; the app's watcher applies it:

# get the live script file path for the doc
curl -s "$BASE/api/doc/$DOC/script-workspace" -X POST \
  -H "Authorization: Bearer $TOKEN"          # -> result.mainJsPath, result.isDefaultScript
# edit result.mainJsPath with read_file / patch / write_file (see scripts/main.js)
# then confirm the watcher applied it:
curl -s "$BASE/api/doc/$DOC/script-status" -H "Authorization: Bearer $TOKEN"

The ready-to-adapt document script is scripts/main.js.

Quick Reference

The document-script contract (verified against the app's bundled script-context.d.ts):

import { createShapeId, toRichText } from 'tldraw'   // primitives: import, not globals

export default function ({ editor, helpers, signal }) {
  editor.run(() => {                                 // batch = one undo step
    helpers.createShapeIfMissing({                   // idempotent furniture
      id: createShapeId('node-1'), type: 'geo', x: 0, y: 0,
      props: { geo: 'rectangle', w: 200, h: 100, richText: toRichText('hi') },
    })
  })

  const stop = editor.store.listen(() => { /* react */ })  // fires the tick AFTER a commit
  signal.addEventListener('abort', () => stop())           // REQUIRED cleanup on rerun/close
}

ctx.editor — the live Editor (createShape, updateShape, deleteShapes, getCurrentPageShapes, getShape, getBindingsFromShape, zoomToFit, on('tick'|'event', fn), run(fn, { history: 'ignore' })).
ctx.helpers — createShapeIfMissing, createShapesIfMissing, createArrowBetweenShapes(from, to, { arrowheadEnd }), translateShapes, onShapeTranslate(id, fn, { signal }), richTextToPlainText, boxShapes, getLints.
ctx.signal — AbortSignal; attach every listener/interval teardown to it.
config.js (separate file) registers custom shape/tool/component utils and runs before mount; main.js runs against the mounted editor and reruns on save.

Interactive UI (clickable buttons that drive state)

Drawn shapes can behave like a real app — the thing a static whiteboard can't do. Full example: scripts/counter.js (a number display + MINUS/RESET/PLUS buttons).

Verification boundary — read this before claiming interaction works or doesn't. The app's OWN agent playbook says to verify a clickable-UI script with "one simulated click and one state read" via /exec (editor.dispatch a pointer event, await a tick, read the shape's state) — NOT by driving a real mouse. By that standard the counter is verified: dispatched clicks stepped it 0 → 1 → 2 → 1 → 0. Two caveats worth writing down:

The script only runs once the app's file-watcher applies it. On Linux that watcher uses inotify; a host with an exhausted fs.inotify.max_user_instances logs inotify_add_watch ... No space left on device, script-status shows state: "not-watching" / hasEntry: false, and the script never executes. This is a host limit, not a script bug — a normal desktop is unaffected.
Driving the canvas with computer-use needs FOREGROUND delivery, not background. cua-driver's default background delivery returns background_unavailable against Electron's occluded renderer — but that is the first rung, not a wall. cua-driver returns an escalation: "foreground" hint; set delivery_mode: "foreground" (pair with bring_to_front) and its X11 XTest path (x11_xtest_fg) clicks land — this dismisses the "Run Script" consent dialog and clicks the canvas. Verified by the Cua team on this exact v1.11.0 AppImage (Linux/X11). Do NOT conclude "Electron rejects synthetic clicks" and give up in background mode — climb to foreground. (The real product path is still /exec, not clicks; this note is for computer-use-driven testing.)

The pattern:

export default function ({ editor, helpers, signal }) {
  // 1. Build buttons idempotently; tag each with meta so the handler finds them.
  //    Give buttons a visible label AND a meta.action.
  // 2. Hit-test pointer_down in PAGE coordinates against the button bounds:
  const inside = (b, p) => p.x >= b.x && p.x <= b.x + b.w && p.y >= b.y && p.y <= b.y + b.h
  function onEvent(info) {
    if (!info || info.name !== 'pointer_down') return
    let p = null
    try { if (info.point && editor.screenToPage) p = editor.screenToPage(info.point) } catch {}
    p = p ?? editor.inputs?.currentPagePoint
    if (!p) return
    const hit = editor.getCurrentPageShapes().find(
      (s) => s.meta?.ui === 'button' &&
        inside({ x: s.x, y: s.y, w: s.props.w, h: s.props.h }, p)
    )
    if (hit) runAction(hit.meta.action)   // mutate state; store it in a shape's meta
  }
  editor.on('event', onEvent)
  signal.addEventListener('abort', () => editor.off('event', onEvent))  // REQUIRED
}

Find buttons by meta (or visible label via helpers.richTextToPlainText), not by hard-coded coordinates.
One script owns both build and read. If the shapes are created by one code path (with meta.action: 'inc') and the handler reads another convention (meta.action === 'PLUS'), clicks silently do nothing. Ship the buttons built by the same script that handles them, or ship an empty canvas so the script builds them fresh — never pre-bake mismatched shapes into the file's db.
Keep app state in a shape's meta (e.g. meta.count) and render it as that shape's richText label, so it survives save and is readable for verification.
Detach the listener on signal abort. Skipping this is not cosmetic: on the next save the old onEvent stays attached alongside the new one, so every click fires twice and a counter jumps by 2 instead of 1.
For continuous motion use editor.on('tick', fn); for a moving anchor with attached pieces use helpers.onShapeTranslate(id, fn, { signal }).

Shipping a self-running scripted `.tldraw`

A .tldraw is a zip of metadata.json + session.json + db.sqlite + assets/

script/ (only those entries are packable). For the script to auto-run without the "This document contains a script → Run Script" consent dialog:

metadata.json must carry a script manifest: { "sha256": "<digest>" }, where the digest is sha256 over each sorted script/ path as `${path}\0${sha256hex(bytes)}\n`. A mismatch is rejected as tampered.
Pre-trust the digest by adding it to ~/.tldraw/script-trust.json ({ "trusted": ["<digest>"] }, or $TLDRAW_SCRIPT_TRUST). The app skips consent when isScriptTrusted(digest) is true.

Procedure

Read the current token/port from server.json. Find the target doc with api.getFocusedDoc() (or api.getDocs()); name it explicitly if several are open.
For layout/generation, use /exec. For durable behavior, edit script/main.js via /script-workspace.
Make scripts idempotent: create durable shapes with helpers.createShapeIfMissing and stable createShapeId('name') ids. Scripts rerun on every load.
Keep script-owned writes out of the user's undo stack: editor.run(fn, { history: 'ignore' }) (or helpers.translateShapes, which already does).
For reactivity, editor.store.listen(cb) and tear it down on signal abort. For interaction, editor.on('event', h) (hit-test pointer_down in page coords); for animation, editor.on('tick', h).
For a single moving anchor + attached internals, prefer helpers.onShapeTranslate(anchorId, fn, { signal }) over a broad store listener — a broad listener can turn your own writes into feedback loops.

Shape props (validated against tldraw SDK v5 schema)

editor.createShape / createShapeIfMissing accept partial props (shape utils fill defaults). When building raw records for a file snapshot, every prop below is required (run scripts/validate_shapes.mjs):

Shape	Required props
`note`	`richText`, `color`, `labelColor`, `size`, `font`, `align`, `verticalAlign`, `growY`, `fontSizeAdjustment`, `url`, `scale`, `textLastEditedBy`
`text`	`richText`, `color`, `size`, `font`, `textAlign`, `w`, `scale`, `autoSize`
`frame`	`w`, `h`, `name`, `color`
`geo`	`geo`, `w`, `h`, `color`, `fill`, `richText` (+ dash/size/etc. defaulted)

richText must be toRichText('...') — a bare string is rejected. color enum: black grey light-violet violet blue light-blue yellow orange green light-green light-red red white. font enum: draw sans serif mono.

Pitfalls

store.listen fires on the tick AFTER a commit, not synchronously. If you write a shape and immediately read state expecting the listener to have run, it hasn't. Verified live: an in-turn read shows 0 fires; after one setTimeout tick it shows 1. Same reason the app notes editor.dispatch is async — await a tick before verifying.
ctx, not globals. The entry is export default function ({ editor, helpers, signal }). There is no bare editor global in a document script. createShapeId / toRichText / Vec come from import ... from 'tldraw'.
richText, not text. Text/note/geo labels use richText: toRichText(s).
Raw records need every prop; createShape does not. In-app pass only the props you care about; a hand-built .tldraw snapshot needs the full set (table).
Scripts rerun on every load — be idempotent. Use createShapeIfMissing with stable ids or you duplicate content and clobber user edits.
Clean up on signal. signal.addEventListener('abort', () => stop()) for every store.listen / editor.on / setInterval; the signal fires before rerun and on close.
Keep script writes out of undo: editor.run(fn, { history: 'ignore' }).
editor.on('tick') pauses when the window is hidden (it is a RAF loop); setInterval keeps firing but Electron throttles it to ~1/s in the background.
The API needs the bearer token from server.json; the port can be non-default (server.listen(0) picks one) — always read the file, don't hardcode 7236.
Only tldraw / react / react-dom import — not a Node project.

Verification

Shape schema (offline, no app): node scripts/validate_shapes.mjs — builds the real tldraw schema and validates note/text/frame. Passing prints 3/3.
Live canvas edits: after /exec, read back with /api/search → api.getShapes(docId) (returns { page, viewport, shapes }) and api.getBindings(docId) (array). Confirm expected shapes/bindings exist. Grab api.getScreenshot(docId) (returns { filePath, ... }) and inspect the PNG/JPEG with vision_analyze.
Durable script applied: GET /api/doc/:id/script-status. Success is state: "applied" (currentDiskDigest === lastAppliedDigest === manifestSha256, pendingApply === false, lastApplyError === null). If it stays "pending" after a short retry, report that instead of claiming success; "error" means the apply failed — read errorLogPath.

About this skill

Automatic

Coding pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v5 — 8/17/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

nousresearch / hermes-agent

optional-skills/creative/tldraw-offline/SKILL.md

View on GitHub ↗

License MITImported 8/17/2026Version v2Bundled 3 files · scripts

Illustrative Want to see what nousresearch/tldraw-offline would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

Other skills for the same job

Published skills whose descriptions closely match this one, with their headline benchmark lift.

elianiva/tldraw-offlineOperate the user's tldraw offline canvas app, including open .tldraw or .tldr files. Use whenever a task involves inspecting, editing, arranging, connecting, linting, or scripting a tldraw Desktop canvas.Close match+32 pts pass-rate lift

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SkillScore

Safety

What it changes

Track record

Use this skill

tldraw offline Skill

When to Use

Prerequisites

How to Run

Quick Reference

Interactive UI (clickable buttons that drive state)

Shipping a self-running scripted .tldraw

Procedure

Shape props (validated against tldraw SDK v5 schema)

Pitfalls

Verification

SkillSafety

Source

Other skills for the same job

Related skills

More in the registry

Shipping a self-running scripted `.tldraw`